Papers:

My Ph.D. thesis: On Elliptic Curves, the ABC Conjecture, and Polynomial Threshold Functions.

If you are a potential student and would like to get a better idea of what I do, here is a curated list of Introductory Papers, which might be more approachable than simply reading random papers off this list.

Note: Author order for all papers is either alphabetical or randomized.

Books:

• Ilias Diakonikolas and Daniel M. Kane Algorithmic High-Dimensional Robust Statistics, Cambridge University Press, 2023. [Hard copy can be purchased here.]
  

Publications:

• Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation, in preparation.
  
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostically Learning Multi-index Models with Queries, in preparation.
  
• Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan Do PAC-Learners Learn the Marginal Distribution?, in preparation.
• Ilias Diakonikolas, Daniel M. Kane Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials, in preparation.
• Ery Arias-Castro, Clement Berenfeld, Daniel Kane Theoretical Foundations of Ordinal Multidimensional Scaling, Including Internal and External Unfolding, in preparation.
• Ilias Diakonikolas, Daniel Kane, Mingchen Ma Active Learning of General Halfspaces: Label Queries vs Membership Queries, Advances in Neural Information Processing Systems (NeurIPS) 2024.
• Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye Replicability in High Dimensional Statistics, Foundations Of Computer Science (FOCS) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostically Learning Multi-index Models with Queries, Foundations Of Computer Science (FOCS) 2024.
  
• Ilias Diakonikolas, Daniel M. Kane Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials, Conference on Learning Theory (COLT) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis Statistical Query Lower Bounds for Learning Truncated Gaussians, Conference on Learning Theory (COLT) 2024.
• Ilias Diakonikolas, Daniel M Kane, Sihan Liu, Nikos Zarifis Testable Learning of General Halfspaces with Adversarial Label Noise, Conference on Learning Theory (COLT) 2024.
• Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions, Conference on Learning Theory (COLT) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination, International Conference on Machine Learning (ICML) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs, Symposium on Theory of Computation (STOC) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Sihan Liu Testing Closeness of Multivariate Distributions via Ramsey Theory, Symposium on Theory of Computation (STOC) 2024.
• Daniel M. Kane, Anthony Ostuni, Kewen Wu Locality Bounds for Sampling Hamming Slices, Symposium on Theory of Computation (STOC) 2024.
• Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu Online Robust Mean Estimation, Symposium on Discrete Algorithms (SODA) 2024.
• Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun SQ Lower Bounds for Learning Mixtures of Linear Classifiers, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis Efficient Testable Learning of Halfspaces with Adversarial Label Noise, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm, Advances in Neural Information Processing Systems (NeurIPS) 2023 (spotlight presentation).
• Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis  SQ Lower Bounds for Learning Bounded Covariance GMMs, Conference on Learning Theory (COLT) 2023.
• Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise, Conference on Learning Theory (COLT) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru R. Zhang Statistical and Computational Limits for Tensor-on-Tensor Association Detection, Conference on Learning Theory (COLT) 2023.
• Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz Exponential Hardness of Reinforcement Learning with Linear Function Approximation, Conference on Learning Theory (COLT) 2023.
• Daniel M. Kane, Ilias Diakonikolas A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points, Conference on Learning Theory (COLT) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals, International Conference on Machine Learning (ICML) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA, International Conference on Machine Learning (ICML) 2023.
• Ilias Diakonikolas, Christos Tzamos, Daniel Kane A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning, Symposium on Theory of Computation (STOC) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia Gaussian Mean Testing Made Simple, Symposium on Simplicity in Algorithms (SOSA 2023).
  
• Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett Sampling Equilibria: Fast No-Regret Learning in Structured Games, Symposium on Discrete Algorithms (SODA) 2023.
  
• Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun SQ Lower Bounds for Learning Single Neurons with Massart Noise, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  
• Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu Near-Optimal Bounds for Testing Histogram Distributions, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  
• Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren Cryptographic Hardness of Learning Halfspaces with Massart Noise, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  
• Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi Outlier-Robust Sparse Estimation via Non-Convex Optimization, Advances in Neural Information Processing Systems (NeurIPS) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering, Advances in Neural Information Processing Systems (NeurIPS) 2022 (oral presentation).
• Daniel M. Kane Asymptotic Results for the Queen Packing Problem, submitted to Journal of Combinatorics.
• Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan with appendix by Daniel Kane Fooling Gaussian PTFs via Local Hyperconcentration, Journal of the ACM, to appear.
• Ilias Diakonikolas, Daniel M. Kane, Jasper C.H. Lee, Ankit Pensia Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions, Advances in Neural Information Processing Systems (NeurIPS) 2022.
• Jeffery S. Cohen, Daniel M. Kane Bounds on the Independence Required for Cuckoo Hashing, manuscript.
• Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas Robust Sparse Mean Estimation via Sum of Squares, Conference on Learning Theory (COLT) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun, Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models, Conference on Learning Theory (COLT) 2022.
• Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan Realizable Learning is All You Need, Conference on Learning Theory (COLT) 2022, Journal version TheoretiCS, Vol 3 (2024).
• Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan Computational-Statistical Gaps in Reinforcement Learning, Conference on Learning Theory (COLT) 2022.
• Ilias Diakonikolas, Daniel M. Kane Non-Gaussian Component Analysis via Lattice Basis Reduction, Conference on Learning Theory (COLT) 2022.
• Ilias Diakonikolas, Daniel M. Kane Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise, Conference on Learning Theory (COLT) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Streaming Algorithms for High-Dimensional Robust Statistics, International Conference on Machine Learning (ICML) 2022.
• Daniel M. Kane, Shahed Sharif, Alice Silverberg Quantum Money from Quaternion Algebras, MathCrypt 2022.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Learning General Halfspaces with General Massart Noise under the Gaussian Distribution, Symposium on Theory of Computation (STOC) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian, Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation, Symposium on Theory of Computation (STOC) 2022.
• Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala, Robustly Learning Mixtures of k Arbitrary Gaussians, Symposium on Theory of Computation (STOC) 2022.
• Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren Hardness of Learning a Single Neuron with Adversarial Label Noise International Conference on Artificial Intelligence and Statistics (AISTATS) 2022 (oral presentation).
  
• Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman Coresets for Data Discretization and Sine Wave Fitting, Conference on Artificial Intelligence and Statistics (AISTATS) 2022.
  
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart Statistical Query Lower Bounds for List-Decodable Linear Regression, Conference on Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
• Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos Forster Decomposition and Learning Halfspaces with Noise, Conference on Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
• Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian, List-Decodable Mean Estimation in Nearly-PCA Time, Advances in Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
• Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz Bounded Memory Active Learning through Enriched Queries Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostic Proper Learning of Halfspaces under Gaussian Marginals Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos Boosting in the Presence of Massart Noise Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Daniel M. Kane, The Sample Complexity of Robust Covariance Testing, Conference on Learning Theory (COLT) 2021.
• Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price, Optimal Testing of Discrete Distributions with High Probability, Symposium on Theory of Computation (STOC) 2021.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis, A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise, Symposium on Theory of Computation (STOC) 2021.
• Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran The entropy of lies: playing twenty questions with a liar, Innovations in Theoretical Computer Science (ITCS) 2021.
• Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, Alistair Stewart Robustness Meets Algorithms, Communications of the ACM Vol 64(5) p. 107-115, 2021.
• Daniel M. Kane, Scott Duke Kominers Prisoners, Rooms, and Lightswitches, Electronic Journal of Combinatorics, Vol 28 (1), 2021 pp. 1-27.
• Daniel M. Kane Robust Learning of Mixtures of Gaussians,  Symposium On Discrete Algorithms (SODA) 2021.
• Daniel Kane, Andreas Fackler, Adam Gągol, Damian Straszak, Highway: Efficient Consensus with Flexible Finality, in preparation.
• Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar Robustly Learning any Clusterable Mixture of Gaussians, Foundations Of Computer Science (FOCS) 2020.
• Ilias Diakonikolas, Daniel M. Kane Small Covers for Near-zero Sets of Polynomials and Learning Latent Variable Models, Foundations Of Computer Science (FOCS) 2020.
• Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan, Point Location and Active Learning: Learning Halfspaces Almost Optimally, Foundations Of Computer Science (FOCS) 2020.
• Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia Outlier Robust Mean Estimation with Subgaussian Rates via Stability, Advances in Neural Information Processing Systems (NeurIPS) 2020.
• Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise, Advances in Neural Information Processing Systems (NeurIPS) 2020.
• Ilias Diakonikolas, Daniel M. Kane, Nikos Zarifis Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals, Advances in Neural Information Processing Systems (NeurIPS) 2020.
• Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard List-Decodable Mean Estimation via Iterative Multi-Fitering, Advances in Neural Information Processing Systems (NeurIPS) 2020.
• Max Hopkins, Daniel Kane, Shachar Lovett, The Power of Comparisons for Actively Learning Linear Classifiers, Advances in Neural Information Processing Systems (NeurIPS) 2020.
• Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar Vector Quantized Stochastic Gradient Descent, 54th Asilomar Conference on Signals, Systems and Computers (Asilomar) 2020, journal version in IEEE Transactions on Information Theory, 2022.
  
• Ilias Diakonikolas, Daniel Kane, Vasileios Kontonis, Nikos Zarifis Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks Conference On Learning Theory (COLT) 2020.
  
• Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan Noise-tolerant, Reliable Active Classification with Comparison Queries, Conference On Learning Theory (COLT) 2020.
  
• Ilias Diakonikolas, Daniel M. Kane Recent Advances in Algorithmic High-Dimensional Robust Statistics, shortened version in Tim Roughgarden's Beyond Worst Case Analysis book.
• Daniel M. Kane, Carlo Sanna, Jeffrey Shallit Waring's Theorem for Binary Powers, Combinatorica Vol 39 (2019) pp. 1335-1350.
• M. Aliakbarpour, I. Diakonikolas, D. Kane, R. Rubinfeld Private Testing of Distributions via Sample Permutations, Advances in Neural Information Processing Systems (NeurIPS) 2019.
  
• Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin, Advances in Neural Information Processing Systems (NeurIPS) 2019 (Spotlight Presentation).
  
• Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price, Alistair Stewart Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering, Advances in Neural Information Processing Systems (NeurIPS) 2019.
• Daniel M Kane, Roi Livni, Shay Moran, Amir Yehudayoff On Communication Complexity of Classification Problems, Conference on Learning Theory (COLT) 2019.
• Surbhi Goel, Daniel M. Kane, Adam R. Klivans Learning Ising Models with Independent Failures, Conference on Learning Theory (COLT) 2019.
• Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao, Communication and Memory Efficient Testing of Discrete Distributions, Conference on Learning Theory (COLT) 2019.
• Olivier Bousquet, Daniel Kane, Shay Moran The Optimal Approximation Factor in Density Estimation, Conference on Learning Theory (COLT) 2019.
• Ilias Diakonikolas, Daniel M. Kane, John Peebles Testing Identity of Multidimensional Histograms, Conference on Learning Theory (COLT) 2019.
• Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart Sever: A Robust Meta-Algorithm for Stochastic Optimization, International Conference on Machine Learning (ICML) 2019.
• Ilias Diakonikolas, Daniel M. Kane, Degree-d Chow Parameters Robustly Determine Degree-d PTFs (and Algorithmic Applications) Symposium on Theory Of Computation (STOC) 2019.
• Daniel M Kane, Ryan Williams The Orthogonal Vectors Conjecture for Branching Programs and Formulas, Innovations in Theoretical Computer Science (ITCS) 2019.
• Daniel M. Kane, Robert C. Rhoades A Proof of Andrews' Conjecture on Partitions with no Short Sequences, Forum of Mathematics Sigma, Vol 7, 2019.
• Ben Green, Daniel M. Kane, An Example Concerning Set Addition in F_2^n, Trudy Matematicheskogo Instituta im. V.A. Steklova Vol 303 (2018) pp. 116-119.
• Ilias Diakonikolas, Daniel M Kane, Alistair Stewart Sharp Bounds for Generalized Uniformity Testing, Advances in Neural Information Processing Systems (NeurIPS) 2018, Spotlight Presentation at NeurIPS 2018.
• Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Robust Learning of Fixed-Structure Bayesian Networks, Neural Information Processing Systems (NIPS) 2018.
• Daniel M Kane, Shachar Lovett, Shay Moran Generalized Comparison Trees for Point-Location Problems, International Colloquium on Automata, Languages and Programming (ICALP) 2018.
  
• Ilias Diakonikolas, Daniel M Kane, Alistair Stewart Learning Geometric Concepts with Nasty Noise, Symposium on Theory Of Computation (STOC) 2018.
• Clement Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Testing Conditional Independence of Discrete Distributions, Symposium on Theory Of Computation (STOC) 2018.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians, Symposium on Theory Of Computation (STOC) 2018.
  
• Daniel M Kane, Shachar Lovett, Shay Moran Near-Optimal Linear Decision Trees for k-SUM and Related Problems, Symposium on Theory Of Computation (STOC) 2018, invited to STOC special issue, Journal of the ACM, Vol 66, no 3 (2019).
• Daniel M Kane, Sankeerth Rao A PRG for Boolean PTF of Degree 2 with Seed Length Subpolynomial in ε and Logarithmic in n, Conference on Computational Complexity (CCC) 2018.
• Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Robustly Learning a Gaussian: Getting Optimal Error Efficiently, Symposium On Discrete Algorithms (SODA) 2018.
• Daniel M. Kane, Joseph Palmer, Alvaro Pelayo Minimal Models of Compact Symplectic Semiotic Manifolds, Journal of Geometry and Physics, Vol 125 (2018) pp. 48-74.
• Daniel M. Kane, Joseph Palmer, Alvaro Pelayo Classifying Toric and Semitoric Fans by Lifting Equations from SL₂(Z), SIGMA Vol 14 (2018).
• Daniel M. Kane, Zev Klagsbrun, On the Joint Distribution Of Sel_φ(E/Q) and Sel_φ^(E/Q) in Quadratic Twist Families, manuscript.
• Daniel Kane, Sushrut Karmalkar, Eric Price Robust Polynomial Regression up to the Information Theoretic Limit, Foundations of Computer Science (FOCS) 2017.
• Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang, Active Classification with Comparison Queries, Foundations Of Computer Science (FOCS) 2017.
• Daniel M Kane, Shachar Lovett, Sankeerth Rao, The Independence Number of the Birkhoff Polytope Graph and Applications to Maximally Recoverable Codes, Foundations Of Computer Science (FOCS) 2017, journal version SIAM Journal of computing (SICOMP) Vol 48, no 4 (2019) pp 1425-1435.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Statistical Query Lower Bounds for Robust Estimation of High Dimensional Gaussians and Gaussian Mixtures, Foundations Of Computer Science (FOCS) 2017.
• Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin Near-Optimal Closeness Testing of Discrete Histogram Distributions, International Colloquium on Automata, Languages and Programming (ICALP) 2017.
• Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Being Robust in High Dimensions Can Be Practical, International Conference on Machine Learning (ICML) 2017.
• Clement Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Testing Bayesian Networks, Conference on Learning Theory (COLT) 2017; Journal version in IEEE Transactions on Information Theory pp. 1-39, 2020.
  
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Learning Multivariate Log-concave Distributions, Conference On Learning Theory (COLT) 2017.
• Valentine Kabanets, Daniel M. Kane, Zhenjian Lu A Polynomial Restriction Lemma with Applications, Symposium on Theory Of Computation (STOC) 2017.
• Daniel M. Kane, Terence Tao A Bound on Partitioning Clusters, Electronic Journal of Combinatorics Vol 24 no 2 (2017) #P2.31.
• Daniel M. Kane On the Crossing Number of Complete Graphs with an Uncrossed Hamiltonian Cycle, manuscript.
• Daniel M. Kane, Jack A. Thorne, On the φ-Selmer Groups of Elliptic Curves y² = x³ – Dx, Mathematical Proceedings of the Cambridge Philosophical Society, Vol 163 no 1 (2017) pp. 1–23.
  
• Chung-Kuan Cheng, Daniel M. Kane, Ilgweon Kang, Fang Qiao, 3D Floorplan Representations: Corner Links and Partial Ordering, IEEE International 3D Systems Integration Conference (3DIC) 2016, Journal Version: Kang, I., Qiao, F., Park, D., Kane, D., Young, E.F.Y., Cheng, C.K., Graham, R. Three-dimensional Floorplan Representations by Using Corner Links and Partial Order ACM Transactions on Design Automation of Electronic Systems (TODAES), 24(1), p.13, 2018.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Efficient Robust Proper Learning of Log-concave Distributions, manuscript.
• Xue Chen, Daniel M. Kane, Eric Price, Zhao Song, Fourier-sparse interpolation without a frequency gap, Foundations Of Computer Science, (FOCS) 2016.
• Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart, Robust Estimators in High Dimensions without the Computational Intractability, Foundations Of Computer Science, (FOCS) 2016, journal version in SIAM Journal of computing (SICOMP) Vol 48, no 2 (2019), pp. 742-864.
• Ilias Diakonikolas, Daniel M. Kane, A New Approach for Testing Properties of Discrete Distributions, Foundations Of Computer Science, (FOCS) 2016.
• Mihir Bellare, Daniel M. Kane, Phillip Rogaway Big-Key Symmetric Encryption: Resisting Key Exfiltration, International Cryptography Conference (CRYPTO) 2016.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables, Conference On Learning Theory, (COLT) 2016.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Properly Learning Poisson Binomial Distributions in Almost Polynomial Time, Conference On Learning Theory, (COLT) 2016.
• Daniel M. Kane, Ryan Williams, Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits, Symposium on the Theory of Computation (STOC) 2016.
• Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications, Symposium on the Theory of Computation (STOC) 2016.
• Mihir Bellare, Joseph Jaeger, Daniel Kane, Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks on Symmetric Encryption, Conference on Computer and Communications Security (CCS) 2016.
• Daniel M. Kane On the Number of ABC Solutions with Restricted Radical Sizes, Journal of Number Theory, (2015), pp. 32-43.
• Daniel M. Kane Small Designs for Path Connected Spaces and Path Connected Homogeneous Spaces, Transactions of the AMS, Vol. 367 (2015), pp. 6387-6414.
• Daniel M. Kane Canonical Projective Embeddings of the Deligne-Lusztig Curves Associated to ²A₂, ²B₂ and ²G₂, International Mathematics Research Notices, (2015) pp. 1158-1189.
• Manjul Bhargava, Daniel M. Kane, Hendrik W. Lenstra Jr., Bjorn Poonen, Eric Rains Modeling the Distribution of Ranks, Selmer Groups, and Shafarevich-Tate Groups of Elliptic Curves, Cambridge Journal of Mathematics, Vol. 3 (2015), pp. 275-321.
• Bobbie Chern, Persi Diaconis, Daniel M. Kane, Robert C. Rhoades Central Limit Theorems for Some Set Partition Statistics, Advances in Applied Mathematics, Vol. 70 (2015), pp. 92–105.
• Andrew Granville, Daniel M Kane, Dimitris Koukoulopoulos, Robert J Lemke Oliver Best Possible Densities of Dickson m-Tuples, as a Consequence of Zhang–Maynard–Tao in Analytic Number Theory Springer, 2015.
  
• Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin, Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions, Foundations of Computer Science, (FOCS) 2015.
• Parikshit Gopalan, Daniel M. Kane, Raghu Meka, Pseudorandomness via the Discrete Fourier Transform, Foundations of Computer Science (FOCS) 2015, SIAM Journal of computing (SICOMP) Vol 47, no 6 pp. 2451-2487.
• Daniel M. Kane A Polylogarthmic PRG for Degree 2 Threshold Functions in the Gaussian Setting, Conference on Computational Complexity (CCC) 2015.
• Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin, Testing Identity of Structured Distributions, Symposium On Discrete Algorithms (SODA) 2015.
• Daniel M. Kane, Osamu Watanabe A Short Implicant of CNFs with a Relatively Many Satisfying Assignments, International Symposium on Algorithms And Computation (ISAAC) 2014; journal version Algorithmica, (2016), DOI: 10.1007/s00453-016-0125-z.
• Daniel M. Kane The Average Sensitivity of an Intersection of Half Spaces, Symposium on the Theory Of Computing 2014, journal version (open access) in Research In the Mathematical Sciences Vol. 1 no 1 (2014).
• Daniel M. Kane A Pseudorandom Generator for Polynomial Threshold Functions of Gaussians with Subpolynomial Seed Length, Conference on Computational Complexity 2014.
• Bobbie Chern, Persi Diaconis, Daniel M. Kane, Robert C. Rhoades Closed Expressions for Averages of Set Partition Statistics, Research in the Mathematical Sciences, Vol. 1 (2014) no. 2.
• Daniel M. Kane, Scott Duke Kominers Asymptotic Improvements of Lower Bounds for the Least Common Multiples of Arithmetic Progressions, Canadian Mathematical Bulletin, Vol. 57 (2014), pp. 551-561.
• Daniel M. Kane, Adam Klivans, Raghu Meka Learning Half Spaces Under Log-Concave Densities: Polynomial Approximation and Moment Matching, Conference on Learning Theory (COLT) 2013.
• Daniel M. Kane The Correct Exponent for the Gotsman-Linial Conjecture, Conference on Computational Complexity (CCC) 2013 (won best paper award).
• Daniel M. Kane, Raghu Meka A PRG for Lipschitz Functions of Polynomials with Applications to Sparsest Cut, Symposium on the Theory of Computation (STOC) 2013.
• Daniel M. Kane A Low-Depth Monotone Function Given by a Low-Depth Decision Tree that is not an Approximate Junta, Theory of Computing Vol. 9 (2013) pp. 587-592.
• Noam D. Elkies, Daniel M. Kane, Scott Duke Kominers, Minimal S-Criteria May Vary in Size, Journal de Theorie des Nombres de Bordeaux, Vol. 23 no. 3 (2013) pp. 557-563.
• Daniel M. Kane On the Ranks of the 2-Selmer Groups of Twists of a Given Elliptic Curve, Algebra & Number Theory, 7 issue 5 (2013), pp. 1253-1297.
• Daniel M. Kane An Asymptotic for the Number of Solutions to Linear Equations in Prime Numbers from Specified Chebotarev Classes, International Journal of Number Theory, Vol. 9 no. 4 (2013) pp. 1073-1111.
• Daniel M. Kane A Structure Theorem for Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions, Foundations of Computer Science (FOCS) 2012, pp. 91-100; journal version Annals of Probability, Vol. 45 no. 3 (2017) pp. 1612-1679.
• Daniel M. Kane, Kurt Mehlhorn, Thomas Sauerwald, He Sun Counting Arbitrary Subgraphs in Data Streams, International Colloquium on Automata, Languages and Programming (ICALP) 2012, pp. 598-609.
• Eric Blais, Daniel Kane Tight Bounds for Testing k-Linearity, International Workshop on Randomization and Computation (RANDOM) 2012.
• Daniel M. Kane, Jelani Nelson, Sparser Johnson-Lindenstrauss Transforms, Symposium on Discrete Algorithms (SODA) 2012, Journal of the ACM, Vol. 61 no. 1, Article 4, 2014.
• Daniel M. Kane, A Small PRG for Polynomial Threshold Functions of Gaussians, Foundations of Computer Science (FOCS) 2011.
• Daniel M. Kane, Raghu Meka, Jelani Nelson, Almost Optimal Explicit Johnson-Lindenstrauss Transforms, International Workshop on Randomization and Computation (RANDOM), 2011.
• Daniel Kane, Jelani Nelson, A Derandomized Sparse Johnson-Lindenstrauss Transform, superseded by Sparser Johnson-Lindenstrauss Transforms (above).
• Daniel M. Kane k-Independent Gaussians Fool Polynomial Threshold Functions, Conference on Computational Complexity (CCC), 2011.
• Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff, Fast Moment Estimation in Data Streams in Optimal Space, Symposium on the Theory of Computing (STOC) 2011.
• Daniel M. Kane, Samuel A. Kutin, Quantum Interpolation of Polynomials, presented at Combinatorics, Groups, Algorithms, and Complexity (conference in honor of Laci Babai’s 60^th birthday (CGAC) 2010, journal version in Quantum Information and Computation Vol. 11 no. 1&2 (2011).
• Daniel M. Kane Unary Subset-Sum is in Logspace, unpublished.
• Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson, Bounded Independence Fools Degree-2 Threshold Functions, Foundations of Computer Science (FOCS) 2010.
• Daniel M. Kane The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions, in Conference on Computational Complexity (CCC) 2010, pp. 205-210 (Won CCC 2010 best student paper).
• Daniel M. Kane, Jelani Nelson, David P. Woodruff An Optimal Algorithm for the Distinct Elements Problem, Symposium on Principles of Database Systems (PODS) 2010 (Won PODS 2010 best paper, and 2010 IBM research Pat Goldberg Memorial Best Paper Award in Computer Science, Electrical Engineering and Math). Invited to Journal of the ACM.
• Daniel M. Kane, Jelani Nelson, David P. Woodruff On the Exact Space Complexity of Sketching and Streaming Small Norms, Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) 2010.
• Chris Dodd, Phakawa Jeasakul, Anne Jirapattanakul, Daniel M. Kane, Becky Robinson, Noah Stein, Cesar E. Silva Ergodic Properties of a Class of Discrete Abelian Group Extensions of Rank-One Transformations, Colloquium Mathematicum, 119 (2010), pp. 1-22.
• Daniel M. Kane On Solving Games Constructed Using Both Shortened and Continued Conjunctive Sums, Integers: Electronic Journal of Combinatorial Number Theory, 10 (2010), pp. 849-878.
• Daniel M. Kane A Partition of the Positive Reals into Algebraically Closed Subsets, unpublished.
• Erik D. Demaine, Dion Harmon, John Iacono, Daniel M. Kane, Mihai Pǎtraşcu, The Geometry of Binary Search Trees, in Symposium on Discrete Algorithms (SODA) 2009.
• Daniel M. Kane, Gregory N. Price, Erik D. Demaine, A Pseudopolynomial Algorithm for Alexandrov's Theorem, Algorithms and Data Structures Symposium (WADS) 2009. Also in Lecture Notes in Computer Science, 5664 (2009) pp. 435–446.
• Bakir Farhi, Daniel Kane New Results on the Least Common Multiple of Consecutive Integers, Proceedings of the AMS, 137 (2009), no. 6, pp. 1933-1939.
• Daniel Kane, Steven Sivek On the S_n-Modules Generated by Partitions of a Given Shape, The Electronic Journal of Combinatorics, 15 (2008), 12 pages.
• Daniel M. Kane On Lower Bounds on the Size of Sums-of-Squares Formulas Journal of Number Theory, 128 (2008) pp. 639-644.
• Daniel M. Kane Improved Bounds on the Number of Ways of Expressing t as a Binomial Coefficient, Integers: Electronic Journal of Combinatorial Number Theory, Vol. 7 (2007), #A53 pp. 1-7.
• Dan Gulotta, Daniel M. Kane, Andrew Spann Electoral Redistricting with Moment of Inertia and Diminishing Halves Models(3.81 MB) UMAP Journal, Vol. 28 (2007), pp. 281-299
• Daniel M. Kane Weak Mixing of a Transformation Similar to Pascal, Colloquium Mathematicum, Vol. 108 no. 1(2007), pp. 135-140.
• Daniel M. Kane Asymptotics of McKay Numbers for Sn, Journal of Number Theory, Vol. 124 (2007) pp. 200-228.
• Dan Gulotta, Daniel M. Kane, Andrew Spann Application of Min-Cost Flow to Airline Accessibility Services UMAP Journal, Vol. 27 (2006), pp. 367-385.
• Daniel M. Kane Generalized Base Representations Journal of Number Theory, Vol. 120 (2006) pp. 92-100.
• Daniel M. Kane, Jonathan M. Kane Dropping Lowest Grades Mathematics Magazine, Vol. 79 (June 2006) pp. 181-189.
• Daniel M. Kane An Elementary Derivation of the Asymptotics of Partition Functions The Ramanujan Journal, Vol. 11 no. 1(2006) pp. 49-66.
• Tim G. Abbott, Daniel M. Kane, Paul Valiant On the Complexity of Two-Player Win-Lose Games Foundations Of Computer Science (FOCS) 2005 (Won Machtey award for best student paper).
• Timothy G. Abbott, Michael A. Burr, Timothy M. Chan, Erik D. Demaine, Martin L. Demaine, John Hugg, Daniel Kane, Stefan Langerman, Jelani Nelson, Eynat Rafalin, Kathryn Seyboth, Vincent Yeung, Dynamic Ham-Sandwich Cuts in the Plane, Computational Geometry: Theory and Applications, Vol. 42, no. 5, July 2009, pages 419–428. Special issue of selected papers from the 17th Canadian Conference on Computational Geometry, 2005.
• Tim Abbott, Erik D. Demaine, Martin L. Demaine, Daniel M. Kane, Setfan Langerman, Jelani Nelson, Vincent Yeung Dynamic Ham-Sandwich Cuts of Polygons in the Plane Canadian Conference on Computational Geometry, (2005) pp. 61-64.
• Dan Gulotta, Daniel M. Kane, Andrew Spann Lane Changes and Close Following: Troublesome Tollbooth Traffic(6 MB) UMAP Journal, Vol. 26 no. 3 (2005) pp. 251-264.
• Daniel M. Kane On the Number of Ways of Writing t as a Product of Factorials Integers: Electronic Journal of Combinatorial Number Theory, Vol. 5 (2005), #A02, pp. 1-10.
• Daniel M. Kane Resolution of a Conjecture Involving Cranks of Partitions of Andrews and Lewis Proceedings of the American Mathematical Society, Vol. 132 No. 8(2004), pp. 2247-2256.
• Daniel M. Kane New Bounds on the Number of Representations of t as a Binomial Coefficient Integers: Electronic Journal of Combinatorial Number Theory, Vol. 4 (2004), #A07, pp. 1-10.

By Topic:

• Computer Science:
• Learning Theory:
• Robust Statistics:
  
  • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination, International Conference on Machine Learning (ICML) 2024.
  • Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu Online Robust Mean Estimation, Symposium on Discrete Algorithms (SODA) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions, Advances in Neural Information Processing Systems (NeurIPS) 2023.
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression, Advances in Neural Information Processing Systems (NeurIPS) 2023.
  • Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm, Advances in Neural Information Processing Systems (NeurIPS) 2023 (spotlight presentation).
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA, International Conference on Machine Learning (ICML) 2023.
  • Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi Outlier-Robust Sparse Estimation via Non-Convex Optimization, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering, Advances in Neural Information Processing Systems (NeurIPS) 2022 (oral presentation).
  • Ilias Diakonikolas, Daniel M. Kane, Jasper C.H. Lee, Ankit Pensia Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas Robust Sparse Mean Estimation via Sum of Squares, Conference on Learning Theory (COLT) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun, Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models, Conference on Learning Theory (COLT) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas Streaming Algorithms for High-Dimensional Robust Statistics, International Conference on Machine Learning (ICML) 2022.
  • Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala, Robustly Learning Mixtures of k Arbitrary Gaussians, Symposium on Theory of Computation (STOC) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation, Symposium on Theory of Computation (STOC) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart Statistical Query Lower Bounds for List-Decodable Linear Regression, Conference on Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition, Conference on Learning Theory (COLT) 2021.
  • Ilias Diakonikolas, Daniel M. Kane, The Sample Complexity of Robust Covariance Testing, Conference on Learning Theory (COLT) 2021.
  • Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, Alistair Stewart Robustness Meets Algorithms, Communications of the ACM Vol 64(5) p. 107-115, 2021.
  • Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian, List-Decodable Mean Estimation in Nearly-PCA Time, Advances in Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
  • Daniel M. Kane Robust Learning of Mixtures of Gaussians,  Symposium On Discrete Algorithms (SODA) 2021.
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia Outlier Robust Mean Estimation with Subgaussian Rates via Stability, Advances in Neural Information Processing Systems (NeurIPS) 2020.
  • Ilias Diakonikolas, Daniel M. Kane, Nikos Zarifis Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals, Advances in Neural Information Processing Systems (NeurIPS) 2020.
  • Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard List-Decodable Mean Estimation via Iterative Multi-Fitering, Advances in Neural Information Processing Systems (NeurIPS) 2020.
  • Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar Robustly Learning any Clusterable Mixture of Gaussians, Foundations Of Computer Science (FOCS) 2020.
  • Ilias Diakonikolas, Daniel M. Kane Recent Advances in Algorithmic High-Dimensional Robust Statistics, shortened version in Tim Roughgarden's Beyond Worst Case Analysis book.
  • Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price, Alistair Stewart Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering, Advances in Neural Information Processing Systems (NeurIPS 2019).
    
  • Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart Sever: A Robust Meta-Algorithm for Stochastic Optimization, International Conference on Machine Learning (ICML) 2019.
  • Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Robust Learning of Fixed-Structure Bayesian Networks, Neural Information Processing Systems (NIPS) 2018.
  • Ilias Diakonikolas, Daniel M Kane, Alistair Stewart Learning Geometric Concepts with Nasty Noise, Symposium on Theory Of Computation (STOC) 2018.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians, Symposium on Theory Of Computation (STOC) 2018.
  • Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Robustly Learning a Gaussian: Getting Optimal Error Efficiently, Symposium On Discrete Algorithms (SODA) 2018.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Statistical Query Lower Bounds for Robust Estimation of High Dimensional Gaussians and Gaussian Mixtures, Foundations Of Computer Science (FOCS) 2017.
  • Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Being Robust in High Dimensions Can Be Practical, International Conference on Machine Learning (ICML) 2017.
  • Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart, Robust Estimators in High Dimensions without the Computational Intractability, Foundations Of Computer Science, (FOCS) 2016, journal version in SIAM Journal of computing (SICOMP) Vol 48, no 2 (2019), pp. 742-864.
• Testing Discrete Distributions:
  
  • Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions, Conference on Learning Theory (COLT) 2024.
  • Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu Near-Optimal Bounds for Testing Histogram Distributions, Advances in Neural Information Processing Systems (NeurIPS) 2022.
  • Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price, Optimal Testing of Discrete Distributions with High Probability, Symposium on Theory of Computation (STOC) 2021.
  • M. Aliakbarpour, I. Diakonikolas, D. Kane, R. Rubinfeld Private Testing of Distributions via Sample Permutations, Advances in Neural Information Processing Systems (NeurIPS 2019).
  • Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao, Communication and Memory Efficient Testing of Discrete Distributions, Conference on Learning Theory (COLT) 2019.
  • Ilias Diakonikolas, Daniel M Kane, Alistair Stewart Sharp Bounds for Generalized Uniformity Testing, Advances in Neural Information Processing Systems (NeurIPS) 2018, Spotlight Presentation at NeurIPS 2018.
  • Clement Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Testing Conditional Independence of Discrete Distributions, Symposium on Theory Of Computation (STOC) 2018.
  • Ilias Diakonikolas, Daniel M. Kane, A New Approach for Testing Properties of Discrete Distributions, Foundations Of Computer Science, (FOCS) 2016.
• Structured Learning/Testing Problems:
  
  • Ilias Diakonikolas, Daniel Kane, Mingchen Ma Active Learning of General Halfspaces: Label Queries vs Membership Queries, Advances in Neural Information Processing Systems (NeurIPS) 2024.
  • Ilias Diakonikolas, Daniel M. Kane Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials, Conference on Learning Theory (COLT) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis Statistical Query Lower Bounds for Learning Truncated Gaussians, Conference on Learning Theory (COLT) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Sihan Liu Testing Closeness of Multivariate Distributions via Ramsey Theory, Symposium on Theory of Computation (STOC) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun SQ Lower Bounds for Learning Mixtures of Linear Classifiers, Advances in Neural Information Processing Systems (NeurIPS) 2023.
  • Ilias Diakonikolas, Daniel M. Kane Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials, in preparation.
  • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis SQ Lower Bounds for Learning Bounded Covariance GMMs, Conference on Learning Theory (COLT) 2023.
  • Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru R. Zhang Statistical and Computational Limits for Tensor-on-Tensor Association Detection, Conference on Learning Theory (COLT) 2023.
  • Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia Gaussian Mean Testing Made Simple, Symposium on Simplicity in Algorithms (SOSA 2023).
  • Ilias Diakonikolas, Daniel M. Kane, John Peebles Testing Identity of Multidimensional Histograms, Conference on Learning Theory (COLT) 2019.
  • Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin Near-Optimal Closeness Testing of Discrete Histogram Distributions, International Colloquium on Automata, Languages and Programming (ICALP) 2017.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables, Conference On Learning Theory, (COLT) 2016.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Properly Learning Poisson Binomial Distributions in Almost Polynomial Time, Conference On Learning Theory, (COLT) 2016.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications, Symposium on the Theory of Computation (STOC) 2016.
  • Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin, Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions, Foundations of Computer Science, (FOCS) 2015.
  • Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin, Testing Identity of Structured Distributions, Symposium On Discrete Algorithms (SODA) 2015.
• Active Learning and Inference Dimension:
  
  • Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz Bounded Memory Active Learning through Enriched Queries, Conference on Learning Theory (COLT) 2021.
  • Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan Point Location and Active Learning: Learning Halfspaces Almost Optimally, Foundations Of Computer Science (FOCS) 2020.
  • Max Hopkins, Daniel Kane, Shachar Lovett, The Power of Comparisons for Actively Learning Linear Classifiers, Advances in Neural Information Processing Systems (NeurIPS) 2020.
  • Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan Noise-tolerant, Reliable Active Classification with Comparison Queries, Conference On Learning Theory (COLT) 2020.
  • Daniel M Kane, Shachar Lovett, Shay Moran Generalized Comparison Trees for Point-Location Problems, International Colloquium on Automata, Languages and Programming (ICALP) 2018.
  • Daniel M Kane, Shachar Lovett, Shay Moran Near-Optimal Linear Decision Trees for k-SUM and Related Problems, Symposium on Theory Of Computation (STOC) 2018, invited to STOC special issue, Journal of the ACM, Vol 66, no 3 (2019).
  • Daniel M. Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang, Active Classification with Comparison Queries Foundations Of Computer Science (FOCS) 2017.
• Learning Functions:
  
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostically Learning Multi-index Models with Queries, Foundations Of Computer Science (FOCS) 2024.
  • Ilias Diakonikolas, Daniel M Kane, Sihan Liu, Nikos Zarifis Testable Learning of General Halfspaces with Adversarial Label Noise, Conference on Learning Theory (COLT) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostically Learning Multi-index Models with Queries, in preparation.
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis Efficient Testable Learning of Halfspaces with Adversarial Label Noise, Advances in Neural Information Processing Systems (NeurIPS) 2023.
  • Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals, International Conference on Machine Learning (ICML) 2023.
  • Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren Hardness of Learning a Single Neuron with Adversarial Label Noise International Conference on Artificial Intelligence and Statistics (AISTATS) 2022 (oral presentation).
  • Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos Forster Decomposition and Learning Halfspaces with Noise, Conference on Neural Information Processing Systems (NeurIPS) 2021, spotlight presentation.
  • Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals, Conference on Learning Theory (COLT) 2021.
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Agnostic Proper Learning of Halfspaces under Gaussian Marginals, Conference on Learning Theory (COLT) 2021.
  • Ilias Diakonikolas, Daniel M. Kane Small Covers for Near-zero Sets of Polynomials and Learning Latent Variable Models, Foundations Of Computer Science (FOCS) 2020.
  • Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise, Advances in Neural Information Processing Systems (NeurIPS) 2020.
  • Ilias Diakonikolas, Daniel Kane, Vasileios Kontonis, Nikos Zarifis Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks Conference On Learning Theory (COLT) 2020.
  • Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin, Advances in Neural Information Processing Systems (NeurIPS 2019) (Spotlight Presentation).
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis, A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise, Symposium on Theory of Computation (STOC) 2021.
  • Daniel Kane, Sushrut Karmalkar, Eric Price Robust Polynomial Regression up to the Information Theoretic Limit, Foundations of Computer Science (FOCS) 2017.
  • Xue Chen, Daniel M. Kane, Eric Price, Zhao Song, Fourier-sparse interpolation without a frequency gap, Foundations Of Computer Science, (FOCS) 2016.
• Semi-Random Noise Models:
• Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise, Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise, Conference on Learning Theory (COLT) 2023.
• Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun SQ Lower Bounds for Learning Single Neurons with Massart Noise, Advances in Neural Information Processing Systems (NeurIPS) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren Cryptographic Hardness of Learning Halfspaces with Massart Noise, Advances in Neural Information Processing Systems (NeurIPS) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise, Conference on Learning Theory (COLT) 2022.
• Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis Learning General Halfspaces with General Massart Noise under the Gaussian Distribution, Symposium on Theory of Computation (STOC) 2022.
• Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos Boosting in the Presence of Massart Noise, Conference on Learning Theory (COLT) 2021.
• Other Learning Theory:
  
  • Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye Replicability in High Dimensional Statistics, Foundations Of Computer Science (FOCS) 2024.
  • Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation, in preparation.
  • Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan Do PAC-Learners Learn the Marginal Distribution?, in preparation.
  • Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz Exponential Hardness of Reinforcement Learning with Linear Function Approximation, Conference on Learning Theory (COLT) 2023.
  • Ilias Diakonikolas, Christos Tzamos, Daniel Kane A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning, Symposium on Theory of Computation (STOC) 2023.
  • Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan Computational-Statistical Gaps in Reinforcement Learning, in preparation.
  • Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan Realizable Learning is All You Need, Conference on Learning Theory (COLT) 2022, Journal version TheoretiCS, Vol 3 (2024).
  • Ilias Diakonikolas, Daniel M. Kane Non-Gaussian Component Analysis via Lattice Basis Reduction, Conference on Learning Theory (COLT) 2022.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Efficient Robust Proper Learning of Log-concave Distributions, manuscript.
  • Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran The entropy of lies: playing twenty questions with a liar, Innovations in Theoretical Computer Science (ITCS) 2021.
  • Daniel M Kane, Roi Livni, Shay Moran, Amir Yehudayoff On Communication Complexity of Classification Problems, Conference on Learning Theory (COLT) 2019.
  • Surbhi Goel, Daniel M. Kane, Adam R. Klivans Learning Ising Models with Independent Failures, Conference on Learning Theory (COLT) 2019.
  • Olivier Bousquet, Daniel Kane, Shay Moran The Optimal Approximation Factor in Density Estimation, Conference on Learning Theory (COLT) 2019.
  • Clement Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Testing Bayesian Networks, Conference on Learning Theory (COLT) 2017; Journal version in IEEE Transactions on Information Theory pp. 1-39, 2020.
  • Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart Learning Multivariate Log-concave Distributions, Conference On Learning Theory (COLT) 2017.
• Polynomial Threshold Functions:
  
  • Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs, Symposium on Theory of Computation (STOC) 2024.
  • Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan with appendix by Daniel Kane Fooling Gaussian PTFs via Local Hyperconcentration, Journal of the ACM, to appear.
  • Ilias Diakonikolas, Daniel M. Kane, Degree-d Chow Parameters Robustly Determine Degree-d PTFs (and Algorithmic Applications) Symposium on Theory Of Computation (STOC) 2019.
  • Daniel M Kane, Sankeerth Rao A PRG for Boolean PTF of Degree 2 with Seed Length Subpolynomial in ε and Logarithmic in n, Conference on Computational Complexity (CCC) 2018.
  • Valentine Kabanets, Daniel M. Kane, Zhenjian Lu A Polynomial Restriction Lemma with Applications, Symposium on Theory Of Computation (STOC) 2017.
  • Parikshit Gopalan, Daniel M. Kane, Raghu Meka, Pseudorandomness via the Discrete Fourier Transform, Foundations of Computer Science (FOCS) 2015, SIAM Journal of computing (SICOMP) Vol 47, no 6 pp. 2451-2487.
  • Daniel M. Kane A Polylogarthmic PRG for Degree 2 Threshold Functions in the Gaussian Setting, Conference on Computational Complexity (CCC) 2015.
  • Daniel M. Kane The Average Sensitivity of an Intersection of Half Spaces, Symposium on the Theory Of Computing 2014, journal version (open access) in Research In the Mathematical Sciences Vol. 1 no 1 (2014).
  • Daniel M. Kane A Pseudorandom Generator for Polynomial Threshold Functions of Gaussians with Subpolynomial Seed Length, Conference on Computational Complexity 2014.
  • Daniel M. Kane, Adam Klivans, Raghu Meka Learning Half Spaces Under Log-Concave Densities: Polynomial Approximation and Moment Matching, Conference on Learning Theory (COLT) 2013.
  • Daniel M. Kane The Correct Exponent for the Gotsman-Linial Conjecture, Conference on Computational Complexity (CCC) 2013 (won best paper award).
  • Daniel M. Kane, Raghu Meka A PRG for Lipschitz Functions of Polynomials with Applications to Sparsest Cut, Symposium on the Theory of Computation (STOC) 2013.
  • Daniel M. Kane A Structure Theorem for Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions, Foundations of Computer Science (FOCS) 2012, pp. 91-100; journal version Annals of Probability, Vol. 45 no. 3 (2017) pp. 1612-1679.
  • Daniel M. Kane, A Small PRG for Polynomial Threshold Functions of Gaussians, Foundations of Computer Science (FOCS) 2011.
  • Daniel M. Kane k-Independent Gaussians Fool Polynomial Threshold Functions, Conference on Computational Complexity (CCC), 2011.
  • Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson, Bounded Independence Fools Degree-2 Threshold Functions, Foundations of Computer Science (FOCS) 2010.
  • Daniel M. Kane The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions, in Conference on Computational Complexity (CCC) 2010, pp. 205-210 (Won CCC 2010 best student paper).
• Streaming Algorithms:
  
  • Daniel M. Kane, Kurt Mehlhorn, Thomas Sauerwald, He Sun Counting Arbitrary Subgraphs in Data Streams, International Colloquium on Automata, Languages and Programming (ICALP) 2012, pp. 598-609.
  • Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff, Fast Moment Estimation in Data Streams in Optimal Space, Symposium on the Theory of Computing (STOC) 2011.
  • Daniel M. Kane, Jelani Nelson, David P. Woodruff An Optimal Algorithm for the Distinct Elements Problem, Symposium on Principles of Database Systems (PODS) 2010 (Won PODS 2010 best paper, and 2010 IBM research Pat Goldberg Memorial Best Paper Award in Computer Science, Electrical Engineering and Math). Invited to Journal of the ACM.
  • Daniel M. Kane, Jelani Nelson, David P. Woodruff On the Exact Space Complexity of Sketching and Streaming Small Norms, Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) 2010.
• Johnson-Lindenstrauss:
  
  • Daniel M. Kane, Jelani Nelson, Sparser Johnson-Lindenstrauss Transforms, Symposium on Discrete Algorithms (SODA) 2012, Journal of the ACM, Vol. 61 no. 1, Article 4, 2014.
  • Daniel M. Kane, Raghu Meka, Jelani Nelson, Almost Optimal Explicit Johnson-Lindenstrauss Transforms, International Workshop on Randomization and Computation (RANDOM), 2011.
  • Daniel Kane, Jelani Nelson, A Derandomized Sparse Johnson-Lindenstrauss Transform, superseded by Sparser Johnson-Lindenstrauss Transforms (above).
• Circuit Complexity:
  
  • Daniel M Kane, Ryan Williams The Orthogonal Vectors Conjecture for Branching Programs and Formulas, Innovations in Theoretical Computer Science (ITCS) 2019.
  • Daniel M. Kane, Ryan Williams, Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits, Symposium on the Theory of Computation (STOC) 2016.
• Security:
  
  • Mihir Bellare, Daniel M. Kane, Phillip Rogaway Big-Key Symmetric Encryption: Resisting Key Exfiltration, International Cryptography Conference (CRYPTO) 2016.
  • Mihir Bellare, Joseph Jaeger, Daniel Kane, Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks on Symmetric Encryption, Conference on Computer and Communications Security (CCS) 2016.
• Computational Geometry:
  
  • Daniel M. Kane, Gregory N. Price, Erik D. Demaine, A Pseudopolynomial Algorithm for Alexandrov's Theorem, Algorithms and Data Structures Symposium (WADS) 2009. Also in Lecture Notes in Computer Science, 5664 (2009) pp. 435–446.
  • Timothy G. Abbott, Michael A. Burr, Timothy M. Chan, Erik D. Demaine, Martin L. Demaine, John Hugg, Daniel Kane, Stefan Langerman, Jelani Nelson, Eynat Rafalin, Kathryn Seyboth, Vincent Yeung, Dynamic Ham-Sandwich Cuts in the Plane, Computational Geometry: Theory and Applications, Vol. 42, no. 5, July 2009, pages 419–428. Special issue of selected papers from the 17th Canadian Conference on Computational Geometry, 2005.
  • Tim Abbott, Erik D. Demaine, Martin L. Demaine, Daniel M. Kane, Setfan Langerman, Jelani Nelson, Vincent Yeung Dynamic Ham-Sandwich Cuts of Polygons in the Plane Canadian Conference on Computational Geometry, (2005) pp. 61-64.
• Other Computer Science:
  
  • Daniel M. Kane, Anthony Ostuni, Kewen Wu Locality Bounds for Sampling Hamming Slices, Symposium on Theory of Computation (STOC) 2024.
  • Daniel M. Kane, Ilias Diakonikolas A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points, Conference on Learning Theory (COLT) 2023.
  • Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett Sampling Equilibria: Fast No-Regret Learning in Structured Games, Symposium on Discrete Algorithms (SODA) 2023.
    
  • Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman Coresets for Data Discretization and Sine Wave Fitting, Conference on Artificial Intelligence and Statistics (AISTATS) 2022.
    
  • Daniel Kane, Andreas Fackler, Adam Gągol, Damian Straszak, Highway: Efficient Consensus with Flexible Finality, in preparation.
  • Daniel M. Kane, Shahed Sharif, Alice Silverberg Quantum Money from Quaternion Algebras, MathCrypt 2022.
  • Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar Vector Quantized Stochastic Gradient Descent, 4th Asilomar Conference on Signals, Systems and Computers (Asilomar) 2020, journal version in IEEE Transactions on Information Theory, 2022.
  • Jeffery S. Cohen, Daniel M. Kane Bounds on the Independence Required for Cuckoo Hashing, manuscript.
    
  • Chung-Kuan Cheng, Daniel M. Kane, Ilgweon Kang, Fang Qiao, 3D Floorplan Representations: Corner Links and Partial Ordering, IEEE International 3D Systems Integration Conference (3DIC) 2016, Journal Version: Kang, I., Qiao, F., Park, D., Kane, D., Young, E.F.Y., Cheng, C.K., Graham, R. Three-dimensional Floorplan Representations by Using Corner Links and Partial Order ACM Transactions on Design Automation of Electronic Systems (TODAES), 24(1), p.13, 2018.
  • Daniel M Kane, Shachar Lovett, Sankeerth Rao, The Independence Number of the Birkhoff Polytope Graph and Applications to Maximally Recoverable Codes, Foundations Of Computer Science (FOCS) 2017, journal version SIAM Journal of computing (SICOMP) Vol 48, no 4 (2019) pp 1425-1435.
  • Daniel M. Kane, Osamu Watanabe A Short Implicant of CNFs with a Relatively Many Satisfying Assignments, International Symposium on Algorithms And Computation (ISAAC) 2014; journal version Algorithmica, (2016), DOI: 10.1007/s00453-016-0125-z.
  • Daniel M. Kane A Low-Depth Monotone Function Given by a Low-Depth Decision Tree that is not an Approximate Junta, Theory of Computing Vol. 9 (2013) pp. 587-592.
  • Eric Blais, Daniel Kane Tight Bounds for Testing k-Linearity, International Workshop on Randomization and Computation (RANDOM) 2012.
  • Daniel M. Kane, Samuel A. Kutin, Quantum Interpolation of Polynomials, presented at Combinatorics, Groups, Algorithms, and Complexity (conference in honor of Laci Babai’s 60^th birthday (CGAC) 2010, journal version in Quantum Information and Computation Vol. 11 no. 1&2 (2011).
  • Daniel M. Kane Unary Subset-Sum is in Logspace, unpublished.
  • Erik D. Demaine, Dion Harmon, John Iacono, Daniel M. Kane, Mihai Pǎtraşcu, The Geometry of Binary Search Trees, in Symposium on Discrete Algorithms (SODA) 2009.
  • Daniel M. Kane, Jonathan M. Kane Dropping Lowest Grades Mathematics Magazine, Vol. 79 (June 2006) pp. 181-189.
  • Tim G. Abbott, Daniel M. Kane, Paul Valiant On the Complexity of Two-Player Win-Lose Games Foundations Of Computer Science (FOCS) 2005 (Won Machtey award for best student paper).
• Mathematics:
• Number Theory:
• Arithmetic Statistics:
  
  • Daniel M. Kane, Zev Klagsbrun, On the Joint Distribution Of Sel_φ(E/Q) and Sel_φ^(E/Q) in Quadratic Twist Families, in preparation.
  • Daniel M. Kane, Jack A. Thorne, On the φ-Selmer Groups of Elliptic Curves y² = x³ – Dx, Mathematical Proceedings of the Cambridge Philosophical Society, Vol 163 no 1 (2017) pp. 1–23.
  • Manjul Bhargava, Daniel M. Kane, Hendrik W. Lenstra Jr., Bjorn Poonen, Eric Rains Modeling the Distribution of Ranks, Selmer Groups, and Shafarevich-Tate Groups of Elliptic Curves, Cambridge Journal of Mathematics, Vol. 3 (2015), pp. 275-321.
  • Daniel M. Kane On the Ranks of the 2-Selmer Groups of Twists of a Given Elliptic Curve, Algebra & Number Theory, 7 issue 5 (2013), pp. 1253-1297.
• Additive Combinatorics:
  
  • Ben Green, Daniel M. Kane, An Example Concerning Set Addition in F_2^n, Trudy Matematicheskogo Instituta im. V.A. Steklova Vol 303 (2018) pp. 116-119.
  • Daniel M. Kane Asymptotic Results for the Queen Packing Problem, submitted to Journal of Combinatorics.
  • Daniel M. Kane, Carlo Sanna, Jeffrey Shallit Waring's Theorem for Binary Powers, Combinatorica Vol 39 (2019) pp. 1335-1350.
  • Daniel M. Kane, Terence Tao A Bound on Partitioning Clusters, Electronic Journal of Combinatorics Vol 24 no 2 (2017) #P2.31.
• Other Number Theory:
  
  • Daniel M. Kane, Robert C. Rhoades A Proof of Andrews' Conjecture on Partitions with no Short Sequences, Forum of Mathematics Sigma, Vol 7, 2019.
  • Daniel M. Kane On the Crossing Number of Complete Graphs with an Uncrossed Hamiltonian Cycle, manuscript.
  • Daniel M. Kane On the Number of ABC Solutions with Restricted Radical Sizes, Journal of Number Theory, (2015), pp. 32-43.
  • Andrew Granville, Daniel M Kane, Dimitris Koukoulopoulos, Robert J Lemke Oliver Best Possible Densities of Dickson m-Tuples, as a Consequence of Zhang–Maynard–Tao in Analytic Number Theory Springer, 2015.
  • Daniel M. Kane, Scott Duke Kominers Asymptotic Improvements of Lower Bounds for the Least Common Multiples of Arithmetic Progressions, Canadian Mathematical Bulletin, Vol. 57 (2014), pp. 551-561
  • Noam D. Elkies, Daniel M. Kane, Scott Duke Kominers, Minimal S-Criteria May Vary in Size, Journal de Theorie des Nombres de Bordeaux, Vol. 23 no. 3 (2013) pp. 557-563.
  • Daniel M. Kane An Asymptotic for the Number of Solutions to Linear Equations in Prime Numbers from Specified Chebotarev Classes, International Journal of Number Theory, Vol. 9 no. 4 (2013) pp. 1073-1111.
  • Bakir Farhi, Daniel Kane New Results on the Least Common Multiple of Consecutive Integers, Proceedings of the AMS, 137 (2009), no. 6, pp. 1933-1939.
  • Daniel Kane, Steven Sivek On the S_n-Modules Generated by Partitions of a Given Shape, The Electronic Journal of Combinatorics, 15 (2008), 12 pages.
  • Daniel M. Kane On Lower Bounds on the Size of Sums-of-Squares Formulas Journal of Number Theory, 128 (2008) pp. 639-644.
  • Daniel M. Kane Improved Bounds on the Number of Ways of Expressing t as a Binomial Coefficient, Integers: Electronic Journal of Combinatorial Number Theory, Vol. 7 (2007), #A53 pp. 1-7.
  • Daniel M. Kane Asymptotics of McKay Numbers for Sn, Journal of Number Theory, Vol. 124 (2007) pp. 200-228.
  • Daniel M. Kane Generalized Base Representations Journal of Number Theory, Vol. 120 (2006) pp. 92-100.
  • Daniel M. Kane An Elementary Derivation of the Asymptotics of Partition Functions The Ramanujan Journal, Vol. 11 no. 1(2006) pp. 49-66.
  • Daniel M. Kane On the Number of Ways of Writing t as a Product of Factorials Integers: Electronic Journal of Combinatorial Number Theory, Vol. 5 (2005), #A02, pp. 1-10.
  • Daniel M. Kane Resolution of a Conjecture Involving Cranks of Partitions of Andrews and Lewis Proceedings of the American Mathematical Society, Vol. 132 No. 8(2004), pp. 2247-2256.
  • Daniel M. Kane New Bounds on the Number of Representations of t as a Binomial Coefficient Integers: Electronic Journal of Combinatorial Number Theory, Vol. 4 (2004), #A07, pp. 1-10.
• Semitoric Manifolds:
  
  • Daniel M. Kane, Joseph Palmer, Alvaro Pelayo Minimal Models of Compact Symplectic Semiotic Manifolds, Journal of Geometry and Physics, Vol 125 (2018) pp. 48-74.
  • Daniel M. Kane, Joseph Palmer, Alvaro Pelayo Classifying Toric and Semitoric Fans by Lifting Equations from SL₂(Z), SIGMA Vol 14 (2018).
• COMAP MCM Papers:
  
  • Dan Gulotta, Daniel M. Kane, Andrew Spann Electoral Redistricting with Moment of Inertia and Diminishing Halves Models(3.81 MB) UMAP Journal, Vol. 28 (2007), pp. 281-299.
  • Dan Gulotta, Daniel M. Kane, Andrew Spann Application of Min-Cost Flow to Airline Accessibility Services UMAP Journal, Vol. 27 (2006), pp. 367-385.
  • Dan Gulotta, Daniel M. Kane, Andrew Spann Lane Changes and Close Following: Troublesome Tollbooth Traffic(6 MB) UMAP Journal, Vol. 26 no. 3 (2005) pp. 251-264.
• Other Mathematics
  
  • Ery Arias-Castro, Clement Berenfeld, Daniel Kane Theoretical Foundations of Ordinal Multidimensional Scaling, Including Internal and External Unfolding, in preparation.
  • Daniel M. Kane, Scott Duke Kominers Prisoners, Rooms, and Lightswitches, Electronic Journal of Combinatorics, Vol 28 (1), 2021 pp. 1-27.
  • Daniel M. Kane Small Designs for Path Connected Spaces and Path Connected Homogeneous Spaces, Transactions of the AMS, Vol. 367 (2015), pp. 6387-6414.
  • Daniel M. Kane Canonical Projective Embeddings of the Deligne-Lusztig Curves Associated to ²A₂, ²B₂ and ²G₂, International Mathematics Research Notices, (2015) pp. 1158-1189.
  • Bobbie Chern, Persi Diaconis, Daniel M. Kane, Robert C. Rhoades Central Limit Theorems for Some Set Partition Statistics, Advances in Applied Mathematics, Vol. 70 (2015), pp. 92–105.
  • Bobbie Chern, Persi Diaconis, Daniel M. Kane, Robert C. Rhoades Closed Expressions for Averages of Set Partition Statistics, Research in the Mathematical Sciences, Vol. 1 (2014) no. 2.
  • Chris Dodd, Phakawa Jeasakul, Anne Jirapattanakul, Daniel M. Kane, Becky Robinson, Noah Stein, Cesar E. Silva Ergodic Properties of a Class of Discrete Abelian Group Extensions of Rank-One Transformations, Colloquium Mathematicum, 119 (2010), pp. 1-22.
  • Daniel M. Kane On Solving Games Constructed Using Both Shortened and Continued Conjunctive Sums, Integers: Electronic Journal of Combinatorial Number Theory, 10 (2010), pp. 849-878.
  • Daniel M. Kane A Partition of the Positive Reals into Algebraically Closed Subsets, unpublished.
  • Daniel M. Kane Weak Mixing of a Transformation Similar to Pascal, Colloquium Mathematicum, Vol. 108 no. 1(2007), pp. 135-140.

Coauthors: Tim Abbott, Ery Arias-Castro, Daniel Beaglehole, Mihir Bellare, Clement Berenfeld, Manjul Bhargava, Eric Blais, Olivier Bousquet, Michael A. Burr, Clement Canonne, Timothy M. Chan, Xue Chen, Chung-Kuan Cheng, Yu Cheng, Yuqian Cheng, Bobbie Chern, Jeffery Cohen, Yuval Dagan, Erik Demaine, Martin Demaine, Ilias Diakonikolas, Chris Dodd, Noam Elkies, Andreas Fackler, Bakir Farhi, Dan Feldman, Yuval Filmus, Adam Gągol, Venkata Gandikota, Rong Ge, Surbhi Goel, Themis Gouleakis, Ron Graham, Andrew Granville, Parikshit Gopalan, Ben Green, Dan Gulotta, Shivam Gupta, Max Hopkins, Samuel B. Hopkins, John Hugg, John Iacono, Russell Impagliazzo, Joseph Jaeger, Phakawa Jeasakul, He Jia, Anne Jirapattanakul, Gautam Kamath, Ilgweon Kang, Valentine Kabanets, Jonathan Kane, Ilgweon Kang, Sushrut Karmalkar, Zev Klagsbrun, Adam Klivans, Scott Kominers, Daniel Kongsgaard, Vasileios Kontonis, Pravesh Kothari, Dimitris Koukoulopoulos, Samuel A. Kutin, Setfan Langerman, Rex Lei, Robert Lemke Oliver, Hendrik W. Lenstra Jr., Jerry Li, Sihan Liu, Roi Livni, Shachar Lovett, Zhenjian Lu, Yuetian Luo, Alaa Maalouf, Gaurav Mahajan, Pasin Manurangsi, Raj Kumar Maity, Arya Mazumdar, Kurt Mehlhorn, Raghu Meka, Ankur Moitra, Shay Moran, Michal Moshkovitz, Jelani Nelson, Vladimir Nikishkin, Ryan O'Donnell, Anthony Ostuni, Joseph Palmer,  Dongwon Park, Mihai Pǎtraşcu, John Peebles, Alvaro Pelayo, Ankit Pensia, Thanasis Pittas, Bjorn Poonen, Ely Porat, Eric Price, Gregory N. Price, Fang Qiao, Eynat Rafalin, Eric Rains, Sankeerth Rao, Lisheng Ren, Robert Rhoades, Becky Robinson, Phillip Rogaway, Carlo Sanna, Thomas Sauerwald, Rocco A. Servedio, Kathryn Seyboth, Jeffrey Shallit, Shahed Sharif, Cesar E. Silva, Alice Silverberg, Mahdi Soltanolkotabi, Zhao Song, Jessica Sorrell, Andrew Spann, Jacob Steinhardt, Alistair Stewart, Noah Stein, Damian Straszak, He Sun, Yuxin Sun, Csaba Szepesvári, Li-Yang Tan, Terence Tao, Kevin Tian, Jack A. Thorne, Murad Tukan, Christos Tzamos, Paul Valiant, Santosh Vempala, Puqian Wang, Osamu Watanabe, Gellért Weisz, Ryan Williams, David Woodruff, Kewen Wu, Hanshen Xiao, Vincent Yeung, Amir Yehudayoff, Evangeline Fung Yu Young, Nikos Zarifis, Anru Zhang, Zhicheng Zheng