Publications (chronological order)
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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)
- Permute-And-Flip: An Optimally Robust and Watermarkable Decoder for LLMs
Xuandong Zhao, Lei Li, Yu-Xiang Wang.
Technical report [arxiv, code]
- 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). [Preprint available soon, 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.
Manuscript. [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.
Manuscript. [arxiv]
- 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]