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2020 – today
- 2024
[c71]Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo:
Online Newton Method for Bandit Convex Optimisation Extended Abstract. COLT 2024: 1713-1714- [i71]Tor Lattimore:
Bandit Convex Optimisation. CoRR abs/2402.06535 (2024) - [i70]Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore, Jack J. Mayo:
Online Newton Method for Bandit Convex Optimisation. CoRR abs/2406.06506 (2024) - 2023
- [j6]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. J. Mach. Learn. Res. 24: 346:1-346:45 (2023) - [c70]Tor Lattimore, András György:
A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT 2023: 2067-2094 - [c69]Tor Lattimore:
A Lower Bound for Linear and Kernel Regression with Adaptive Covariates. COLT 2023: 2095-2113 - [c68]Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML 2023: 691-717 - [c67]Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen:
Leveraging Demonstrations to Improve Online Learning: Quality Matters. ICML 2023: 12527-12545 - [c66]Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári:
Context-lumpable stochastic bandits. NeurIPS 2023 - [c65]Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue:
Probabilistic Inference in Reinforcement Learning Done Right. NeurIPS 2023 - [i69]Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen:
Leveraging Demonstrations to Improve Online Learning: Quality Matters. CoRR abs/2302.03319 (2023) - [i68]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications. CoRR abs/2302.03683 (2023) - [i67]Tor Lattimore, András György:
A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR abs/2302.05371 (2023) - [i66]Xin Zhou, Botao Hao, Jian Kang, Tor Lattimore, Lexin Li:
Sequential Best-Arm Identification with Application to Brain-Computer Interface. CoRR abs/2305.11908 (2023) - [i65]Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári:
Context-lumpable stochastic bandits. CoRR abs/2306.13053 (2023) - [i64]Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue:
Probabilistic Inference in Reinforcement Learning Done Right. CoRR abs/2311.13294 (2023) - 2022
- [c64]Tor Lattimore:
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. COLT 2022: 1547-1575 - [c63]Julian Zimmert, Tor Lattimore:
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. COLT 2022: 3285-3312 - [c62]Botao Hao, Tor Lattimore, Chao Qin:
Contextual Information-Directed Sampling. ICML 2022: 8446-8464 - [c61]Botao Hao, Tor Lattimore:
Regret Bounds for Information-Directed Reinforcement Learning. NeurIPS 2022 - [i63]Tor Lattimore:
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. CoRR abs/2202.10997 (2022) - [i62]Botao Hao, Tor Lattimore, Chao Qin:
Contextual Information-Directed Sampling. CoRR abs/2205.10895 (2022) - [i61]Sanae Amani, Tor Lattimore, András György, Lin F. Yang
:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR abs/2205.13170 (2022) - [i60]Botao Hao, Tor Lattimore:
Regret Bounds for Information-Directed Reinforcement Learning. CoRR abs/2206.04640 (2022) - 2021
- [c60]Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter:
Gated Linear Networks. AAAI 2021: 10015-10023 - [c59]Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. AISTATS 2021: 316-324 - [c58]Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári:
Asymptotically Optimal Information-Directed Sampling. COLT 2021: 2777-2821 - [c57]Tor Lattimore, András György:
Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT 2021: 2938-2964 - [c56]Tor Lattimore, András György:
Mirror Descent and the Information Ratio. COLT 2021: 2965-2992 - [c55]Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. ICML 2021: 4063-4073 - [c54]Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. ICML 2021: 11362-11371 - [c53]Brendan O'Donoghue, Tor Lattimore:
Variational Bayesian Optimistic Sampling. NeurIPS 2021: 12507-12519 - [c52]Botao Hao, Tor Lattimore, Wei Deng:
Information Directed Sampling for Sparse Linear Bandits. NeurIPS 2021: 16738-16750 - [c51]Tor Lattimore, Botao Hao:
Bandit Phase Retrieval. NeurIPS 2021: 18801-18811 - [c50]Brendan O'Donoghue, Tor Lattimore, Ian Osband:
Matrix games with bandit feedback. UAI 2021: 279-289 - [i59]Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade, Shantanu Thakoor, Bilal Piot, Bernardo Ávila Pires, Michal Valko, Thomas Mesnard, Tor Lattimore, Rémi Munos:
Geometric Entropic Exploration. CoRR abs/2101.02055 (2021) - [i58]Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. CoRR abs/2104.02293 (2021) - [i57]Botao Hao, Tor Lattimore, Wei Deng:
Information Directed Sampling for Sparse Linear Bandits. CoRR abs/2105.14267 (2021) - [i56]Tor Lattimore:
Minimax Regret for Bandit Convex Optimisation of Ridge Functions. CoRR abs/2106.00444 (2021) - [i55]Tor Lattimore, Botao Hao:
Bandit Phase Retrieval. CoRR abs/2106.01660 (2021) - [i54]Brendan O'Donoghue, Tor Lattimore:
Variational Bayesian Optimistic Sampling. CoRR abs/2110.15688 (2021) - 2020
- [c49]Botao Hao, Tor Lattimore, Csaba Szepesvári:
Adaptive Exploration in Linear Contextual Bandit. AISTATS 2020: 3536-3545 - [c48]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Information Directed Sampling for Linear Partial Monitoring. COLT 2020: 2328-2369 - [c47]Tor Lattimore, Csaba Szepesvári:
Exploration by Optimisation in Partial Monitoring. COLT 2020: 2488-2515 - [c46]Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt:
Behaviour Suite for Reinforcement Learning. ICLR 2020 - [c45]Tor Lattimore, Csaba Szepesvári, Gellért Weisz:
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. ICML 2020: 5662-5670 - [c44]Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brückner:
Linear bandits with Stochastic Delayed Feedback. ICML 2020: 9712-9721 - [c43]David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness:
Gaussian Gated Linear Networks. NeurIPS 2020 - [c42]Botao Hao, Tor Lattimore, Mengdi Wang:
High-Dimensional Sparse Linear Bandits. NeurIPS 2020 - [c41]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS 2020 - [i53]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Information Directed Sampling for Linear Partial Monitoring. CoRR abs/2002.11182 (2020) - [i52]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. CoRR abs/2003.01704 (2020) - [i51]Tor Lattimore:
Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation. CoRR abs/2006.00475 (2020) - [i50]Brendan O'Donoghue, Tor Lattimore, Ian Osband:
Stochastic matrix games with bandit feedback. CoRR abs/2006.05145 (2020) - [i49]David Budden, Adam H. Marblestone, Eren Sezener, Tor Lattimore, Greg Wayne, Joel Veness:
Gaussian Gated Linear Networks. CoRR abs/2006.05964 (2020) - [i48]Tor Lattimore, András György:
Mirror Descent and the Information Ratio. CoRR abs/2009.12228 (2020) - [i47]Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. CoRR abs/2011.04018 (2020) - [i46]Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. CoRR abs/2011.04019 (2020) - [i45]Botao Hao, Tor Lattimore, Mengdi Wang:
High-Dimensional Sparse Linear Bandits. CoRR abs/2011.04020 (2020) - [i44]Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári:
Asymptotically Optimal Information-Directed Sampling. CoRR abs/2011.05944 (2020)
2010 – 2019
- 2019
- [c40]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. AIES 2019: 383-390 - [c39]Tor Lattimore, Csaba Szepesvári:
Cleaning up the neighborhood: A full classification for adversarial partial monitoring. ALT 2019: 529-556 - [c38]Tor Lattimore, Csaba Szepesvári:
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. COLT 2019: 2111-2139 - [c37]Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. ICML 2019: 3601-3610 - [c36]Shuai Li, Tor Lattimore, Csaba Szepesvári:
Online Learning to Rank with Features. ICML 2019: 3856-3865 - [c35]Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant:
Iterative Budgeted Exponential Search. IJCAI 2019: 1249-1257 - [c34]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS 2019: 4360-4371 - [c33]Julian Zimmert, Tor Lattimore:
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. NeurIPS 2019: 11950-11959 - [c32]Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi:
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. UAI 2019: 196-206 - [c31]Roman Pogodin, Tor Lattimore:
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits. UAI 2019: 894-904 - [i43]Laurent Orseau, Tor Lattimore, Shane Legg:
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. CoRR abs/1901.02230 (2019) - [i42]Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR abs/1901.11530 (2019) - [i41]Tor Lattimore, Csaba Szepesvári:
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. CoRR abs/1902.00470 (2019) - [i40]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. CoRR abs/1902.10730 (2019) - [i39]Roman Pogodin, Tor Lattimore:
Adaptivity, Variance and Separation for Adversarial Bandits. CoRR abs/1903.07890 (2019) - [i38]Julian Zimmert, Tor Lattimore:
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. CoRR abs/1905.11817 (2019) - [i37]Laurent Orseau, Levi H. S. Lelis, Tor Lattimore:
Zooming Cautiously: Linear-Memory Heuristic Search With Node Expansion Guarantees. CoRR abs/1906.03242 (2019) - [i36]Tor Lattimore, Csaba Szepesvári:
Exploration by Optimisation in Partial Monitoring. CoRR abs/1907.05772 (2019) - [i35]Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant:
Iterative Budgeted Exponential Search. CoRR abs/1907.13062 (2019) - [i34]Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt:
Behaviour Suite for Reinforcement Learning. CoRR abs/1908.03568 (2019) - [i33]Joel Veness, Tor Lattimore, Avishkar Bhoopchand, David Budden, Christopher Mattern, Agnieszka Grabska-Barwinska, Peter Toth, Simon Schmitt, Marcus Hutter:
Gated Linear Networks. CoRR abs/1910.01526 (2019) - [i32]Botao Hao, Tor Lattimore, Csaba Szepesvári:
Adaptive Exploration in Linear Contextual Bandit. CoRR abs/1910.06996 (2019) - [i31]Tor Lattimore, Csaba Szepesvári:
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. CoRR abs/1911.07676 (2019) - 2018
- [j5]Tor Lattimore:
Refining the Confidence Level for Optimistic Bandit Strategies. J. Mach. Learn. Res. 19: 20:1-20:32 (2018) - [c30]Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber:
Single-Agent Policy Tree Search With Guarantees. NeurIPS 2018: 3205-3215 - [c29]Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári:
TopRank: A practical algorithm for online stochastic ranking. NeurIPS 2018: 3949-3958 - [i30]Tor Lattimore, Csaba Szepesvári:
Cleaning up the neighborhood: A full classification for adversarial partial monitoring. CoRR abs/1805.09247 (2018) - [i29]Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári:
TopRank: A practical algorithm for online stochastic ranking. CoRR abs/1806.02248 (2018) - [i28]Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, Masrour Zoghi:
BubbleRank: Safe Online Learning to Rerank. CoRR abs/1806.05819 (2018) - [i27]Shuai Li, Tor Lattimore, Csaba Szepesvári:
Online Learning to Rank with Features. CoRR abs/1810.02567 (2018) - [i26]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. CoRR abs/1811.05154 (2018) - [i25]Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber:
Single-Agent Policy Tree Search With Guarantees. CoRR abs/1811.10928 (2018) - 2017
- [j4]Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. J. Mach. Learn. Res. 18: 145:1-145:31 (2017) - [c28]Tor Lattimore, Csaba Szepesvári:
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. AISTATS 2017: 728-737 - [c27]Laurent Orseau, Tor Lattimore, Shane Legg:
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss. ALT 2017: 372-399 - [c26]Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter:
On Thompson Sampling and Asymptotic Optimality. IJCAI 2017: 4889-4893 - [c25]Tor Lattimore:
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis. NIPS 2017: 1584-1593 - [c24]Christoph Dann, Tor Lattimore, Emma Brunskill:
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. NIPS 2017: 5713-5723 - [i24]Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR abs/1702.03040 (2017) - [i23]Christoph Dann, Tor Lattimore, Emma Brunskill:
UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. CoRR abs/1703.07710 (2017) - [i22]Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth:
Online Learning with Gated Linear Networks. CoRR abs/1712.01897 (2017) - 2016
- [c23]Tor Lattimore:
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits. COLT 2016: 1214-1245 - [c22]Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári:
Conservative Bandits. ICML 2016: 1254-1262 - [c21]Aurélien Garivier, Tor Lattimore, Emilie Kaufmann:
On Explore-Then-Commit strategies. NIPS 2016: 784-792 - [c20]Finnian Lattimore, Tor Lattimore, Mark D. Reid:
Causal Bandits: Learning Good Interventions via Causal Inference. NIPS 2016: 1181-1189 - [c19]Sébastien Gerchinovitz, Tor Lattimore:
Refined Lower Bounds for Adversarial Bandits. NIPS 2016: 1190-1198 - [c18]Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. NIPS 2016: 4970-4978 - [c17]Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter:
Thompson Sampling is Asymptotically Optimal in General Environments. UAI 2016 - [i21]Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári:
Conservative Bandits. CoRR abs/1602.04282 (2016) - [i20]Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter:
Thompson Sampling is Asymptotically Optimal in General Environments. CoRR abs/1602.07905 (2016) - [i19]Tor Lattimore:
Regret Analysis of the Anytime Optimally Confident UCB Algorithm. CoRR abs/1603.08661 (2016) - [i18]Sébastien Gerchinovitz, Tor Lattimore:
Refined Lower Bounds for Adversarial Bandits. CoRR abs/1605.07416 (2016) - [i17]Aurélien Garivier, Emilie Kaufmann, Tor Lattimore:
On Explore-Then-Commit Strategies. CoRR abs/1605.08988 (2016) - [i16]Finnian Lattimore, Tor Lattimore, Mark D. Reid:
Causal Bandits: Learning Good Interventions via Causal Inference. CoRR abs/1606.03203 (2016) - [i15]Tom Everitt, Tor Lattimore, Marcus Hutter:
Free Lunch for Optimisation under the Universal Distribution. CoRR abs/1608.04544 (2016) - [i14]Tor Lattimore, Csaba Szepesvári:
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. CoRR abs/1610.04491 (2016) - 2015
- [j3]Tor Lattimore, Marcus Hutter:
On Martin-Löf (non-)convergence of Solomonoff's universal mixture. Theor. Comput. Sci. 588: 2-15 (2015) - [c16]Tor Lattimore:
The Pareto Regret Frontier for Bandits. NIPS 2015: 208-216 - [c15]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Linear Multi-Resource Allocation with Semi-Bandit Feedback. NIPS 2015: 964-972 - [i13]Tor Lattimore:
Optimally Confident UCB : Improved Regret for Finite-Armed Bandits. CoRR abs/1507.07880 (2015) - [i12]Tor Lattimore:
The Pareto Regret Frontier for Bandits. CoRR abs/1511.00048 (2015) - [i11]Tor Lattimore:
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits. CoRR abs/1511.06014 (2015) - 2014
- [j2]Tor Lattimore, Marcus Hutter:
General time consistent discounting. Theor. Comput. Sci. 519: 140-154 (2014) - [j1]Tor Lattimore, Marcus Hutter:
Near-optimal PAC bounds for discounted MDPs. Theor. Comput. Sci. 558: 125-143 (2014) - [c14]Tor Lattimore, Marcus Hutter:
Bayesian Reinforcement Learning with Exploration. ALT 2014: 170-184 - [c13]Tor Lattimore, András György, Csaba Szepesvári:
On Learning the Optimal Waiting Time. ALT 2014: 200-214 - [c12]Tom Everitt, Tor Lattimore, Marcus Hutter:
Free Lunch for optimisation under the universal distribution. IEEE Congress on Evolutionary Computation 2014: 167-174 - [c11]Tor Lattimore, Rémi Munos:
Bounded Regret for Finite-Armed Structured Bandits. NIPS 2014: 550-558 - [c10]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. UAI 2014: 477-486 - [i10]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. CoRR abs/1406.3840 (2014) - [i9]