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Semantic ScholarCsaba Szepesvári
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- affiliation: University of Alberta
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2010 – today
- 2018
[j39]Chandrashekar Lakshminarayanan, Shalabh Bhatnagar, Csaba Szepesvári:
A Linearly Relaxed Approximate Linear Program for Markov Decision Processes. IEEE Trans. Automat. Contr. 63(4): 1185-1191 (2018)- [c125]Chandrashekar Lakshminarayanan, Csaba Szepesvári:
Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go? AISTATS 2018: 1347-1355 - [c124]Karim T. Abou-Moustafa, Csaba Szepesvári:
An Exponential Tail Bound for Lq Stable Learning Rules. Application to k-Folds Cross-Validation. ISAIM 2018 - 2017
- [j38]Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. Journal of Machine Learning Research 18: 145:1-145:31 (2017) - [c123]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Stochastic Rank-1 Bandits. AISTATS 2017: 392-401 - [c122]Tor Lattimore, Csaba Szepesvári:
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. AISTATS 2017: 728-737 - [c121]Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Unsupervised Sequential Sensor Acquisition. AISTATS 2017: 803-811 - [c120]Ruitong Huang, Mohammad M. Ajallooeian, Csaba Szepesvári, Martin Müller:
Structured Best Arm Identification with Fixed Confidence. ALT 2017: 593-616 - [c119]Pooria Joulani, András György, Csaba Szepesvári:
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. ALT 2017: 681-720 - [c118]Masrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
Online Learning to Rank in Stochastic Click Models. ICML 2017: 4199-4208 - [c117]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Bernoulli Rank-1 Bandits for Click Feedback. IJCAI 2017: 2001-2007 - [c116]Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvári:
Multi-view Matrix Factorization for Linear Dynamical System Estimation. NIPS 2017: 7095-7104 - [i48]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) - [i47]Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Tomás Tunys, Zheng Wen, Masrour Zoghi:
Online Learning to Rank in Stochastic Click Models. CoRR abs/1703.02527 (2017) - [i46]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Bernoulli Rank-1 Bandits for Click Feedback. CoRR abs/1703.06513 (2017) - [i45]Chandrashekar Lakshminarayanan, Shalabh Bhatnagar, Csaba Szepesvári:
A Linearly Relaxed Approximate Linear Program for Markov Decision Processes. CoRR abs/1704.02544 (2017) - [i44]Ruitong Huang, Mohammad M. Ajallooeian, Csaba Szepesvári, Martin Müller:
Structured Best Arm Identification with Fixed Confidence. CoRR abs/1706.05198 (2017) - [i43]Karim T. Abou-Moustafa, Csaba Szepesvári:
An a Priori Exponential Tail Bound for k-Folds Cross-Validation. CoRR abs/1706.05801 (2017) - [i42]Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvári:
Crowdsourcing with Sparsely Interacting Workers. CoRR abs/1706.06660 (2017) - [i41]Daniel J. Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári:
Mixing time estimation in reversible Markov chains from a single sample path. CoRR abs/1708.07367 (2017) - [i40]Pooria Joulani, András György, Csaba Szepesvári:
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. CoRR abs/1709.02726 (2017) - [i39]Chandrashekar Lakshminarayanan, Csaba Szepesvári:
Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging. CoRR abs/1709.04073 (2017) - [i38]Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvári, Steffen Grünewälder:
Bandits with Delayed Anonymous Feedback. CoRR abs/1709.06853 (2017) - [i37]Branislav Kveton, Csaba Szepesvári, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, S. Muthukrishnan:
Stochastic Low-Rank Bandits. CoRR abs/1712.04644 (2017) - 2016
- [j37]Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
Regularized Policy Iteration with Nonparametric Function Spaces. Journal of Machine Learning Research 17: 139:1-139:66 (2016) - [c115]Pooria Joulani, András György, Csaba Szepesvári:
Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms. AAAI 2016: 1744-1750 - [c114]Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvári:
Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. AAAI 2016: 1779-1787 - [c113]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS 2016: 819-828 - [c112]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
DCM Bandits: Learning to Rank with Multiple Clicks. ICML 2016: 1215-1224 - [c111]Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári:
Conservative Bandits. ICML 2016: 1254-1262 - [c110]Prashanth L. A., Cheng Jie, Michael C. Fu, Steven I. Marcus, Csaba Szepesvári:
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control. ICML 2016: 1406-1415 - [c109]András György, Csaba Szepesvári:
Shifting Regret, Mirror Descent, and Matrices. ICML 2016: 2943-2951 - [c108]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 - [c107]Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu:
SDP Relaxation with Randomized Rounding for Energy Disaggregation. NIPS 2016: 4979-4987 - [i36]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
DCM Bandits: Learning to Rank with Multiple Clicks. CoRR abs/1602.03146 (2016) - [i35]Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári:
Conservative Bandits. CoRR abs/1602.04282 (2016) - [i34]Bernardo Ávila Pires, Csaba Szepesvári:
Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models. CoRR abs/1602.06346 (2016) - [i33]Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen:
Stochastic Rank-1 Bandits. CoRR abs/1608.03023 (2016) - [i32]Gábor Balázs, András György, Csaba Szepesvári:
Chaining Bounds for Empirical Risk Minimization. CoRR abs/1609.01872 (2016) - [i31]Gábor Balázs, András György, Csaba Szepesvári:
Max-affine estimators for convex stochastic programming. CoRR abs/1609.06331 (2016) - [i30]Bernardo Ávila Pires, Csaba Szepesvári:
Multiclass Classification Calibration Functions. CoRR abs/1609.06385 (2016) - [i29]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR abs/1609.07087 (2016) - [i28]Tor Lattimore, Csaba Szepesvári:
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. CoRR abs/1610.04491 (2016) - [i27]Manjesh Kumar Hanawal, Csaba Szepesvári, Venkatesh Saligrama:
Sequential Learning without Feedback. CoRR abs/1610.05394 (2016) - [i26]Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu:
SDP Relaxation with Randomized Rounding for Energy Disaggregation. CoRR abs/1610.09491 (2016) - 2015
- [c106]Nolan Bard, Deon Nicholas, Csaba Szepesvári, Michael Bowling:
Decision-Theoretic Clustering of Strategies. AAAI Workshop: Computer Poker and Imperfect Information 2015 - [c105]Bernardo Ávila Pires, Csaba Szepesvári:
Pathological Effects of Variance on Classification-Based Policy Iteration. AAAI Workshop: Learning for General Competency in Video Games 2015 - [c104]Gábor Balázs, András György, Csaba Szepesvári:
Near-optimal max-affine estimators for convex regression. AISTATS 2015 - [c103]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. AISTATS 2015 - [c102]
- [c101]Roshan Shariff, András György, Csaba Szepesvári:
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM. AISTATS 2015 - [c100]Nolan Bard, Deon Nicholas, Csaba Szepesvári, Michael H. Bowling:
Decision-theoretic Clustering of Strategies. AAMAS 2015: 17-25 - [c99]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Azin Ashkan:
Cascading Bandits: Learning to Rank in the Cascade Model. ICML 2015: 767-776 - [c98]Yifan Wu, András György, Csaba Szepesvári:
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments. ICML 2015: 1283-1291 - [c97]Ruitong Huang, András György, Csaba Szepesvári:
Deterministic Independent Component Analysis. ICML 2015: 2521-2530 - [c96]Pooria Joulani, András György, Csaba Szepesvári:
Fast Cross-Validation for Incremental Learning. IJCAI 2015: 3597-3604 - [c95]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Linear Multi-Resource Allocation with Semi-Bandit Feedback. NIPS 2015: 964-972 - [c94]Yifan Wu, András György, Csaba Szepesvári:
Online Learning with Gaussian Payoffs and Side Observations. NIPS 2015: 1360-1368 - [c93]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Combinatorial Cascading Bandits. NIPS 2015: 1450-1458 - [c92]Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári:
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. NIPS 2015: 1459-1467 - [c91]Yasin Abbasi-Yadkori, Csaba Szepesvári:
Bayesian Optimal Control of Smoothly Parameterized Systems. UAI 2015: 1-11 - [i25]Branislav Kveton, Csaba Szepesvári, Zheng Wen, Azin Ashkan:
Cascading Bandits. CoRR abs/1502.02763 (2015) - [i24]Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári:
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. CoRR abs/1506.02903 (2015) - [i23]Pooria Joulani, András György, Csaba Szepesvári:
Fast Cross-Validation for Incremental Learning. CoRR abs/1507.00066 (2015) - [i22]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Combinatorial Cascading Bandits. CoRR abs/1507.04208 (2015) - [i21]Yifan Wu, András György, Csaba Szepesvári:
Online Learning with Gaussian Payoffs and Side Observations. CoRR abs/1510.08108 (2015) - [i20]Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvári:
Learning with a Strong Adversary. CoRR abs/1511.03034 (2015) - 2014
- [j36]Gábor Bartók, Dean P. Foster, Dávid Pál, Alexander Rakhlin, Csaba Szepesvári:
Partial Monitoring - Classification, Regret Bounds, and Algorithms. Math. Oper. Res. 39(4): 967-997 (2014) - [j35]Gergely Neu, András György, Csaba Szepesvári, András Antos:
Online Markov Decision Processes Under Bandit Feedback. IEEE Trans. Automat. Contr. 59(3): 676-691 (2014) - [j34]Jyrki Kivinen, Csaba Szepesvári, Thomas Zeugmann:
Guest Editors' introduction. Theor. Comput. Sci. 519: 1-3 (2014) - [j33]Thanh Le, Csaba Szepesvári, Rong Zheng:
Sequential Learning for Multi-Channel Wireless Network Monitoring With Channel Switching Costs. IEEE Trans. Signal Processing 62(22): 5919-5929 (2014) - [c90]Hengshuai Yao, Csaba Szepesvári, Bernardo Ávila Pires, Xinhua Zhang:
Pseudo-MDPs and factored linear action models. ADPRL 2014: 1-9 - [c89]Ruitong Huang, Csaba Szepesvári:
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models. AISTATS 2014: 402-410 - [c88]Tor Lattimore, András György, Csaba Szepesvári:
On Learning the Optimal Waiting Time. ALT 2014: 200-214 - [c87]Travis Dick, András György, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML 2014: 512-520 - [c86]James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans:
Adaptive Monte Carlo via Bandit Allocation. ICML 2014: 1944-1952 - [c85]
- [c84]Hengshuai Yao, Csaba Szepesvári, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar:
Universal Option Models. NIPS 2014: 990-998 - [c83]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. UAI 2014: 477-486 - [e3]Maria-Florina Balcan, Vitaly Feldman, Csaba Szepesvári:
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014. JMLR Workshop and Conference Proceedings 35, JMLR.org 2014 [contents] - [i19]James Neufeld, András György, Dale Schuurmans, Csaba Szepesvári:
Adaptive Monte Carlo via Bandit Allocation. CoRR abs/1405.3318 (2014) - [i18]Tor Lattimore, Koby Crammer, Csaba Szepesvári:
Optimal Resource Allocation with Semi-Bandit Feedback. CoRR abs/1406.3840 (2014) - [i17]Yasin Abbasi-Yadkori, Csaba Szepesvári:
Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm. CoRR abs/1406.3926 (2014) - [i16]Lihong Li, Rémi Munos, Csaba Szepesvári:
On Minimax Optimal Offline Policy Evaluation. CoRR abs/1409.3653 (2014) - [i15]Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvári:
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits. CoRR abs/1410.0949 (2014) - 2013
- [j32]Arash Afkanpour, Csaba Szepesvári, Michael Bowling:
Alignment based kernel learning with a continuous set of base kernels. Machine Learning 91(3): 305-324 (2013) - [j31]András Antos, Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Toward a classification of finite partial-monitoring games. Theor. Comput. Sci. 473: 77-99 (2013) - [c82]Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling:
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning. ICML (1) 2013: 374-382 - [c81]Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári:
Characterizing the Representer Theorem. ICML (1) 2013: 570-578 - [c80]Bernardo Ávila Pires, Csaba Szepesvári, Mohammad Ghavamzadeh:
Cost-sensitive Multiclass Classification Risk Bounds. ICML (3) 2013: 1391-1399 - [c79]Pooria Joulani, András György, Csaba Szepesvári:
Online Learning under Delayed Feedback. ICML (3) 2013: 1453-1461 - [c78]Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári:
Online Learning with Costly Features and Labels. NIPS 2013: 1241-1249 - [c77]Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. NIPS 2013: 2508-2516 - [i14]Yasin Abbasi-Yadkori, Peter L. Bartlett, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions. CoRR abs/1303.3055 (2013) - [i13]Pooria Joulani, András György, Csaba Szepesvári:
Online Learning under Delayed Feedback. CoRR abs/1306.0686 (2013) - 2012
- [j30]Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, Csaba Szepesvári, Olivier Teytaud:
The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM 55(3): 106-113 (2012) - [c76]
- [c75]
- [c74]
- [c73]Yevgeny Seldin, Csaba Szepesvári, Peter Auer, Yasin Abbasi-Yadkori:
Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments. EWRL 2012: 103-116 - [c72]Gábor Bartók, Navid Zolghadr, Csaba Szepesvári:
An adaptive algorithm for finite stochastic partial monitoring. ICML 2012 - [c71]Bernardo Ávila Pires, Csaba Szepesvári:
Statistical linear estimation with penalized estimators: an application to reinforcement learning. ICML 2012 - [c70]
- [c69]Ryan Kiros, Csaba Szepesvári:
Deep Representations and Codes for Image Auto-Annotation. NIPS 2012: 917-925 - [c68]Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári:
Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits. AISTATS 2012: 1-9 - [c67]Gergely Neu, András György, Csaba Szepesvári:
The adversarial stochastic shortest path problem with unknown transition probabilities. AISTATS 2012: 805-813 - [e2]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Proceedings of the Tenth European Workshop on Reinforcement Learning, EWRL 2012, Edinburgh, Scotland, June, 2012. JMLR Proceedings 24, JMLR.org 2012 [contents] - [i12]Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvári:
PAC-Bayesian Policy Evaluation for Reinforcement Learning. CoRR abs/1202.3717 (2012) - [i11]Arash Afkanpour, András György, Csaba Szepesvári, Michael H. Bowling:
A Randomized Strategy for Learning to Combine Many Features. CoRR abs/1205.0288 (2012) - [i10]Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner:
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions. CoRR abs/1206.3233 (2012) - [i9]Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael Bowling:
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. CoRR abs/1206.3285 (2012) - [i8]Yaoliang Yu, Csaba Szepesvári:
Analysis of Kernel Mean Matching under Covariate Shift. CoRR abs/1206.4650 (2012) - [i7]Gergely Neu, Csaba Szepesvári:
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods. CoRR abs/1206.5264 (2012) - 2011
- [j29]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
X-Armed Bandits. Journal of Machine Learning Research 12: 1655-1695 (2011) - [j28]Amir Massoud Farahmand, Csaba Szepesvári:
Model selection in reinforcement learning. Machine Learning 85(3): 299-332 (2011) - [c66]Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
Editors' Introduction. ALT 2011: 1-13 - [c65]
- [c64]Pallavi Arora, Csaba Szepesvári, Rong Zheng:
Sequential learning for optimal monitoring of multi-channel wireless networks. INFOCOM 2011: 1152-1160 - [c63]Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári:
Improved Algorithms for Linear Stochastic Bandits. NIPS 2011: 2312-2320 - [c62]Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvári:
PAC-Bayesian Policy Evaluation for Reinforcement Learning. UAI 2011: 195-202 - [c61]Yasin Abbasi-Yadkori, Csaba Szepesvári:
Regret Bounds for the Adaptive Control of Linear Quadratic Systems. COLT 2011: 1-26 - [c60]Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments. COLT 2011: 133-154 - [c59]István Szita, Csaba Szepesvári:
Agnostic KWIK learning and efficient approximate reinforcement learning. COLT 2011: 739-772 - [e1]Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science 6925, Springer 2011, ISBN 978-3-642-24411-7 [contents] - [i6]András Antos, Gábor Bartók, Dávid Pál, Csaba Szepesvári:
Toward a Classification of Finite Partial-Monitoring Games. CoRR abs/1102.2041 (2011) - [i5]Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári:
Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems. CoRR abs/1102.2670 (2011) - [i4]András Antos, Gábor Bartók, Csaba Szepesvári:
Non-trivial two-armed partial-monitoring games are bandits. CoRR abs/1108.4961 (2011) - [i3]Arash Afkanpour, Csaba Szepesvári,