Publications (scholar page)

 Preprints

• Gaussian Width of Convex Sets via Integral Decompositions, Projections, and the Distribution of Intrinsic Volumes, 2026. [arxiv] (with R. Pathak)

• Ratio Covers of Convex Sets and Optimal Mixture Density Estimation, 2026. [arxiv] (with S. Compton, G. Lugosi, J. Mourtada, J. Qian)

• Consistency and Inconsistency in K-Means Clustering, 2025. [arxiv] (with M. Blanchard, A. Q. Jaffe)

• High-Probability Risk Bounds via Sequential Predictors, 2023. [arxiv] (with N. Cesa-Bianchi, and D. van der Hoeven)

Refereed conference papers (with no journal version)

• Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression, Conference on Learning Theory (COLT), 2026. [arxiv] (with F Chen, J Qian, and A Rakhlin)

• Efficient Logistic Regression with Mixture of Sigmoids, Conference on Artificial Intelligence and Statistics (AISTATS), 2026. [arxiv] (with F. Di Gennaro, S. Chakraborty)

• Lower Bounds for Greedy Teaching Set Constructions, Conference on Learning Theory (COLT), 2025. [arxiv] (with S. Compton, and C. Pabbaraju)

• Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks,  Conference on Learning Theory (COLT), 2025. [arxiv] (with O. Montasser, A. Shetty)

• Dimension-free Private Mean Estimation for Anisotropic Distributions, NeurIPS, 2024. [arxiv] (with Y. Dagan, M. I. Jordan, X. Yang, L. Zakynthinou)

• Derandomizing Multi-Distribution Learning, NeurIPS, 2024. [arxiv] (with K. Green Larsen and O. Montasser)

• Revisiting Agnostic PAC Learning, IEEE Symposium on Foundations of Computer Science (FOCS), 2024. [arxiv] (with K. Green Larsen and S. Hanneke)

• Majority-of-Three: The Simplest Optimal Learner?, Conference on Learning Theory (COLT), 2024. [arxiv] (with I. Aden-Ali, M. Møller Høgsgaard, K. Green Larsen)

• Optimal PAC Bounds Without Uniform Convergence, IEEE Symposium on Foundations of Computer Science (FOCS), 2023. [arxiv] (with I. Aden-Ali, Y. Cherapanamjeri, A. Shetty)

• Local Risk Bounds for Statistical Aggregation, Conference on Learning Theory (COLT), 2023. [arxiv] (with J. Mourtada and T. Vaškevičius)

• Exploring Local Norms in Exp-concave Statistical Learning, Conference on Learning Theory (COLT), 2023. [arxiv] (with Nikita Puchkin)

• The One-Inclusion Graph Algorithm is not Always Optimal, Conference on Learning Theory (COLT), 2023. [arxiv] (with I. Aden-Ali, Y. Cherapanamjeri, A. Shetty)

• A Regret-Variance Trade-Off in Online Learning, NeurIPS 2022. [arxiv] (with N. Cesa-Bianchi, and D. van der Hoeven)

• Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n), NeurIPS (Oral presentation) 2021. [arxiv] (with Y. Klochkov)

• Proper Learning, Helly Number, and an Optimal SVM Bound, Conference on Learning Theory (COLT) (Best Paper Award), 2020. [arxiv] (with O. Bousquet, S. Hanneke and S. Moran)

• Fast Rates for Online Prediction with Abstention, Conference on Learning Theory (COLT), 2020. [arxiv] (with G. Neu)

• Sharper bounds for uniformly stable algorithms, Conference on Learning Theory (COLT), 2020. [arxiv] (with O. Bousquet and Y. Klochkov)

• Optimal learning via local entropies and sample compression. Conference on Learning Theory (COLT), 2017. [arXiv] 

• Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning. Algorithmic Learning Theory (ALT), 2015. [arXiv] (with G. Blanchard and I. Tolstikhin)

Journal publications

• Refined Risk Bounds for Unbounded Losses via Transductive Priors, Journal of Machine Learninig Research, 2026. [arxiv] (with J. Qian, and A. Rakhlin)

• Statistically Optimal Robust Mean and Covariance Estimation for Anisotropic Gaussians, Mathematical Statistics and Learning, 2025. [arxiv] (with Arshak Minasyan)

• Covariance Estimation: Optimal Dimension-free Guarantees for Adversarial Corruption and Heavy Tails, Journal of the European Mathematical Society, 2024. [arxiv] (with P. Abdalla)

• Dimension-free Bounds for Sums of Independent Matrices and Simple Tensors via the Variational Principle, Electronic Journal of Probability, 2023. [arxiv]

• Robustifying Markowitz, Journal of Econometrics, 2022. [arxiv] (with W. Härdle, Y. Klochkov and A. Petukhina)

• Exponential Savings in Agnostic Active Learning through Abstention, 2022, IEEE Transactions on Information Theory. [arxiv] Preliminary version in Conference on Learning Theory (COLT), 2021. (with N. Puchkin)

• On Mean Estimation for Heteroscedastic Random Variables, 2021. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques [arxiv] (with L. Devroye, S. Lattanzi, and G. Lugosi)

• Distribution-Free Robust Linear Regression, Mathematical Statistics and Learning, 2022. [arxiv] (with J. Mourtada and T. Vaškevičius)

• Suboptimality of Constrained Least Squares and Improvements via Non-Linear Predictors, Bernoulli, 2022. [arxiv] (with T. Vaškevičius)

• Fast classification rates without standard margin assumptions, 2021. Information and Inference: A Journal of the IMA [arxiv] (with O. Bousquet)

• Robust k-means Clustering for Distributions with Two Moments, Annals of Statistics, 2021. [arxiv] (with Y. Klochkov and A. Kroshnin)

• Empirical Variance Minimization with Applications in Variance Reduction and Optimal Control, Bernoulli 2021. [arXiv] (with D. Belomestny, L. Iosipoi and Q. Paris.)

• Noise sensitivity of the top eigenvector of a Wigner matrix, Probability Theory and Related Fields, 2019. [arxiv] (with C. Bordenave and G. Lugosi)

• Uniform Hanson-Wright type concentration inequalities for unbounded entries via the entropy method, Electronic Journal of Probability , 2020. [arXiv] (with Y. Klochkov)

• Robust covariance estimation under L4 − L2 moment equivalence, Annals of Statistics, 2020. [arxiv] (with S. Mendelson)

• Concentration of the spectral norm of Erdős–Rényi random graphs, Bernoulli, 2018. [arXiv] (with G. Lugosi and S. Mendelson)

• When are epsilon-nets small?,  Journal of Computer and System Sciences, 2020. [arXiv]  (with A. Kupavskii)

• Localization of VC Classes: Beyond Local Rademacher Complexities. Theoretical Computer Science (Invited paper, ALT special issue), 2018. [arXiv] Preliminary version in Algorithmic Learning Theory (ALT), 2016. (with S. Hanneke)

Expository notes and surveys

• A remark on Kashin's discrepancy argument and partial coloring in the Komlós conjecture, Portugaliae Mathematica, 2022. [arxiv] (with A. S. Bandeira, A. Maillard)