Nikita Zhivotovskiy

16 papers · 2020–2026 · 3 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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🌉 Interdisciplinary Bridge · Silver 🌍 Conference Polyglot · Bronze 🏃 Academic Marathon · Sprint 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (29)

🔬 Deep Specialist (14) 💎 Century Club · Bronze ❓ The Questioner 🗃️ Keyword Collector (78) 🔥 Unstoppable · Bronze

Venues

COLT (11) NeurIPS (4) JMLR (1)

Top co-authors

Olivier Bousquet (2) Nikita Puchkin (2) Kasper Green Larsen (2) Yegor Klochkov (2) Ishaq Aden-Ali (2) Alexander Rakhlin (2) Omar Montasser (2) Jian Qian (2) Abhishek Shetty (2) Yuval Dagan (1)

Research topics

Machine Learning (16) Optimization & Theory (15) Mathematics & Optimization (6) Core Methods (5) Learning Types (5) Optimization (4) Artificial Intelligence (3) Application Areas (2) Mathematics (2) Computer Science (1) Bayesian & Probabilistic (1) Core AI (1) Foundations (1) Learning Paradigms (1)

Keywords

vc dimension (5) regret bound (4) pac learning (4) sample complexity (4) empirical risk minimization (3) strongly convex (2) learning theory (2) online learning (2) uniform stability (2) exponential weight (2) expert advice (2) excess risk (2) risk bound (2) concentration inequality (2) robustness (1) mean estimation (1) domain adaptation (1) online prediction (1) transductive learning (1) binary classification (1)

Papers

Refined Risk Bounds for Unbounded Losses via Transductive Priors JMLR 2026 Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression COLT 2026 Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks COLT 2025 Lower Bounds for Greedy Teaching Set Constructions COLT 2025 Derandomizing Multi-Distribution Learning NeurIPS 2024 Dimension-free Private Mean Estimation for Anisotropic Distributions NeurIPS 2024 Majority-of-Three: The Simplest Optimal Learner? COLT 2024 Exploring Local Norms in Exp-concave Statistical Learning COLT 2023 Local Risk Bounds for Statistical Aggregation COLT 2023 The One-Inclusion Graph Algorithm is not Always Optimal COLT 2023 A Regret-Variance Trade-Off in Online Learning NeurIPS 2022 Exponential savings in agnostic active learning through abstention COLT 2021 Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ NeurIPS 2021 Fast Rates for Online Prediction with Abstention COLT 2020 Proper Learning, Helly Number, and an Optimal SVM Bound COLT 2020 Sharper Bounds for Uniformly Stable Algorithms COLT 2020