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Tomer Koren
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- affiliation: Tel Aviv University, Tel Aviv, Israel
- affiliation (former): Google Brain, Mountain View, CA, USA
- affiliation (former): Technion, Haifa, Israel
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2020 – today
- 2024
- [c67]Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren:
Faster Convergence with MultiWay Preferences. AISTATS 2024: 433-441 - [c66]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
The Real Price of Bandit Information in Multiclass Classification. COLT 2024: 1573-1598 - [c65]Amit Attia, Tomer Koren:
How Free is Parameter-Free Stochastic Optimization? ICML 2024 - [c64]Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour:
Rate-Optimal Policy Optimization for Linear Markov Decision Processes. ICML 2024 - [i70]Matan Schliserman, Uri Sherman, Tomer Koren:
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. CoRR abs/2401.12058 (2024) - [i69]Amit Attia, Tomer Koren:
How Free is Parameter-Free Stochastic Optimization? CoRR abs/2402.03126 (2024) - [i68]Amit Attia, Tomer Koren:
A Note on High-Probability Analysis of Algorithms with Exponential, Sub-Gaussian, and General Light Tails. CoRR abs/2403.02873 (2024) - [i67]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
The Real Price of Bandit Information in Multiclass Classification. CoRR abs/2405.10027 (2024) - [i66]Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar:
Private Online Learning via Lazy Algorithms. CoRR abs/2406.03620 (2024) - [i65]Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran:
Fast Rates for Bandit PAC Multiclass Classification. CoRR abs/2406.12406 (2024) - [i64]Amit Attia, Ofir Gaash, Tomer Koren:
Faster Stochastic Optimization with Arbitrary Delays via Asynchronous Mini-Batching. CoRR abs/2408.07503 (2024) - 2023
- [c63]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. COLT 2023: 674-699 - [c62]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. ICML 2023: 1107-1120 - [c61]Amit Attia, Tomer Koren:
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. ICML 2023: 1147-1171 - [c60]Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour:
Regret Minimization and Convergence to Equilibria in General-sum Markov Games. ICML 2023: 9343-9373 - [c59]Uri Sherman, Tomer Koren, Yishay Mansour:
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. ICML 2023: 31117-31150 - [c58]Matan Schliserman, Tomer Koren:
Tight Risk Bounds for Gradient Descent on Separable Data. NeurIPS 2023 - [i63]Uri Sherman, Tomer Koren, Yishay Mansour:
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. CoRR abs/2301.13087 (2023) - [i62]Amit Attia, Tomer Koren:
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. CoRR abs/2302.08783 (2023) - [i61]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. CoRR abs/2302.14154 (2023) - [i60]Matan Schliserman, Tomer Koren:
Tight Risk Bounds for Gradient Descent on Separable Data. CoRR abs/2303.01135 (2023) - [i59]Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour:
Rate-Optimal Policy Optimization for Linear Markov Decision Processes. CoRR abs/2308.14642 (2023) - [i58]Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Locally Optimal Descent for Dynamic Stepsize Scheduling. CoRR abs/2311.13877 (2023) - [i57]Aadirupa Saha, Vitaly Feldman, Tomer Koren, Yishay Mansour:
Faster Convergence with Multiway Preferences. CoRR abs/2312.11788 (2023) - 2022
- [c57]Amit Attia, Tomer Koren:
Uniform Stability for First-Order Empirical Risk Minimization. COLT 2022: 3313-3332 - [c56]Matan Schliserman, Tomer Koren:
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond. COLT 2022: 3380-3394 - [c55]Asaf B. Cassel, Alon Cohen, Tomer Koren:
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics. COLT 2022: 3589-3604 - [c54]Idan Amir, Guy Azov, Tomer Koren, Roi Livni:
Better Best of Both Worlds Bounds for Bandits with Switching Costs. NeurIPS 2022 - [c53]Asaf B. Cassel, Alon Peled-Cohen, Tomer Koren:
Rate-Optimal Online Convex Optimization in Adaptive Linear Control. NeurIPS 2022 - [c52]Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman:
Benign Underfitting of Stochastic Gradient Descent. NeurIPS 2022 - [i56]Tomer Koren, Roi Livni, Yishay Mansour, Uri Sherman:
Benign Underfitting of Stochastic Gradient Descent. CoRR abs/2202.13361 (2022) - [i55]Matan Schliserman, Tomer Koren:
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond. CoRR abs/2202.13441 (2022) - [i54]Asaf B. Cassel, Alon Cohen, Tomer Koren:
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics. CoRR abs/2203.01170 (2022) - [i53]Asaf B. Cassel, Alon Cohen, Tomer Koren:
Rate-Optimal Online Convex Optimization in Adaptive Linear Control. CoRR abs/2206.01426 (2022) - [i52]Idan Amir, Guy Azov, Tomer Koren, Roi Livni:
Better Best of Both Worlds Bounds for Bandits with Switching Costs. CoRR abs/2206.03098 (2022) - [i51]Amit Attia, Tomer Koren:
Uniform Stability for First-Order Empirical Risk Minimization. CoRR abs/2207.08257 (2022) - [i50]Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour:
Regret Minimization and Convergence to Equilibria in General-sum Markov Games. CoRR abs/2207.14211 (2022) - [i49]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Dueling Convex Optimization with General Preferences. CoRR abs/2210.02562 (2022) - [i48]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. CoRR abs/2210.13537 (2022) - 2021
- [c51]Idan Amir, Tomer Koren, Roi Livni:
SGD Generalizes Better Than GD (And Regularization Doesn't Help). COLT 2021: 63-92 - [c50]Alon Cohen, Haim Kaplan, Tomer Koren, Yishay Mansour:
Online Markov Decision Processes with Aggregate Bandit Feedback. COLT 2021: 1301-1329 - [c49]Uri Sherman, Tomer Koren:
Lazy OCO: Online Convex Optimization on a Switching Budget. COLT 2021: 3972-3988 - [c48]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. ICML 2021: 393-403 - [c47]Asaf B. Cassel, Tomer Koren:
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret. ICML 2021: 1304-1313 - [c46]Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour:
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions. ICML 2021: 5969-5978 - [c45]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Adversarial Dueling Bandits. ICML 2021: 9235-9244 - [c44]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Dueling Convex Optimization. ICML 2021: 9245-9254 - [c43]Amit Attia, Tomer Koren:
Algorithmic Instabilities of Accelerated Gradient Descent. NeurIPS 2021: 1204-1214 - [c42]Uri Sherman, Tomer Koren, Yishay Mansour:
Optimal Rates for Random Order Online Optimization. NeurIPS 2021: 2097-2108 - [c41]Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. NeurIPS 2021: 9024-9035 - [c40]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). NeurIPS 2021: 25033-25043 - [c39]Liad Erez, Tomer Koren:
Towards Best-of-All-Worlds Online Learning with Feedback Graphs. NeurIPS 2021: 28511-28521 - [i47]Alon Cohen, Haim Kaplan, Tomer Koren, Yishay Mansour:
Online Markov Decision Processes with Aggregate Bandit Feedback. CoRR abs/2102.00490 (2021) - [i46]Idan Amir, Tomer Koren, Roi Livni:
SGD Generalizes Better Than GD (And Regularization Doesn't Help). CoRR abs/2102.01117 (2021) - [i45]Amit Attia, Tomer Koren:
The Instability of Accelerated Gradient Descent. CoRR abs/2102.02167 (2021) - [i44]Uri Sherman, Tomer Koren:
Lazy OCO: Online Convex Optimization on a Switching Budget. CoRR abs/2102.03803 (2021) - [i43]Noga Bar, Tomer Koren, Raja Giryes:
Multiplicative Reweighting for Robust Neural Network Optimization. CoRR abs/2102.12192 (2021) - [i42]Asaf B. Cassel, Tomer Koren:
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret. CoRR abs/2102.12608 (2021) - [i41]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in 𝓁1 Geometry. CoRR abs/2103.01516 (2021) - [i40]Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour:
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions. CoRR abs/2106.02436 (2021) - [i39]Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:
Asynchronous Stochastic Optimization Robust to Arbitrary Delays. CoRR abs/2106.11879 (2021) - [i38]Uri Sherman, Tomer Koren, Yishay Mansour:
Optimal Rates for Random Order Online Optimization. CoRR abs/2106.15207 (2021) - [i37]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). CoRR abs/2107.00469 (2021) - [i36]Liad Erez, Tomer Koren:
Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs. CoRR abs/2107.09572 (2021) - 2020
- [c38]Tomer Koren, Shahar Segal:
Open Problem: Tight Convergence of SGD in Constant Dimension. COLT 2020: 3847-3851 - [c37]Asaf B. Cassel, Alon Cohen, Tomer Koren:
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently. ICML 2020: 1328-1337 - [c36]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. NeurIPS 2020 - [c35]Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni:
Prediction with Corrupted Expert Advice. NeurIPS 2020 - [c34]Asaf B. Cassel, Tomer Koren:
Bandit Linear Control. NeurIPS 2020 - [c33]Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. NeurIPS 2020 - [c32]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private stochastic convex optimization: optimal rates in linear time. STOC 2020: 439-449 - [i35]Asaf B. Cassel, Alon Cohen, Tomer Koren:
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently. CoRR abs/2002.08095 (2020) - [i34]Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer:
Second Order Optimization Made Practical. CoRR abs/2002.09018 (2020) - [i33]Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour:
Prediction with Corrupted Expert Advice. CoRR abs/2002.10286 (2020) - [i32]Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang:
Disentangling Adaptive Gradient Methods from Learning Rates. CoRR abs/2002.11803 (2020) - [i31]Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni:
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study. CoRR abs/2003.06152 (2020) - [i30]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in Linear Time. CoRR abs/2005.04763 (2020) - [i29]Asaf B. Cassel, Tomer Koren:
Bandit Linear Control. CoRR abs/2007.00759 (2020) - [i28]Shahar Azulay, Lior Raz, Amir Globerson, Tomer Koren, Yehuda Afek:
Holdout SGD: Byzantine Tolerant Federated Learning. CoRR abs/2008.04612 (2020) - [i27]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. CoRR abs/2010.13639 (2020) - [i26]Aadirupa Saha, Tomer Koren, Yishay Mansour:
Adversarial Dueling Bandits. CoRR abs/2010.14563 (2020)
2010 – 2019
- 2019
- [c31]Anupam Gupta, Tomer Koren, Kunal Talwar:
Better Algorithms for Stochastic Bandits with Adversarial Corruptions. COLT 2019: 1562-1578 - [c30]Alon Cohen, Tomer Koren, Yishay Mansour:
Learning Linear-Quadratic Regulators Efficiently with only √T Regret. ICML 2019: 1300-1309 - [c29]Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. ICML 2019: 1764-1773 - [c28]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory Efficient Adaptive Optimization. NeurIPS 2019: 9746-9755 - [c27]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. NeurIPS 2019: 14987-14996 - [i25]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory-Efficient Adaptive Optimization for Large-Scale Learning. CoRR abs/1901.11150 (2019) - [i24]Alon Cohen, Tomer Koren, Yishay Mansour:
Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret. CoRR abs/1902.06223 (2019) - [i23]Anupam Gupta, Tomer Koren, Kunal Talwar:
Better Algorithms for Stochastic Bandits with Adversarial Corruptions. CoRR abs/1902.08647 (2019) - [i22]Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. CoRR abs/1904.10120 (2019) - [i21]Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren:
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. CoRR abs/1906.03361 (2019) - 2018
- [c26]Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar:
Online Linear Quadratic Control. ICML 2018: 1028-1037 - [c25]Vineet Gupta, Tomer Koren, Yoram Singer:
Shampoo: Preconditioned Stochastic Tensor Optimization. ICML 2018: 1837-1845 - [i20]Vineet Gupta, Tomer Koren, Yoram Singer:
Shampoo: Preconditioned Stochastic Tensor Optimization. CoRR abs/1802.09568 (2018) - [i19]Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar:
Online Linear Quadratic Control. CoRR abs/1806.07104 (2018) - 2017
- [j3]Uriel Feige, Tomer Koren, Moshe Tennenholtz:
Chasing Ghosts: Competing with Stateful Policies. SIAM J. Comput. 46(1): 190-223 (2017) - [c24]Alon Cohen, Tamir Hazan, Tomer Koren:
Tight Bounds for Bandit Combinatorial Optimization. COLT 2017: 629-642 - [c23]Tomer Koren, Roi Livni, Yishay Mansour:
Bandits with Movement Costs and Adaptive Pricing. COLT 2017: 1242-1268 - [c22]Tomer Koren, Roi Livni, Yishay Mansour:
Multi-Armed Bandits with Metric Movement Costs. NIPS 2017: 4119-4128 - [c21]Tomer Koren, Roi Livni:
Affine-Invariant Online Optimization and the Low-rank Experts Problem. NIPS 2017: 4747-4755 - [i18]Tomer Koren, Roi Livni, Yishay Mansour:
Bandits with Movement Costs and Adaptive Pricing. CoRR abs/1702.07444 (2017) - [i17]Alon Cohen, Tamir Hazan, Tomer Koren:
Tight Bounds for Bandit Combinatorial Optimization. CoRR abs/1702.07539 (2017) - [i16]Vineet Gupta, Tomer Koren, Yoram Singer:
A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization. CoRR abs/1706.06569 (2017) - [i15]Tomer Koren, Roi Livni, Yishay Mansour:
Multi-Armed Bandits with Metric Movement Costs. CoRR abs/1710.08997 (2017) - 2016
- [j2]Elad Hazan, Tomer Koren:
A linear-time algorithm for trust region problems. Math. Program. 158(1-2): 363-381 (2016) - [c20]Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour:
Online Learning with Low Rank Experts. COLT 2016: 1096-1114 - [c19]Alon Cohen, Tamir Hazan, Tomer Koren:
Online Learning with Feedback Graphs Without the Graphs. ICML 2016: 811-819 - [c18]Brian Bullins, Elad Hazan, Tomer Koren:
The Limits of Learning with Missing Data. NIPS 2016: 3495-3503 - [c17]Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar:
Online Pricing with Strategic and Patient Buyers. NIPS 2016: 3864-3872 - [c16]Elad Hazan, Tomer Koren:
The computational power of optimization in online learning. STOC 2016: 128-141 - [i14]Elad Hazan, Tomer Koren, Roi Livni, Yishay Mansour:
Online Learning with Low Rank Experts. CoRR abs/1603.06352 (2016) - [i13]Alon Cohen, Tamir Hazan, Tomer Koren:
Online Learning with Feedback Graphs Without the Graphs. CoRR abs/1605.07018 (2016) - 2015
- [j1]Aharon Ben-Tal, Elad Hazan, Tomer Koren, Shie Mannor:
Oracle-Based Robust Optimization via Online Learning. Oper. Res. 63(3): 628-638 (2015) - [c15]Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, Tomer Koren:
Online Learning with Feedback Graphs: Beyond Bandits. COLT 2015: 23-35 - [c14]Sébastien Bubeck, Ofer Dekel, Tomer Koren, Yuval Peres:
Bandit Convex Optimization: \(\sqrt{T}\) Regret in One Dimension. COLT 2015: 266-278 - [c13]Tomer Koren, Kfir Y. Levy:
Fast Rates for Exp-concave Empirical Risk Minimization. NIPS 2015: 1477-1485 - [c12]Ofer Dekel, Ronen Eldan, Tomer Koren:
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff. NIPS 2015: 2926-2934 - [i12]Sébastien Bubeck, Ofer Dekel, Tomer Koren, Yuval Peres:
Bandit Convex Optimization: sqrt{T} Regret in One Dimension. CoRR abs/1502.06398 (2015) - [i11]Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, Tomer Koren:
Online Learning with Feedback Graphs: Beyond Bandits. CoRR abs/1502.07617 (2015) - [i10]Elad Hazan, Tomer Koren:
The Computational Power of Optimization in Online Learning. CoRR abs/1504.02089 (2015) - 2014
- [c11]Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. COLT 2014: 197-209 - [c10]Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres:
Online Learning with Composite Loss Functions. COLT 2014: 1214-1231 - [c9]Uriel Feige, Tomer Koren, Moshe Tennenholtz:
Chasing Ghosts: Competing with Stateful Policies. FOCS 2014: 100-109 - [c8]Ofer Dekel, Elad Hazan, Tomer Koren:
The Blinded Bandit: Learning with Adaptive Feedback. NIPS 2014: 1610-1618 - [c7]Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres:
Bandits with switching costs: T2/3 regret. STOC 2014: 459-467 - [i9]Elad Hazan, Tomer Koren:
A Linear-Time Algorithm for Trust Region Problems. CoRR abs/1401.6757 (2014) - [i8]Aharon Ben-Tal, Elad Hazan, Tomer Koren, Shie Mannor:
Oracle-Based Robust Optimization via Online Learning. CoRR abs/1402.6361 (2014) - [i7]Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. CoRR abs/1405.3843 (2014) - [i6]Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres:
Online Learning with Composite Loss Functions. CoRR abs/1405.4471 (2014) - [i5]Uriel Feige, Tomer Koren, Moshe Tennenholtz:
Chasing Ghosts: Competing with Stateful Policies. CoRR abs/1407.7635 (2014) - 2013
- [c6]Tomer Koren:
Open Problem: Fast Stochastic Exp-Concave Optimization. COLT 2013: 1073-1075 - [c5]Zohar Shay Karnin, Tomer Koren, Oren Somekh:
Almost Optimal Exploration in Multi-Armed Bandits. ICML (3) 2013: 1238-1246 - [c4]Eshcar Hillel, Zohar Shay Karnin, Tomer Koren, Ronny Lempel, Oren Somekh:
Distributed Exploration in Multi-Armed Bandits. NIPS 2013: 854-862 - [i4]Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres:
Bandits with Switching Costs: T^{2/3} Regret. CoRR abs/1310.2997 (2013) - [i3]Eshcar Hillel, Zohar Shay Karnin, Tomer Koren, Ronny Lempel, Oren Somekh:
Distributed Exploration in Multi-Armed Bandits. CoRR abs/1311.0800 (2013) - 2012
- [c3]Tomer Koren, Ronen Talmon, Israel Cohen:
Supervised system identification based on local PCA models. ICASSP 2012: 541-544 - [c2]Elad Hazan, Tomer Koren:
Linear Regression with Limited Observation. ICML 2012 - [i2]Elad Hazan, Tomer Koren:
Linear Regression with Limited Observation. CoRR abs/1206.4678 (2012) - 2011
- [c1]Elad Hazan, Tomer Koren, Nati Srebro:
Beating SGD: Learning SVMs in Sublinear Time. NIPS 2011: 1233-1241 - [i1]Elad Hazan, Tomer Koren:
Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes. CoRR abs/1108.4559 (2011)
Coauthor Index
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