Beating SGD: Learning SVMs in Sublinear Time. - Elad Hazan; Tomer Koren; Nati Srebro (NIPS, 2011)

Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes - Elad Hazan; Tomer Koren (CoRR, 2011)

Supervised system identification based on local PCA models. - Tomer Koren; Ronen Talmon; Israel Cohen (ICASSP, 2012)

Linear Regression with Limited Observation. - Elad Hazan; Tomer Koren (ICML, 2012)

Open Problem: Fast Stochastic Exp-Concave Optimization. - Tomer Koren (COLT, 2013)

Almost Optimal Exploration in Multi-Armed Bandits. - Zohar Shay Karnin; Tomer Koren; Oren Somekh (ICML (3), 2013)

Distributed Exploration in Multi-Armed Bandits. - Eshcar Hillel; Zohar Shay Karnin; Tomer Koren; Ronny Lempel; Oren Somekh (NIPS, 2013)

Bandits with Switching Costs: T^{2/3} Regret. - Ofer Dekel; Jian Ding; Tomer Koren; Yuval Peres (CoRR, 2013)

Online Learning with Composite Loss Functions. - Ofer Dekel; Jian Ding; Tomer Koren; Yuval Peres (COLT, 2014)

Logistic Regression: Tight Bounds for Stochastic and Online Optimization. - Elad Hazan; Tomer Koren; Kfir Y. Levy (COLT, 2014)

The Blinded Bandit: Learning with Adaptive Feedback. - Ofer Dekel; Elad Hazan; Tomer Koren (NIPS, 2014)

Bandits with switching costs: T2/3 regret. - Ofer Dekel; Jian Ding; Tomer Koren; Yuval Peres (STOC, 2014)

Online Learning with Feedback Graphs: Beyond Bandits. - Noga Alon; Nicolò Cesa-Bianchi; Ofer Dekel; Tomer Koren (COLT, 2015)

Bandit Convex Optimization: ( sqrt{T} ) Regret in One Dimension. - Sébastien Bubeck; Ofer Dekel; Tomer Koren; Yuval Peres (COLT, 2015)

Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff. - Ofer Dekel; Ronen Eldan; Tomer Koren (NIPS, 2015)

Fast Rates for Exp-concave Empirical Risk Minimization. - Tomer Koren; Kfir Y. Levy (NIPS, 2015)

Oracle-Based Robust Optimization via Online Learning. - Aharon Ben-Tal; Elad Hazan; Tomer Koren; Shie Mannor (Oper. Res., 2015)

Online Learning with Low Rank Experts. - Elad Hazan; Tomer Koren; Roi Livni; Yishay Mansour (COLT, 2016)

Online Learning with Feedback Graphs Without the Graphs. - Alon Cohen; Tamir Hazan; Tomer Koren (ICML, 2016)

The Limits of Learning with Missing Data. - Brian Bullins; Elad Hazan; Tomer Koren (NIPS, 2016)

Online Pricing with Strategic and Patient Buyers. - Michal Feldman; Tomer Koren; Roi Livni; Yishay Mansour; Aviv Zohar (NIPS, 2016)

The computational power of optimization in online learning. - Elad Hazan; Tomer Koren (STOC, 2016)

A linear-time algorithm for trust region problems. - Elad Hazan; Tomer Koren (Math. Program., 2016)

Tight Bounds for Bandit Combinatorial Optimization. - Alon Cohen; Tamir Hazan; Tomer Koren (COLT, 2017)

Bandits with Movement Costs and Adaptive Pricing. - Tomer Koren; Roi Livni; Yishay Mansour (COLT, 2017)

Affine-Invariant Online Optimization and the Low-rank Experts Problem. - Tomer Koren; Roi Livni (NIPS, 2017)

Multi-Armed Bandits with Metric Movement Costs. - Tomer Koren; Roi Livni; Yishay Mansour (NIPS, 2017)

A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization. - Vineet Gupta 001; Tomer Koren; Yoram Singer (CoRR, 2017)

Chasing Ghosts: Competing with Stateful Policies. - Uriel Feige; Tomer Koren; Moshe Tennenholtz (SIAM J. Comput., 2017)

Shampoo: Preconditioned Stochastic Tensor Optimization. - Vineet Gupta 001; Tomer Koren; Yoram Singer (ICML, 2018)

Online Linear Quadratic Control. - Alon Cohen; Avinatan Hassidim; Tomer Koren; Nevena Lazic; Yishay Mansour; Kunal Talwar (ICML, 2018)

Better Algorithms for Stochastic Bandits with Adversarial Corruptions. - Anupam Gupta 001; Tomer Koren; Kunal Talwar (COLT, 2019)

Learning Linear-Quadratic Regulators Efficiently with only √T Regret. - Alon Cohen; Tomer Koren; Yishay Mansour (ICML, 2019)

Semi-Cyclic Stochastic Gradient Descent. - Hubert Eichner; Tomer Koren; Brendan McMahan; Nathan Srebro; Kunal Talwar (ICML, 2019)

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. - Ehsan Amid; Manfred K. Warmuth; Rohan Anil; Tomer Koren (NeurIPS, 2019)

Memory Efficient Adaptive Optimization. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Yoram Singer (NeurIPS, 2019)

Memory-Efficient Adaptive Optimization for Large-Scale Learning. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Yoram Singer (CoRR, 2019)

Learning Linear-Quadratic Regulators Efficiently with only $ sqrt{T}$ Regret. - Alon Cohen; Tomer Koren; Yishay Mansour (CoRR, 2019)

Open Problem: Tight Convergence of SGD in Constant Dimension. - Tomer Koren; Shahar Segal (COLT, 2020)

Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently. - Asaf B. Cassel; Alon Cohen; Tomer Koren (ICML, 2020)

Stochastic Optimization with Laggard Data Pipelines. - Naman Agarwal; Rohan Anil; Tomer Koren; Kunal Talwar; Cyril Zhang (NeurIPS, 2020)

Prediction with Corrupted Expert Advice. - Idan Amir; Idan Attias; Tomer Koren; Yishay Mansour; Roi Livni (NeurIPS, 2020)

Bandit Linear Control. - Asaf B. Cassel; Tomer Koren (NeurIPS, 2020)

Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. - Assaf Dauber; Meir Feder; Tomer Koren; Roi Livni (NeurIPS, 2020)

Private stochastic convex optimization: optimal rates in linear time. - Vitaly Feldman; Tomer Koren; Kunal Talwar (STOC, 2020)

Second Order Optimization Made Practical. - Rohan Anil; Vineet Gupta 001; Tomer Koren; Kevin Regan 001; Yoram Singer (CoRR, 2020)

Disentangling Adaptive Gradient Methods from Learning Rates. - Naman Agarwal; Rohan Anil; Elad Hazan; Tomer Koren; Cyril Zhang (CoRR, 2020)

Holdout SGD: Byzantine Tolerant Federated Learning. - Shahar Azulay; Lior Raz; Amir Globerson; Tomer Koren; Yehuda Afek (CoRR, 2020)

SGD Generalizes Better Than GD (And Regularization Doesn't Help). - Idan Amir; Tomer Koren; Roi Livni (COLT, 2021)

Online Markov Decision Processes with Aggregate Bandit Feedback. - Alon Cohen; Haim Kaplan; Tomer Koren; Yishay Mansour (COLT, 2021)

Lazy OCO: Online Convex Optimization on a Switching Budget. - Uri Sherman; Tomer Koren (COLT, 2021)

Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. - Hilal Asi; Vitaly Feldman; Tomer Koren; Kunal Talwar (ICML, 2021)

Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret. - Asaf B. Cassel; Tomer Koren (ICML, 2021)

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions. - Tal Lancewicki; Shahar Segal; Tomer Koren; Yishay Mansour (ICML, 2021)

Adversarial Dueling Bandits. - Aadirupa Saha; Tomer Koren; Yishay Mansour (ICML, 2021)

Dueling Convex Optimization. - Aadirupa Saha; Tomer Koren; Yishay Mansour (ICML, 2021)

Never Go Full Batch (in Stochastic Convex Optimization). - Idan Amir; Yair Carmon; Tomer Koren; Roi Livni (NeurIPS, 2021)

Algorithmic Instabilities of Accelerated Gradient Descent. - Amit Attia; Tomer Koren (NeurIPS, 2021)

Asynchronous Stochastic Optimization Robust to Arbitrary Delays. - Alon Cohen; Amit Daniely; Yoel Drori; Tomer Koren; Mariano Schain (NeurIPS, 2021)

Towards Best-of-All-Worlds Online Learning with Feedback Graphs. - Liad Erez; Tomer Koren (NeurIPS, 2021)

Optimal Rates for Random Order Online Optimization. - Uri Sherman; Tomer Koren; Yishay Mansour (NeurIPS, 2021)

The Instability of Accelerated Gradient Descent. - Amit Attia; Tomer Koren (CoRR, 2021)

Multiplicative Reweighting for Robust Neural Network Optimization. - Noga Bar; Tomer Koren; Raja Giryes (CoRR, 2021)

Private Stochastic Convex Optimization: Optimal Rates in 𝓁1 Geometry. - Hilal Asi; Vitaly Feldman; Tomer Koren; Kunal Talwar (CoRR, 2021)

Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs. - Liad Erez; Tomer Koren (CoRR, 2021)

Uniform Stability for First-Order Empirical Risk Minimization. - Amit Attia; Tomer Koren (COLT, 2022)

Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics. - Asaf B. Cassel; Alon Cohen; Tomer Koren (COLT, 2022)

Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond. - Matan Schliserman; Tomer Koren (COLT, 2022)

Better Best of Both Worlds Bounds for Bandits with Switching Costs. - Idan Amir; Guy Azov; Tomer Koren; Roi Livni (NeurIPS, 2022)

Rate-Optimal Online Convex Optimization in Adaptive Linear Control. - Asaf B. Cassel; Alon Peled-Cohen; Tomer Koren (NeurIPS, 2022)

Benign Underfitting of Stochastic Gradient Descent. - Tomer Koren; Roi Livni; Yishay Mansour; Uri Sherman (NeurIPS, 2022)

Dueling Convex Optimization with General Preferences. - Aadirupa Saha; Tomer Koren; Yishay Mansour (CoRR, 2022)

Private Online Prediction from Experts: Separations and Faster Rates. - Hilal Asi; Vitaly Feldman; Tomer Koren; Kunal Talwar (COLT, 2023)

Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. - Hilal Asi; Vitaly Feldman; Tomer Koren; Kunal Talwar (ICML, 2023)

SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. - Amit Attia; Tomer Koren (ICML, 2023)

Regret Minimization and Convergence to Equilibria in General-sum Markov Games. - Liad Erez; Tal Lancewicki; Uri Sherman; Tomer Koren; Yishay Mansour (ICML, 2023)

Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. - Uri Sherman; Tomer Koren; Yishay Mansour (ICML, 2023)

Tight Risk Bounds for Gradient Descent on Separable Data. - Matan Schliserman; Tomer Koren (NeurIPS, 2023)

Faster Convergence with MultiWay Preferences. - Aadirupa Saha; Vitaly Feldman; Yishay Mansour; Tomer Koren (AISTATS, 2024)

The Real Price of Bandit Information in Multiclass Classification. - Liad Erez; Alon Cohen; Tomer Koren; Yishay Mansour; Shay Moran (COLT, 2024)

How Free is Parameter-Free Stochastic Optimization? - Amit Attia; Tomer Koren (ICML, 2024)

Rate-Optimal Policy Optimization for Linear Markov Decision Processes. - Uri Sherman; Alon Cohen; Tomer Koren; Yishay Mansour (ICML, 2024)

Private Online Learning via Lazy Algorithms. - Hilal Asi; Tomer Koren; Daogao Liu; Kunal Talwar (NeurIPS, 2024)

Fast Rates for Bandit PAC Multiclass Classification. - Liad Erez; Alon Peled-Cohen; Tomer Koren; Yishay Mansour; Shay Moran (NeurIPS, 2024)

A Note on High-Probability Analysis of Algorithms with Exponential, Sub-Gaussian, and General Light Tails. - Amit Attia; Tomer Koren (CoRR, 2024)

Faster Stochastic Optimization with Arbitrary Delays via Asynchronous Mini-Batching. - Amit Attia; Ofir Gaash; Tomer Koren (CoRR, 2024)

Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization. - Matan Schliserman; Tomer Koren (CoRR, 2024)

Locally Optimal Descent for Dynamic Stepsize Scheduling. - Gilad Yehudai; Alon Cohen; Amit Daniely; Yoel Drori; Tomer Koren; Mariano Schain (AISTATS, 2025)

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. - Matan Schliserman; Uri Sherman; Tomer Koren (ALT, 2025)

Bandit Multiclass List Classification. - Liad Erez; Tomer Koren (CoRR, 2025)

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation. - Uri Sherman; Tomer Koren; Yishay Mansour (CoRR, 2025)

Benefits of Learning Rate Annealing for Tuning-Robustness in Stochastic Optimization. - Amit Attia; Tomer Koren (CoRR, 2025)

Better Rates for Random Task Orderings in Continual Linear Models. - Itay Evron; Ran Levinstein; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Nathan Srebro (CoRR, 2025)

Nearly Optimal Sample Complexity for Learning with Label Proportions. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Haim Kaplan; Tomer Koren; Uri Stemmer (CoRR, 2025)

Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization. - Shira Vansover-Hager; Tomer Koren; Roi Livni (CoRR, 2025)

Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification. - Matan Schliserman; Tomer Koren (CoRR, 2025)

Optimal Rates in Continual Linear Regression via Increasing Regularization. - Ran Levinstein; Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Itay Evron (CoRR, 2025)

Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning. - Uri Sherman; Tomer Koren; Yishay Mansour (CoRR, 2025)

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime. - Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren (CoRR, 2025)