Publications (chronological order)
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2025
- Not-a-Bandit: Provably No-Regret Drafter Selection in Speculative Decoding for LLMs
Hongyi Liu, Jiaji Huang, Zhen Jia, Youngsuk Park, Yu-Xiang Wang
Manuscript [arxiv]
- Generalization Below the Edge of Stability: The Role of Data Geometry
Tongtong Liang, Alexander Cloninger, Rahul Parhi, Yu-Xiang Wang
Manuscript [arxiv]
- DPCheatSheet: Using Worked and Erroneous LLM-usage Examples to Scaffold
Differential Privacy Implementation
Shao-Yu Chu, Yuhe Tian, Yu-Xiang Wang, Haojian Jin
Manuscript. [arxiv]
- Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
Tongtong Liang, Dan Qiao, Yu-Xiang Wang, Rahul Parhi.
NeurIPS 2025. [arxiv] (*Spotlight)
-
Purifying Approximate Differential Privacy with Randomized Post-processing
Yingyu Lin, Erchi Wang, Yi-An Ma, Yu-Xiang Wang.
NeurIPS 2025. [arxiv] (*Spotlight)
- A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks
Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang
NeurIPS 2025 (Dataset and Benchmark Track). [available soon, Website]
- Dynamic Pricing with Adversarially-Censored Demands.
Jianyu Xu, Yining Wang, Xi Chen, Yu-Xiang Wang
Conference on Web and Internet Economics (WINE 2025)[arxiv]
- Efficiently Identifying Watermarked Segments in Mixed-Source Texts
Xuandong Zhao, Chenwen Liao, Yu-Xiang Wang, Lei Li
ACL 2025 [arxiv, code]
-
Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning
Erchi Wang, Yuqing Zhu, Yu-Xiang Wang.
ICML 2025 [arxiv]
- AKORN: Adaptive Knots generated Online for RegressioN Splines
Sunil Madhow, Dheeraj Baby, Yu-Xiang Wang.
ICML 2025[pdf]
- Adaptive Estimation and Learning under Temporal Distribution Shift
Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen, Yu-Xiang Wang, Rohit Pyati
ICML 2025 [arxiv]
- Weak-to-Strong Jailbreaking on Large Language Models
Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang
ICML 2025 [arxiv, code]
- ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs
Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis
ICML 2025 [arxiv]
- A Proximal Operator for Inducing 2:4-Sparsity
Jonas M Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis
Transactions on Machine Learning Research [arxiv]
- Permute-And-Flip: An Optimally Robust and Watermarkable Decoder for LLMs
Xuandong Zhao, Lei Li, Yu-Xiang Wang.
ICLR 2025 [arxiv, code]
- Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang, Cuixiong Hu, Bernie Wang, Yu-Xiang Wang
AISTATS 2025 [arxiv]
- On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures
Ming Yin, Mengdi Wang, Yu-Xiang Wang.
Accepted to Statistical Science [arxiv]
- SoK: Watermarking for AI-Generated Content
Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andres Fabrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramer, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song.
IEEE S&P 2025. [arxiv]
- Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach
Jianyu Xu, Xuan Wang, Yu-Xiang Wang, Jiashuo Jiang
Manuscript [arxiv]
2024
- Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes
Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang.
NeurIPS 2024. [arxiv] (*Spotlight)
- Invisible Image Watermarks Are Provably Removable Using Generative AI
Xuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan, Ilya Grishchenko, Christopher Kruegel, Giovanni Vigna, Yu-Xiang Wang, Lei Li.
NeurIPS 2024 [arxiv, code]
- Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger.
NeurIPS 2024. [arxiv]
- Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mendi Wang, Tuo Zhao, Yu-Xiang Wang.
NeurIPS 2024 [arxiv]
- Differentially Private Reinforcement Learning with Self-Play
Dan Qiao, Yu-Xiang Wang.
NeurIPS 2024. [arxiv]
- NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang
NeurIPS 2024 (Dataset and Benchmark Track). [arxiv, github]
- Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning
Chendi Wang, Yuqing Zhu, Weijie Su, Yu-Xiang Wang
ICML 2024. [arxiv] (*Oral Presentation)
- Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
Dan Qiao, Yu-Xiang Wang.
ICML 2024. [arxiv]
- Pricing with Contextual Elasticity and Heteroscedastic Valuation.
Jianyu Xu, Yu-Xiang Wang.
ICML 2024[arxiv] (*Spotlight)
-
Privacy Profiles for Private Selection
Antti Koskela, Rachel Redberg, Yu-Xiang Wang.
ICML 2024. [arxiv]
- Differentially Private Bias-Term only Fine-tuning of Foundation Models
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis.
ICML 2024 [arxiv]. Short version appeared at NeurIPS'22 TSRML Workshop
- Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
ICML 2024. [arxiv]
- Provable Robust Watermarking for AI-Generated Text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, Yu-Xiang Wang.
ICLR 2024 [arxiv, slides, code, demo]
-
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Yingyu Lin, Yi-An Ma, Yu-Xiang Wang, Rachel Redberg.
ICLR 2024. [arxiv]
- Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li, Chong Liu, Yu-Xiang Wang.
ICLR 2024 [arxiv]
- Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Participants of 2022 June Workshop "Differential Privacy: Challenges Towards The Next Fronter".
Harvard Data Science Review [paper, arxiv]
- Towards General Function Approximation in Nonstationary Reinforcement Learning
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
IEEE Journal on Selected Areas in Information Theory. [ieee]
- Improved differentially private regression via gradient boosting
Shuai Tang, Sergul Aydore, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Steven Wu
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) [arxiv]
- CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
CVPR 2024. [arxiv]
2023
- Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang.
NeurIPS 2023. [arxiv, code] (*Spotlight Presentation)
-
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Rachel Redberg, Antti Koskela, Yu-Xiang Wang.
NeurIPS 2023. [arxiv]
- Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal.
NeurIPS 2023. [arxiv] (*Spotlight Presentation)
- Offline Reinforcement Learning with Differential Privacy
Dan Qiao, Yu-Xiang Wang.
NeurIPS 2023. [arxiv]
- Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis.
NeurIPS 2023. [arxiv]. Short version appeared at ICML'22 TPDP workshop.)
- Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma
NeurIPS 2023. [arxiv]
- Non-stationary Online Learning with Memory and Non-stochastic Control
Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou
Journal of Machine Learning Research (JMLR), 2023 [pdf] Shorter version appeared in AISTATS 2022.
- "Private Prediction Strikes back!" Private Kernelized Nearest Neighbors with Individual Renyi Filter
Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang.
UAI 2023. [arxiv] (*Spotlight Presentation)
- No-Regret Linear Bandits beyond Realizability
Chong Liu, Ming Yin, Yu-Xiang Wang.
UAI 2023. [arxiv]
- Protecting Language Generation Models via Invisible Watermarking
Xuandong Zhao, Yu-Xiang Wang, Lei Li.
ICML 2023. [arxiv]
- Global Optimization with Parametric Function Approximation
Chong Liu, Yu-Xiang Wang.
ICML 2023. [arxiv]
- Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang.
ICML 2023. [arxiv, code]
- Non-stationary Reinforcement Learning under General Function Approximation
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
ICML 2023. [arxiv]
- Differentially Private Optimization on Large Model at Small Cost
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis.
ICML 2023. [arxiv, code]
- Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
Ming Yin, Mengdi Wang, Yu-Xiang Wang.
ICLR 2023. [arxiv]
- Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
Kaiqi Zhang, Yu-Xiang Wang.
ICLR 2023. [arxiv]
- Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
Dan Qiao, Yu-Xiang Wang.
ICLR 2023. [arxiv]
- Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang.
AISTATS 2023. [arxiv] (*Plenary Oral Presentation)
- Doubly Fair Dynamic Pricing
Jianyu Xu, Dan Qiao, Yu-Xiang Wang.
AISTATS 2023 [arxiv]
- Near-Optimal Differentially Private Reinforcement Learning
Dan Qiao, Yu-Xiang Wang.
AISTATS 2023 [arxiv]
- Second Order Path Variationals in Non-Stationary Online Learning
Dheeraj Baby, Yu-Xiang Wang.
AISTATS 2023. [arxiv]
- Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Sunil Madhow, Dan Qiao, Ming Yin, Yu-Xiang Wang.
Annual Learning for Dynamics & Control Conference (L4DC 2025) [arxiv]
2022
- Non-stationary Contextual Pricing with Safety Constraints
Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang.
Transaction of Machine Learning Research [openreview]
- Optimal Dynamic Regret in LQR Control
Dheeraj Baby, Yu-Xiang Wang.
NeurIPS 2022. [arxiv]
- Differentially Private Linear Sketches: Efficient Implementations and Applications
Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang.
NeurIPS 2022. [arxiv]
- SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin Zhang, He He
NeurIPS 2022. [openreview]
- Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang.
ICML 2022. [arxiv]
- Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang.
UAI 2022. [arxiv]
- Distillation-Resistant Watermarking for Model Protection in NLP
Xuandong Zhao, Lei Li, Yu-Xiang Wang.
Findings of EMNLP 2022. [arxiv]
- Provably Confidential Language Modelling
Xuandong Zhao, Lei Li, Yu-Xiang Wang.
NAACL 2022. [arxiv] (*Oral Presentation)
- Mixed Differential Privacy in Computer Vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto.
CVPR 2022. [arxiv](*Oral Presentation)
- Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang.
ICLR 2022. [arxiv]
- Optimal Accounting of Differential Privacy via Characteristic Function
Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang.
AISTATS 2022. [arxiv]
- Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
Yuqing Zhu, Yu-Xiang Wang.
AISTATS 2022. [arxiv]
- Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
Jianyu Xu, Yu-Xiang Wang.
AISTATS 2022. [arxiv] (*Plenary Oral Presentation)
- Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
Dheeraj Baby, Yu-Xiang Wang.
AISTATS 2022. [arxiv]
2021
- Multivariate Trend Filtering for Lattice Data
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison Hu, Ryan Tibshirani.
Annals of Statistics [arxiv, DOI]
- Privately Publishable Per-instance Privacy
Rachel Redberg, Yu-Xiang Wang.
NeurIPS 2021. [arxiv]
- Towards Instance-Optimal Offline Reinforcement Learning with Pessimism
Ming Yin, Yu-Xiang Wang.
NeurIPS 2021. [arxiv]
- Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
Ming Yin, Yu-Xiang Wang.
NeurIPS 2021. [arxiv]
- Optimal Dynamic Regret in Exp-Concave Online Learning
Dheeraj Baby, Yu-Xiang Wang.
COLT 2021. [arxiv] (*Best Student Paper Award)
- Logarithmic Regret in Feature-Based Dynamic Pricing
Jianyu Xu, Yu-Xiang Wang.
NeurIPS 2021. [arxiv] (*Spotlight Presentation)
- Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Ming Yin, Yu Bai, Yu-Xiang Wang.
NeurIPS 2021. [arxiv]
- An Optimal Reduction of TV-Denoising to Adaptive Online Learning
Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang.
AISTATS 2021. [arxiv]
- Voting-based Approaches For Differentially Private Federated Learning
Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan chandraker and Yu-Xiang Wang
Manuscript. [arxiv]
2020
-
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Journal of Machine Learning Research. Shorter version appeared in AISTATS 2021. [arxiv]
- Inter-Series Attention Model for COVID-19 Forecasting
Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan
SDM 2021. [arxiv]
- Improving Sparse Vector Technique with Renyi Differential Privacy
Yuqing Zhu, Yu-Xiang Wang
NeurIPS 2020. [paper,supplement ]
- Adaptive Online Estimation of Piecewise Polynomial Trends
Dheeraj Baby, Yu-Xiang Wang.
NeurIPS 2020. [arxiv]
-
Near Optimal Provable Uniform Convergence in Offlin Policy Evaluation for Reinforcement Learning
Ming Yin, Yu Bai, Yu-Xiang Wang.
AISTATS 2021. (*Plenary oral presentation) [arxiv]
-
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
Christopher DeCarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang.
ICML 2020. [pdf, code]
-
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability.
Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, and Giovanni Vigna.
IEEE EuroS&P 2021. [arxiv]
-
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoff Gordon.
NeurIPS 2020. [arxiv]
-
Private-kNN: Practical Differential Privacy for Computer Vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang
CVPR 2020. [paper]
-
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
Ming Yin, Yu-Xiang Wang.
AISTATS 2020. [arxiv]
2019
- Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan
NeurIPS 2019. [arxiv]
- Online Forecasting of Total Variation-Bounded Sequences
Dheeraj Baby, Yu-Xiang Wang.
NeurIPS 2019. [arxiv]
A short version appeared at ICML'19 Time Series Workshop. (*Best Paper Honorable Mention)
- Doubly Robust Crowdsourcing
Chong Liu, Yu-Xiang Wang.
Journal of Artificial Intelligence Research [pdf]
- Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang.
NeurIPS 2019. [arxiv]
- Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang.
NeurIPS 2019. [arxiv]
- Poisson Subsampled Rényi Differential Privacy
Yuqing Zhu, Yu-Xiang Wang
ICML 2019. [paper,code]
2018
- Imitation Regularized Offline Learning
Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy
AISTATS 2019. [arxiv]
- A Higher-Order Kolmogorov-Smirnov Test
Veeranjaneyulu Sadhanala, Aaditya Ramdas, Yu-Xiang Wang and Ryan Tibshirani.
AISTATS 2019. [arxiv] (*Plenary oral presentation)
- ProxQuant: Quantized Neural Networks via Proximal Operators.
Yu Bai, Yu-Xiang Wang, and Edo Liberty.
ICLR 2019. [arxiv]
- Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
AISTATS 2019. [arxiv,code] (*Notable Paper Award, *Plenary oral presentation)
- Revisiting differentially private linear regression: optimal and adaptive prediction and estimation in unbounded domain
Yu-Xiang Wang
UAI 2018, Monterey, CA[arxiv] (*Plenary oral presentation)
- signSGD: compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar
ICML 2018, Stockholm, Sweden. [arxiv]
- Detecting and Correcting for Label Shift with Black Box Predictors
Zack Lipton, Yu-Xiang Wang, Alex Smola
ICML 2018, Stockholm, Sweden. [arxiv]
- Improving Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle, Yu-Xiang Wang
ICML 2018, Stockholm, Sweden. [arxiv]
2017
- Per-instance Differential Privacy
Yu-Xiang Wang
Journal of Confidentiality and Privacy. [pdf]
- Higher-Order Total Variation Classes on Grids:
Minimax Theory and Trend Filtering Methods
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack and Ryan Tibshirani.
NIPS 2017. [paper,supp，poster]
- Non-stationary Stochastic Optimization under Lp,q-Variation Measures
Xi Chen, Yining Wang and Yu-Xiang Wang.
Operations Research. [informs_online, arxiv]
- Optimal and Adaptive Off-Policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal and Miro Dudik
ICML 2017. [paper supp slides]
- Attributing Hacks with Survival Trend Filtering
Ziqi Liu, Alex Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng, Jun Zhou
Electronic Journal of Statistics[ paper slides]
"Attributing Hacks" in AISTATS'17 (*Plenary oral presentation)
2016
- Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
Seth Flaxman, Dougal Sutherland, Yu-Xiang Wang, and Yee Whye Teh
In preparation. [arxiv]
- Total Variation Classes Beyond 1d: Minimax Rates, and the
Limitations of Linear Smoothers
Veeranjaneyulu Sadhanala, Yu-Xiang Wang and Ryan Tibshirani.
NIPS 2016, Barcelona. [arxiv]
- On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
Yu-Xiang Wang, Jing Lei and Stephen E. Fienberg
Privacy in Statistical Databases. PSD'2016, Dubrovnik. [springer, arxiv]
- A Minimax Theory for Adaptive Data Analysis
Yu-Xiang Wang, Jing Lei and Stephen E. Fienberg
In preparation. [arxiv]
- Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra and Eric Xing
ICML 2016, New York City. [pdf][supplement]
- DiFacto --- Distributed Factorization Machines (*Best Paper Honorable Mention)
Mu Li, Ziqi Liu, Alex Smola, and Yu-Xiang Wang
WSDM 2016, San Francisco. [pdf]
2015
- Graph Sparsification Approaches for Large-Scale Laplacian Smoothing
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani.
AISTATS 2016, Cadiz, Spain. [pdf, supplementary]
- Graph Connectivity in Noisy Sparse Subspace Clustering
Yining Wang, Yu-Xiang Wang, and Aarti Singh
AISTATS 2016, Cadiz, Spain. [arxiv]
- Differentially Private Subspace Clustering
Yining Wang, Yu-Xiang Wang, Aarti Singh
NIPS 2015, Montreal, Canada. [pdf]
- Fast Differential Private Matrix Factorization
Ziqi Liu, Yu-Xiang Wang, and Alex Smola
RecSys'15, Vienna, Austria. [arxiv, code]
- Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen E. Fienberg and Alex Smola
ICML 2015, Lille, France. [arxiv]
- A Theoretical Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data
Yining Wang, Yu-Xiang Wang, Aarti Singh
IEEE Transaction on Information Theory[ieee,arxiv]
ICML 2015, Lille, France. [conf]
- Who supported Obama in 2012? Ecological inference through distribution regression (*Best Student Paper)
Seth Flaxman, Yu-Xiang Wang, and Alex Smola
KDD 2015, Sydney, Australia. [pdf]
2014
- Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Yu-Xiang Wang, Jing Lei and Stephen E. Fienberg.
Journal of Machine Learning Research (JMLR), 2016. [arxiv]
- Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alex Smola and Ryan Tibshirani
Journal of Machine Learning Research (JMLR), 2016. [jmlr]
AISTATS 2015, San Diego. [conf]
- The Falling Factorial Basis and Its Statistical Applications
Yu-Xiang Wang, Alex Smola and Ryan Tibshirani
ICML 2014, Beijing. [paper, Supplementary, code]
2013
- Provable Subspace Clustering: When LRR meets SSC
Yu-Xiang Wang, Huan Xu and Chenlei Leng
IEEE Transaction on Information Theory.[fullpaper,arxiv]
NIPS 2013. Selected for spotlight presentation. [conf, demo code]
- Noisy Sparse Subspace Clustering
Yu-Xiang Wang and Huan Xu
Journal of Machine Learning Research (2016). [pdf]
ICML 2013, Atlanta [paper, Supplementary, code, Video]
- Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization
Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong and Kim-Chuan Toh
International Journal of Computer Vision (IJCV), 2014. [Springerlink,DemoCode]
- Block-Sparse RPCA for Salient Motion Detection
Zhi Gao, Loong-Fah Cheong and Yu-Xiang Wang
Pattern Analysis and Machine Intelligence (PAMI), 2014. [IEEEXplore]
2012
- Stability of Matrix Factorization for Collaborative Filtering
Yu-Xiang Wang, and Huan Xu
ICML 2012, Edinburgh. [paper, Supplementary, Slides , Video]
2011
- An efficient algorithm for UAV indoor pose
estimation using vanishing geometry
Yu-Xiang Wang
MVA 2011, Nara, Japan [paper, Poster]
Thesis and Dissertations
- PhD Thesis: "New Paradigms and Optimality Guarantees in Statistical Learning and Estimation", Carnegie Mellon University, 2017. [pdf]
- MEng Thesis: "Robust Learning with Low-dimensional Structures: Theory, Algorithms and Applications", National University of Singapore, 2013. [pdf]
- BEng Thesis: "Vision-based Sensory System in the Indoor Application of Unmanned Aerial Vehicle", National University of Singapore,
2011. [pdf]