Publications

For the most updated list: DBLP or Scholar

The full list below was last updated on 4/September/2019

Published and Accepted Journal Publications

2002

1. S. Mannor and R. Meir, “On the Existence of Weak Learners and Application to Boosting”, Machine Learning Journal, 48:233-255, 2002. 

2003

2. S. Mannor and N. Shimkin, “The Empirical Bayes Envelope and Regret Minimization in Stochastic Games”, Mathematics of Operations Research, 28(2):327-345, 2003.  

3. S. Mannor, R. Meir and T. Zhang, “Greedy Algorithms for Classification – Consistency, Convergence Rates, and Adaptivity”, Journal of Machine Learning Research, 4:713-741, 2003.

2004

4. S. Mannor and N. Shimkin, “A Geometric Approach to Multi-Criterion Reinforcement Learning”, Journal of Machine Learning Research, 5:325-360, 2004.

5. Y. Engel, S. Mannor and R. Meir, “The Kernel Recursive Least Squares Algorithm”,  IEEE Trans. on Signal Processing, 52(8):2275-2285, 2004.

6. S. Mannor and J. N. Tsitsiklis, “The Sample Complexity of Exploration in the Multi-Armed Bandit Problem”, Journal of Machine Learning Research, 5:623-648, 2004.

2005

7. P.T. de Boer, D.P. Kroese, S. Mannor, and R.Y. Rubinstein, “A Tutorial on the Cross-Entropy Method”, Annals of Operations Research, 134(1):19-67, 2005. 

8. I. Menache, S. Mannor and N. Shimkin, “Basis Function Adaptation in Temporal Difference Reinforcement Learning”, Annals of Operations Research, 134(1):215-238, 2005.

9. S. Mannor and J. N. Tsitsiklis, “On the Empirical State-Action Frequencies in Markov Decision Processes under General Policies”, Mathematics of Operations Research, 30(3):545-561, 2005.

10. R. Johari, S. Mannor, and J. N. Tsitsiklis, “Efficiency Loss in a Network Resource Allocation Game: The Case of Elastic Supply”, IEEE Trans. on Automatic Control 50(11):1712-1724, 2005.

2006

11. R. Johari, S. Mannor and J. N. Tsitsiklis, “A Contract-Based Model for Directed Network Formation”, Games and Economic Behavior 56(2):201-224, 2006.

12. E. Even-dar, S. Mannor and Y. Mansour, “Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems”, Journal of Machine Learning Research 7(Jun):1079–1105, 2006.

13. P. Cadotte, S. Mannor, H. Michalska, and B. Boulet, “Design of L1-Optimal Controllers with Robustness versus Performance Tradeoff”, IEEE Trans. on Automatic Control 51(5):868-873, 2006.

14. S. S. Tehrani, W. J. Gross and S. Mannor, “Stochastic Decoding of LDPC Codes”, IEEE Communications Letters 10(8)716-718, 2006.

15. G. Theocharous, S. Mannor, N. Shah, P. Ghandi, B. Kveton, S. Siddiqi, and C. Yu, “Machine Learning for Adaptive Power Management”, Intel Technology Journal, November 2006.

2007

16. S. Mannor, D. Simester, P. Sun, and J. N. Tsitsiklis, “Biases and Variance in Value Function Estimates”, Management Science 53(2):308-322, 2007.

17. S. Mannor, J. Shamma, and G. Arslan, “Calibrated Forecasts: Efficiency versus Universality”, Machine Learning Journal, 67(2):77-115, 2007.

18. C. Caramanis and S. Mannor, “An Inequality for Nearly Log-concave Distributions with Applications to Learning”, IEEE Trans. on Information Theory 53(3):1043-1057, 2007.

19. S. Mannor and J. Shamma, “Multi-agent Learning for Engineers”, Artificial Intelligence, 171(7): 417-422, 2007.

20. J. Y. Yu and S. Mannor, “Asymptotics of Efficiency Loss in Competitive Market Mechanisms”, IEEE Journal on Selected Areas in Communications, 25(6):1244-1259, 2007.

2008

21. S. Mannor and N. Shimkin, “Regret Minimization for Repeated Matrix Games with Variable Duration”, Games and Economic Behavior, 63:227–258, 2008.

22. S. S. Tehrani, W. J. Gross and S. Mannor, ”Fully-Parallel Stochastic LDPC Decoders”, IEEE Trans. Signal Processing 56(11): 5692-5703, 2008.

23. G. Lugosi, S. Mannor and G. Stoltz, “Strategies for Prediction under Imperfect Monitoring”, Mathematics of Operations Research 33(3):513-528, August 2008.

2009

24. S. Mannor and J. N. Tsitsiklis, “Approachability in Repeated Games: Computational Aspects and a Stackelberg Variant”, Games and Economic Behavior, 66(1):315-325, 2009.

25. S. Mannor, J. N. Tsitsiklis and J. Yu, “Online Learning with Pathwise Constraint”, Journal of Machine Learning Research, 10:569-590, 2009.

26. X. Huan and S. Mannor, “A Kalman Filter Design Based on the Performance/Robustness Tradeoff”, IEEE Trans. Automatic Control, 54(5):1171-1175, 2009.

27. E. Arcaute, R. Johari and S. Mannor, “Network Formation: Bilateral Contracting and Myopic Dynamics”, IEEE Trans. Automatic Control 54(8):1765-1778, 2009.

28. H. Xu, C. Caramanis and S. Mannor, “Robustness and Regularization of Support Vector Machines”, Journal of Machine Learning Research, 10:1485-1510, 2009.

29. J. Yu, S. Mannor and N. Shimkin, “Markov Decision Processes with Arbitrarily Varying Rewards”, Mathematics of Operations Research, 34(3): 737-757, 2009.

2010

30. E. Delage and S. Mannor, “Percentile Optimization for Markov Decision Processes with Parameter Uncertainty”, Operations Research, 58(1): 203-213, 2010. This paper was a finalist for the Nicholson Award.

31. H. Xu, C. Caramanis and S. Mannor, “Robust Regression and Lasso”, IEEE Trans. on Information Theory, 57(6):3561-3574.

32. S. S. Tehrani, A. Naderi, G. Kamendje, S. Hemati, S. Mannor, and W. Gross, ”Majority-based Tracking Forecast Memories for Stochastic LDPC Decoding”, IEEE Trans. On Signal Processing, 58(9):4883-4896, 2010.

33. S. Sharifi Tehrani and C. Winstead and W. J. Gross, S. Mannor and S. L. Howard and V. C. Gaudet. “Relaxation Dynamics in Stochastic Iterative Decoders”, IEEE Trans. on Signal Processing, 58(11): 5955-5961, 2010. 

34. C. Leroux, S. Hemati, S. Mannor and W. Gross. Stochastic Chase Decoding of Reed-Solomon Codes. 2010. IEEE Communications Letters, 14(9): 863-865, 2010.

35. K. Cushon, C. Leroux, S. Hemati, S. Mannor, and W. Gross, “A Min-Sum Iterative Decoder with Pulse Width Message Encoding”, IEEE Trans. Circuits and Systems-II, 58(11): 893-897, 2010.

36. S. Mannor and G. Stoltz, “A Geometric Proof of Calibration”, Mathematics of Operations Research, 35(4):721-727, 2010.

2011

37. A. Danak and S. Mannor, “Efficient Bidding in Dynamic Grid Markets”, IEEE Trans. on Parallel and Dist. Computing, (9): 1483-1496, 2011.

38. S. Sharifi Tehrani, A. Naderi, G. Kamendje, S. Mannor, and W. J. Gross, “Tracking Forecast Memories for Stochastic Decoding”, Journal of Signal Processing Systems, 63(1):117-126, 2011.

39. A. Naderi, S. Mannor, M. Sawan, and W. Gross, “Delayed Stochastic Decoding of LDPC Codes”, IEEE Trans. On Signal Processing, 59(11): 5617-5626, 2011.

40. A. Danak and S. Mannor, “A Robust Learning Approach to Repeated Auctions with Monitoring and Entry Fees”, IEEE Trans. On Computational Intelligence and AI in Games, 3(4):302-315, 2011.

41. D. Vainsencher, S. Mannor and A. Bruckstein, “The Sample Complexity of Dictionary Learning”, Journal of Machine Learning Research, 12:3251-3281, 2011.

2012

42. H. Xu, C. Caramanis, and S. Mannor, “Sparse Algorithms are not Stable: A No-free-lunch Theorem”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(1):187-193, 2012.

43. H. Xu and S. Mannor, “Robustness and Generalization”, Machine Learning Journal, 86(3):391-423, 2012. 

44. H. Xu, C. Caramanis, and S. Mannor, “A Distributional Interpretation of Robust Optimization”, Mathematics of Operations Research, 37(1):95-110, 2012.

45. H. Xu, C. Caramanis, and S. Mannor, “Optimization under Probabilistic Envelope Constraints”, Operations Research, 60(3): 682-699, 2012.

46. H. Xu and S. Mannor, “Distributionally Robust Markov Decision Processes”, Mathematics of Operations Research, 37(2):288-300, 2012.

47. F. Leduc-Primeau, S. Hemati, S. Mannor, and W. Gross, “Dithered Belief Propagation Decoding”, IEEE Trans. on Communications 60(8):2042-2047, 2012.

48. A. Danak and S. Mannor, “Approximately Optimal Bidding Policies for Repeated First-price Auctions”, Annals of Operations Research. 196(1):189-199, 2012.

2013

49. H. Xu, C. Caramanis and S. Mannor, “Outlier-Robust PCA: The High Dimensional Case”, IEEE Trans. on Information Theory, 59(1): 546-572, 2013.

50. E. Arcaute, K. Dyagilev, R. Johari, and S. Mannor, “Dynamics in tree formation games”, Games and Economic Behavior, 79:1-29, 2013.

51. J. Frank, S. Mannor, J. Pineau, and D. Precup, “Time Series Analysis Using Geometric Template Matching”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 35(3): 740-754, 2013. 

52. G. Sarkis, S. Hemati, S. Mannor and W. Gross, “Stochastic Decoding of LDPC Codes over GF(q)”, IEEE Trans. on Communications, 61(3): 939-950, 2013. 

53. F. Leduc-Primeau, S. Hemati, S. Mannor and W. Gross, “Relaxed Half-Stochastic Belief Propagation”, IEEE Trans. on Communications, 61(5): 1648-1659, 2013.

54. K. Jagannathan, S. Mannor, I. Menache and E. Modiano, “A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels”, Internet Mathematics, 9(2-3): 136-160, 2013.

55. S. Mannor and J. Tsitsiklis, “Algorithmic Aspects of Mean-Variance Optimization in Markov Decision Processes”, European Journal of Operation Research, 231(3): 645-653, 2013.

56. K. Dyagiliv, S. Mannor and E. Yom-Tov, “On Information Propagation in Mobile Call Networks”, Social Network Analysis and Mining, 3(3):521-541 (2013).

57. J. Frank, S. Mannor and D. Precup, “Generating Storylines from Sensor Data”, Pervasive and Mobile Computing, 9(6): 838-847 (2013).

2014

58. K. Cushon, S. Hemati, C. Leroux, S. Mannor and W. Gross, “High-Throughput Energy-Efficient LDPC Decoders Using Differential Binary Message Passing”, IEEE Trans. on Signal Processing 62(3):619-631, 2014.

59. A. Bernstein, S. Mannor and N. Shimkin, “Opportunistic Approachability and Generalized No-regret Problems”, Mathematics of Operations Research, 39(4): 1057-1083, 2014.

60. S. Mannor, V. Perchet and G. Stoltz, “Set-Valued Approachability and Online Learning under Partial Monitoring”, Journal of Machine Learning Research, 15:3247−3295, 2014.

61. S. Mannor, V. Perchet and G. Stoltz, “A Primal Condition for Approachability with Partial Monitoring”, Journal of Dynamics and Games, 1(3):447-469, 2014.

2015

62. A. Bental, T. Koren, E. Hazan, and S. Mannor, “Oracle-Based Robust Optimization via Online Learning”, Operation Research, 63(3): 628-638, 2015,

63. C. Milling, C. Caramanis, S. Mannor and S. Shakottai, “Distinguishing Infections on Different Graph Topologies”, IEEE Trans. on Information Theory, 61(6): 3100-3120, 2015.

64. T. Mann, S. Mannor, and D. Precup, “Approximate Value Iteration with Temporally Extended Actions”, Journal of Artificial Intelligence Research, 53:375-438.

65. M. Harel and S. Mannor, “The Perturbed Variation”, IEEE Trans. on Pattern Analysis and Machine Intelligence 37(10): 2119-2130, 2015.

2016

66. A. Tamar D. Di-Castro, and S. Mannor, “Learning the Variance of the Reward-To-Go”, Journal of Machine Learning Research, 17(1):1−36, 2016.

67. A. M. Farahmand, M. Ghavamzadeh, C. Szepesvari and S. Mannor, “Regularized Policy Iteration for Nonparametric Function Space”, Journal of Machine Learning Research, 17(139):1-66, 2016.

68. S. Hong Lim, H. Xu, and S. Mannor, “Reinforcement Learning in Robust Markov Decision Processes”, Mathematics of Operations Research, 41(4):1323-1533, 2016.

69. S. Mannor, O. Mebel, and H. Xu, “Robust MDPs with k-Rectangular Uncertainty”, Mathematics of Operations Research, 41(4):1484-1509, 2016. 

70. I. Hochberg, G. Feraru, M. Kozdoba, S Mannor, M. Tennenholtz, and Elad Yom-Tov, “Encouraging Physical Activity in Diabetes Patients Through Automatic Personalized Feedback Via Reinforcement Learning Improves Glycemic Control”, Diabetes Care, forthcoming.

71. H. Xu, C. Caramanis and S. Mannor, “Statistical Optimization in High Dimensions”, Operations Research, 64(4):958-979, 2016.

2017

72. E. Meirom, S. Mannor and A. Orda, “Strategic Formation of Heterogeneous Networks”, IEEE JSAC, 35(3): 751-763, 2017.

73. A. Tamar, Y. Chow, M. Ghavamzadeh, and S. Mannor, “Sequential Decision Making with Coherent Risk”, IEEE Trans. Automatic Control, 62(7): 3323-3338, 2017.

74. N. Alon, N. Cesa-Bianchi, C. Gentile, S. Mannor, Y. Mansour and O. Shamir, “Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback”, SIAM Journal on Computing, 46(6), 1785-1826, 2017.

75. N. Segev, M. Harel, S. Mannor, K. Crammer and R. El-yaniv, “Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests”, IEEE T-PAMI 39(9), 1811-1824, 2017. 

76. E. Yom-Tov, Guy Feraru, M. Kozdoba, S. Mannor, M. Tennenholtz and I. Hochberg, “A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients”, JMIR 19(10): e388, 2017.

2018

77. E. Meirom, C. Caramanis, Shie Mannor, Ariel Orda and Sanjay Shakkottai, “Detecting Cascades from Weak Signatures”, IEEE Transactions on Network Science and Engineering 5(4):313-325 (2018).

78. M. Kozdoba and S. Mannor, “Source Estimation in Time Series and the Surprising Resilience of HMMs”, IEEE Transactions on Information Theory, 64(8) 5555-5569 (2018).

79. T. Zahavy, A. Dikopoltsev, O. Cohen, S. Mannor and M. Segev, “Deep Learning Reconstruction of Ultra-Short Pulses”, Optica 5.5 (2018): 666-673.

2019

80. G. Dallal, E. Gilboa, S. Mannor and L. Wehenkel, “Chance-Constrained Outage Scheduling using a Machine Learning Proxy”, IEEE Trans. Power Systems, 34(3):2528-2540.

81. O. Avner and S. Mannor, “Multi-user Communication Networks: A Coordinated Multi-armed Bandit Approach”, IEEE Trans. Networking, forthcoming.

 

Refereed conference papers

2000

1. S. Mannor and R. Meir, “Weak Learners and Improved Convergence Rates in Boosting”, in Advances in Neural Information Processing Systems (NIPS), 2000. 

2001

2. Y. Engel and S. Mannor, “Learning Embedded Maps of Markov Processes”, in the International Conference on Machine Learning (ICML), 2001.

3. S. Mannor and N. Shimkin, “Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments”, in the Conference on Computational Learning Theory (COLT), 2001. 

4. S. Mannor and R. Meir, “Geometric Bounds for Generalization in Boosting”, in the Conference on Computational Learning Theory (COLT), 2001. 

2002

5. S. Mannor and N. Shimkin, “The Steering Approach for Multi-Criteria Reinforcement Learning”, in Neural Information Processing Systems (NIPS), 2002.

6. S. Mannor, R. Meir, and T. Zhang, “The Consistency of Greedy Algorithms for Classification”, in the Conference on Computational Learning Theory (COLT), 2002.

7. E. Even-Dar, S. Mannor, and Y. Mansour, “PAC bounds for Multi-armed Bandit and Markov Decision Processes”, in the Conference on Computational Learning Theory (COLT), 2002.

8. Y. Engel, S. Mannor, and R. Meir, “Sparse Online Greedy Support Vector Regression”, in the European Conference on Machine Learning (ECML), 2002.

9. I. Menache, S. Mannor, and N. Shimkin, “Q-Cut – Dynamic Discovery of Sub-Goals in Reinforcement Learning”, in the European Conference on Machine Learning (ECML), 2002.

2003

10. Y. Engel, S. Mannor, and R. Meir, “Bayes meets Bellman: The Gaussian Process Approach to Temporal Difference Learning”, in the Conference on Machine Learning (ICML), 2003. Winner of the best student paper award.

11. E. Even-Dar, S. Mannor, and Y. Mansour, “Action Elimination and Stopping Conditions for Reinforcement Learning”, in the International Conference on Machine Learning (ICML), 2003. 

12. S. Mannor, R. Rubinstein and Y. Gat, “The Cross Entropy Method for Fast Policy Search”, in the International Conference on Machine Learning (ICML), 2003.

13. S. Mannor and N. Shimkin, “On-line Learning with Imperfect Monitoring”, in the Conference on Computational Learning Theory (COLT), 2003.

14. S. Mannor and J. N. Tsitsiklis, “Lower Bounds on the Sample Complexity of Exploration in the Multi-Armed Bandit Problem”, in the Conference on Computational Learning Theory (COLT), 2003. 

2004

15. S. Mannor, “Reinforcement Learning for Average Reward Zero-Sum Games”, in the Conference on Computational Learning Theory (COLT), 2004.

16. C. Caramanis and S. Mannor, “An Inequality for Nearly Log-Concave Distributions with Applications to Learning”, in the Conference on Computational Learning Theory (COLT), 2004.

17. S. Mannor, D. Simester, P. Sun and J. N. Tsitsiklis, “Bias and Variance in Value Function Estimation”, in the International Conference on Machine Learning (ICML), 2004.

18. S. Mannor, I. Menache, A. Hoze, and U. Klein “Dynamic Abstraction in Reinforcement Learning via Clustering”, in the International Conference on Machine Learning (ICML), 2004.

19. R. Johari, S. Mannor and J. N. Tsitsiklis, “Efficiency Loss in a Network Resource Allocation Game: The Case of Elastic Supply”, in the IEEE Conference on Decision and Control, 2004.

2005

20. F. Li, S. Mannor and A. Lippman, “Probabilistic Optimization for Energy-Efficient Broadcast in All-Wireless Networks”, in the Conference on Information Sciences and Systems (CISS), 2005.

21. S. Mannor, D. Peleg and R. Rubinstein, “The Cross Entropy Method for Classification”, in the International Conference on Machine Learning (ICML), 2005.

22. Y. Engel, S. Mannor and R. Meir, “Reinforcement Learning with Gaussian Processes”, in the International Conference on Machine Learning (ICML), 2005.

2006

23. J.Y. Yu and S. Mannor, “Asymptotics of Efficiency Loss in Competitive Market Mechanisms”, in the Conference on Computer Communications (INFOCOM), 2006. 

24. P. Cadotte, S. Mannor, H. Michalska, and B. Boulet, “Design of L1-Optimal Controllers with Flexible Disturbance Rejection Level”, in the American Control Conference (ACC), 2006.

25. S. Mannor and N. Shimkin, “Online Learning with Variable Stage Duration”, in the Conference on Computational Learning Theory (COLT), 2006.

26. S. Mannor and J. N. Tsitsiklis, “Online Learning with Constraints”, in the Conference on Computational Learning Theory (COLT), 2006.

27. P. Keller, S. Mannor and D. Precup, “Automatic Basis Function Construction for Approximate Dynamic Programming and Reinforcement Learning”, in the International Conference on Machine Learning (ICML), 2006.

28. H. Xu and S. Mannor, “Trade-off of Performance and Robustness in Markov Decision Process”, in Neural Information Processing Systems (NIPS), 2006.

2007

29. G. Lugosi, S. Mannor and G. Stoltz, “Strategies for prediction under imperfect monitoring”, in the Conference on Learning Theory (COLT), 2007.

30. F. Heidari, S. Mannor and L. G. Mason, “Learning-based Load Shared Sequential Routing”, in IFIP/TC6 Networking 2007.

31. B. Kveton, P. Gandhi, G. Theocharous, S. Mannor, B. Rosario, and N. Shah, “Adaptive Timeout Policies for Fast Fine-Grained Power Management”, in the National Conference on Artificial Intelligence (AAAI), 2007.

32. C. Yu, S. Mannor, G. Theocharous, A. Pfeffer, “User Model and Utility Based Power Management”, in the National Conference on Artificial Intelligence (AAAI), 2007.

33. E. Delage and S. Mannor, “Percentile Optimization in Uncertain Markov Decision Processes with Application to Efficient Exploration” in the International Conference on Machine Learning (ICML), 2007.

34. E.  Aracute, E. Dallal, R. Johari, and S. Mannor, “Dynamics and Stability in Network Formation Games with Bilateral Contracts”, in the IEEE Conference on Decision and Control (CDC), 2007.

35. H. Xu and S. Mannor, “A Kalman Filter Design Based on the

Performance/Robustness Tradeoff”, in the Allerton Conference on Communication, Control, and Computing, 2007.

36. B. Châtelain, S. Mannor, F. Gagnon, and D. V. Plant, “Non-Cooperative Design of Translucent Networks”, in IEEE GLOBECOM, 2007.

37. D. Ernst, M. Glavic, G.B. Stan, S. Mannor and L. Wehenkel, “The cross-entropy method for power system combinatorial optimization problems”, in IEEE Powertech, 2007. 

38. E. Arcaute, R. Johari and S. Mannor, “Network Formation: Bilateral Contracting and Myopic Dynamics”, in the Workshop On Internet And Network Economics (WINE), 2007.

2008

39. B. Kveton, J. Y. Yu, G. Theocharous, S. Mannor, “Online Learning with Expert Advice and Finite-Horizon Constraints”, in the National Conference on Artificial Intelligence (AAAI), 2008.

40. C. Caramanis and S. Mannor, “Learning in the Limit with Adversarial Disturbances”, in thet Conference on Computational Learning Theory (COLT), 2008.

41. J. Frank, S. Mannor and D. Precup, “Reinforcement learning in the presence of rare events”, in the International Conference on Machine Learning (ICML), 2008.

42. H. Xu, C. Caramanis and S. Mannor, “Robust dimensionality reduction for high-dimension data”, in the Annual Allerton Conference on Communication, Control, and Computing, 2008.

43. H. Xu, C. Caramanis and S. Mannor, “Sparse algorithms are not stable”, in the Annual Allerton Conference on Communication, Control, and Computing, 2008.

44. E.  Arcaute, R. Johari, and S. Mannor, “Local Dynamics in Network Formation Games”, in the Annual Allerton Conference on Communication, Control, and Computing, 2008.

45. E.  Aracute, R. Johari, and S. Mannor, “Local Two-Stage Myopic Dynamics for Network Formation Games”, in the Workshop on Internet Economics (WINE), 2008.

46. A. Farahmand, M. Ghavamzadeh, C. Szepesvari and S. Mannor, “Regularized Policy Iteration”, in Neural Information Processing Systems (NIPS), 2008.

47. H. Xu, C. Caramanis and S. Mannor, “Robust Regression and Lasso”, in Neural Information Processing Systems (NIPS), 2008.

2009

48. J. Y. Yu and S. Mannor, “Online Learning in Markov Decision Processes with Arbitrarily Changing Rewards and Transitions”, in IEEE GameNets, 2009.

49. A. Danak and S. Mannor, “Bidding Efficiently in Repeated Auctions with Entry and Observation Costs”, in IEEE GameNets, 2009.

50. E. Even-dar, R. Kleinberg, S. Mannor and Y. Mansour, “Online Learning for Global Cost Functions”, in the Conference on Computational Learning Theory (COLT), 2009.

51. J. Y. Yu and S. Mannor, “Piecewise-stationary Bandit Problems with Side Observations”, in the International Conference on Machine Learning (ICML), 2009.

52. F. Leduc-Primeau, S. Hemati, W. Gross and S. Mannor, “A Relaxed Half-Stochastic Iterative Decoder for LDPC Codes”, in IEEE Globecom, 2009. 

53. H. Xu and S. Mannor, “Parametric Regret in Uncertain Markov Decision Processes”, in the IEEE Conference on Decision and Control (CDC), 2009.

54. H. Xu, C. Caramanis and S. Mannor, “Risk Sensitive Robust Support Vector Machines”, in the IEEE Conference on Decision and Control (CDC), 2009.

55. J. Y. Yu and S. Mannor, “Arbitrarily Modulated Markov Decision Processes”, in the IEEE Conference on Decision and Control (CDC), 2009.

2010

56. E. Even-dar, S. Mannor and Y. Mansour, “Learning with Global Cost in Stochastic Environments”, in the Conference on Computational Learning Theory (COLT), 2010.

57. H. Xu, C. Caramanis and S. Mannor, “Principal Component Analysis with Contaminated Data: The High Dimensional Case”, in the Conference on Computational Learning Theory (COLT), 2010.

58. H. Xu and S. Mannor, “Robustness and Generalization”, in the Conference on Computational Learning Theory (COLT), 2010.

59. J. Frank, S. Mannor and D. Precup, “Activity and Gait Recognition with Time-Delay Embeddings”, in the National Conference on Artificial Intelligence (AAAI), 2010.

60. A. Danak and S. Mannor, “Resource Allocation with Supply Adjustment in Distributed Computing Systems”, in the IEEE International Conference on Distributed Computing Systems (ICDCS), 2010.

61. D. Di-Castro and S. Mannor, “Adaptive Bases for Reinforcement Learning”, in the European Conference for Machine Learning (ECML), 2010.

62. H. Xu, C. Caramanis, and S. Mannor, “A Distributional Interpretation of Robust Optimization”, in the Annual Allerton Conference on Communication, Control, and Computing, 2010.

63. G. Sarkis, S. Hemati, S. Mannor and W. Gross, “Relaxed Half-Stochastic Decoding of LDPC Codes over GF(q)”, in the Annual Allerton Conference on Communication, Control, and Computing, 2010.

64. A. Kilzikale and S. Mannor, “Volatility and Efficiency in Markets with Friction”, in the Annual Allerton Conference on Communication, Control, and Computing, 2010.

65. D. Di-Castro and S. Mannor, “Tutor Learning Using Linear Constraints in Approximate Dynamic Programming”, in the Annual Allerton Conference on Communication, Control, and Computing, 2010.

66. H. Xu and S. Mannor, “Distributionally Robust Markov Decision Processes”, in Neural Information Processing Systems (NIPS), 2010.

67. A. Bernstein, S. Mannor and N. Shimkin, “Online Classification with Specificity Constraints”, in Neural Information Processing Systems (NIPS), 2010.

68. D. Di-Castro and S. Mannor, “Adaptive Bases for Q-Learning”, in the IEEE Conference on Decision and Control (CDC), 2010.

69. A. Kilzikale and S. Mannor, “Regulation and Efficiency in Markets with Friction”, in the IEEE Conference on Decision and Control (CDC), 2010.

2011

70. H. Xu and S. Mannor, “Probabilistic Goal Markov Decision Processes”, in the International Joing Conference on Artificial Intelligence (IJCAI), 2011.

71. J.Y. Yu and S. Mannor, “Unimodal Bandits”, in the International Conference on Machine Learning (ICML), 2011.

72. S. Mannor and J. N. Tsitsiklis, “Mean-variance optimization in Markov Decision processes”, in the International Conference on Machine Learning (ICML), 2011.

73. M. Harel and S. Mannor, “Learning from Multiple Outlooks”, in the International Conference on Machine Learning (ICML), 2011.

74. D. Vainsencher, O. Dekel, and S. Mannor, “Bundle Selling by Online Estimation of Valuation Functions”, in the International Conference on Machine Learning (ICML), 2011.

75. S. Mannor, V. Perchet and G. Stoltz, “Robust approachability and regret minimization in games with partial monitoring”, in the Conference on Learning Theory (COLT), 2011.

76. D. Vainsencher, S. Mannor and A. Bruckstein. “The Sample Complexity of Dictionary Learning”, in the Conference on Learning Theory (COLT), 2011.

77. K. Jagannathan, S. Mannor, I. Menache and E. Modiyano, “A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels”, in the Conference on Computer Communications (INFOCOM), 2011.

78. A. Kizilkale and S. Mannor, “Regulation and Double Price Mechanisms in Markets with Friction”, in the IEEE Conference on Decision and Control (CDC), 2011.

79. O. Avner and S. Mannor, “Stochastic Bandits with Pathwise Constraints”, in the IEEE Conference on Decision and Control (CDC), 2011.

80. S. Mannor and O. Shamir. “From Bandits to Experts: On the Value of Side-Observations”, in Neural Information Processing Systems (NIPS), 2011. 

81. L. Bui and R. Johari and S. Mannor, “Committing Bandits”, in Neural Information Processing Systems (NIPS), 2011. 

2012

82. C. Caramanis, S. Mannor and H. Xu, “Statistical Optimization in High Dimensions”, in the Conference on Artificial Intelligence and Statistics (AISTATS), 2012.

83. C. Milling, C. Caramanis, S. Mannor and S. Shakkottai, “Network forensics: random infection vs spreading epidemic”, in the ACM SIGMETRICS, 2012.

84. A. Tamar, D. Di-Castro and S. Mannor, “Policy Gradients with Variance Related Risk Criteria”, in the International Conference on Machine Learning (ICML), 2012.

85. S. Mannor, O. Mebel, and H. Xu, “Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty”, in the International Conference on Machine Learning (ICML), 2012.

86. O. Avner, S. Mannor, and O. Shamir, “Decoupling Exploration and Exploitation in Multi-Armed Bandits”, in the International Conference on Machine Learning (ICML), 2012.

87. A. Kizilkale and S. Mannor, “Duality of Ancillary Services and Intermittent Suppliers”, in the IEEE Conference on Decision and Control (CDC), 2012.

88. A. Kizilkale, S. Mannor and P. E. Caines, “Large Scale Real-time Bidding in the Smart Grid: A Mean Field Framework”, in the IEEE Conference on Decision and Control (CDC), 2012.

89. Y. Haimovich, K. Crammer and S. Mannor, “More Is Better: Large Scale Partially-supervised Sentiment Classification”, in the Asian Conference on Machine Learning, 2012. 

90. M. Harel and S. Mannor, “The Perturbed Variation”, in Neural Information Processing Systems (NIPS), 2012.

2013

91. A. Tamar, D. Di-Castro and S. Mannor, “Temporal Difference Methods for the Variance of the Reward To Go”, in the International Conference on Machine Learning (ICML), 2013.

92. Y. Chen, C. Caramanis and S. Mannor, “Robust Sparse Regression under Adversarial Corruption”, in the International Conference on Machine Learning (ICML), 2013.

93. S. Mannor and V. Perchet, “Approachability, fast and slow”, in the Conference on Learning Theory (COLT), 2013.

94. O. Anava, E. Hazan, S. Mannor and O. Shamir, “Online Learning for Time Series Prediction”, in the Conference on Learning Theory (COLT), 2013.

95. A. Bernstein, S. Mannor and N. Shimkin, “Opportunistic Strategies for Generalized No-Regret Problems”, in the Conference on Learning Theory (COLT), 2013.

96. C. Milling, C. Caramanis, S. Mannor and S. Shakkottai, “Detecting Epidemics Using Highly Noisy Data”, in the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2013.

97. A. Hallak, D. Di-Castro and S. Mannor, “Model Selection in Markovian Processes”, Knowledge Discovery in Databases (KDD), 2013.

98. S. Hong Lim, H. Xu, and S. Mannor, “Reinforcement Learning in Robust Markov Decision Processes”, in Neural Information Processing Systems (NIPS), 2013.

99. J. Feng, H. Xu, S. Mannor, S. Yan, “Online PCA for Contaminated Data”, in Neural Information Processing Systems (NIPS), 2013.

100. D. Vainsencher, S. Mannor and H. Xu, Learning Multiple Models via Regularized Weighting, in Neural Information Processing Systems (NIPS), 2013.

2014

101. T. Mann and S. Mannor, “Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations”, in the International Conference on Machine Learning (ICML), 2014.

102. A. Gopalan, S. Mannor, and Y. Mansour, “Thompson Sampling for Complex Online Problems”, in the International Conference on Machine Learning (ICML), 2014.

103. O. Maillard,  and S. Mannor, “Latent Bandits”, in the International Conference on Machine Learning (ICML), 2014.

104. M. Harel, K. Crammer, R. El-Yaniv, and S. Mannor, “Concept Drift Detection Through Resampling” in the International Conference on Machine Learning (ICML), 2014. 

105. D. Mankewitz, T. Mann, and S. Mannor, “Time-Regularized Interrupting Options”, in the International Conference on Machine Learning (ICML), 2014

106. A. Tamar, S. Mannor and H. Xu, “Scaling Up Robust MDPs by Reinforcement Learning”, ICML 2014.

107. S. Mannor, V. Perchet, and G. Stoltz, “Approachability in unknown games: Online learning meets multi-objective optimization”, in the Conference on Learning Theory (COLT), 2014.

108. E. Meirom, S. Mannor and A. Orda, “Network Formation Games with Heterogeneous Players and the Internet Structure”, in the ACM Conference on Economics and Computation (EC), 2014.

109. O. Avner and S. Mannor, “Concurrent bandits and cognitive radio networks”, in the European Conference for Machine Learning (ECML), 2014.

110. A. Baransi, O. Maillard, and S. Mannor, “Sub-sampling for multi-armed bandits”, in the European Conference for Machine Learning (ECML), 2014.

111. J. Feng, H. Xu, S. Mannor, S. Yan, “Robust Logistic Regression and Classification”, in Neural Information Processing Systems (NIPS), 2014.

112. O. Maillard, T. Mann, and S. Mannor, “How hard is my MDP? The distribution-norm to the rescue”, in Neural Information Processing Systems (NIPS), 2014.

113. A. Fuchs, S. Mannor, U. Weiser and Y. Etsion, “Loop-Aware Memory Prefetching Using Code Block Working Sets”, the IEEE/ACM Annual Symposium on Microarchitecture (MICRO), 2014.

2015

114. A. Tamar, Y. Glassner and S. Mannor, “Optimizing the CVaR via Sampling”, in the National Conference on Artificial Intelligence (AAAI), 2015.

115. E. Meirom, S. Mannor and A. Orda, “Formation Games of Reliable Networks”, in the Conference on Computer Communications (INFOCOM), 2015.

116. C. Miling, C. Caramanis, S. Mannor, and S. Shakkottai, “Local Detection of Infections in Heterogeneous Networks”, in the Conference on Computer Communications (INFOCOM), 2015.

117. F. Schnitzler, J. Y. Yu, and S. Mannor, “Sensor Selection for Crowdsensing Dynamical Systems”, in the Conference on Artificial Intelligence and Statistics (AISTATS), 2015. 

118. E. Meirom, C. Miling, C. Caramanis, S. Mannor, s. Shakkottai and A. Orda “Localized epidemic detection in networks with overwhelming noise”, in the ACM SIGMETRICS, 2015.

119. L. Peled, S. Mannor, U. Weiser, and Y. Etsion, “Semantic Locality and Context-based Prefetching Using Reinforcement Learning”, in the International Symposium on Computer Architecture (ISCA), 2015.

120. A. Gopalan and S. Mannor, “Thompson Sampling for Learning Parameterized Markov Decision Processes”, in the Conference on Learning Theory (COLT), 2015.

121. O Reichman and S. Mannor, “Dynamic Sensing: Better Classification under Acquisition Constraints”, in the International Conference on Machine Learning (ICML), 2015.

122. A. Hallak, T. Mann, F. Schnitzler and S. Mannor, “Off-policy Model-based Learning under Unknown Factored Dynamics”, in the International Conference on Machine Learning (ICML), 2015.  

123. G. Dallal and S. Mannor, “Reinforcement Learning for the Unit Commitment Problem”, in IEEE Powertech, 2015.

124. M. Kozdoba and S. Mannor, “Community Detection via Measure Space Embedding”, in Neural Information Processing Systems (NIPS), 2015.

125. O. Anava, E. Hazan, and S. Mannor, “Online Learning for Adversaries with Memory: Price of Past Mistakes”, in Neural Information Processing Systems (NIPS), 2015.

126. Y. Chow, A. Tamar, S. Mannor and M. Pavone, “Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach”, in Neural Information Processing Systems (NIPS), 2015.

127. Y. Chow, A. Tamar, M. Ghavamzadeh and S. Mannor, “Policy Gradient for Coherent Risk Measure”, in Neural Information Processing Systems (NIPS), 2015.

2016

128. A. Hallak, A. Tamar, R. Munos and S. Mannor, “Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis”, in the National Conference on Artificial Intelligence (AAAI), 2016.

129. O. Avner and S. Mannor, “Multi-user lax communications: a multi-armed bandit approach”, in the Conference on Computer Communications (INFOCOM), 2016.

130. G. Dallal, E. Gilboa and S. Mannor, “Distributed Scenario-Based Optimization for Asset Management in a Hierarchical Decision Making Environment”, IEEE PSCC, 2016.

131. G. Dallal, E. Gilboa and S. Mannor, “Hierarchical Decision Making In Electricity Grid Management”, ICML 2016.

132. N. Ben Zrihen, T. Zahavy, and S. Mannor, “Graying the black box: Understanding DQNs”, ICML 2016.

133. O. Anava and S. Mannor, “Heteroscedastic Sequences: Beyond Gaussianity”, ICML 2016.

134. D. Mankowitz, T. Mann, and S. Mannor, “Adaptive Skills Adaptive Partitions (ASAP)”, NIPS 2016.

2017

135. T. Zahavy, D. Mankowitz, C. Tessler, S. Givony and S. Mannor, “A Deep Hierarchical Approach to Lifelong Learning in Minecraft”, AAAI 2017.

136. V. Abhishek and S. Mannor, “A nonparametric sequential test for online randomized experiments”, WWW, 606-610, 2017.

137. Gal Cohensius, Shie Mannor, Reshef Meir, Eli Meirom and Ariel Orda, “Proxy Voting for Better Outcomes”, AAMAS, 2017.

138. D. Vainsencher, H. Xu, and S. Mannor, “Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization”, COLT, 2017.

139. A. Hallak and S. Mannor, “Consistent On-Line Off-Policy Evaluation”, ICML 2017.

140. N. Baram, O. Anschel, I. Caspi and S. Mannor, “End-to-End Differentiable Adversarial Imitation Learning”, ICML 2017.

141. P. Weng, B. Szorenyi, S. Mannor and R. Busa-Fekete, “Multi-objective Bandits: Optimizing the Generalized Gini Index”, ICML 2017.

142. S. Cohen, B. Szorrenyi and S. Mannor, “Non-Parametric Online AUC Maximization”, ECML/PKDD 2017.

143. N. Levine, T. Zahavy, D. Mankowitz, A. Tamar, and S. Mannor, “Shallow Updates for Deep Reinforcement Learning”, NIPS 2017.

144. N. Levine, K. Crammer, and S. Mannor, “Rotting Bandits”, NIPS 2017.

2018

145. D. Mankowitz, T. Mann, P. Bacon, S. Mannor, and Doina Precup, “Learning Robust Options”, AAAI 2018.

146. G. Dalal, B. Szorenyi, G. Thoppe, and S. Mannor, “Finite Sample Analyses for TD(0) with Function Approximation”, AAAI 2018.

147. T. Zahavy, A. Magnani, A. Krishnan, and S. Mannor, “Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce”, IAAI-2018.

148. G. Dallal, E. Gilboa, S. Mannor and L. Wehenkel, “Unit Commitment using Nearest Neighbor as a Short-Term Proxy”, IEEE PSCC, 2018.

149. A. Cassel, S. Mannor and A. Zeevi, “A General Approach to Multi-Armed Bandits Under Risk Criteria”, COLT 2018

150. Gal Dalal, B. Szorenyi, G. Thoppe and S. Mannor. ”Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning”, COLT 2018.

151. Y. Efroni, G. Dalal, B. Scherrer, S. Mannor, “On the multiple step greedy policy in approximate and online reinforcement learning”, ICML 2018.

152. E. Boccara, D. Mankowitz, T. Mann and S. Mannor, “Soft-Robust Actor-Critic Policy-Gradient”, UAI 2018.

153. T. Zahavy, M. Haroush, N. Merlis, D. J. Mankowitz, and S. Mannor, “Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning”, NeurIPS 2018: 3566-3577

154. Y. Efroni, G. Dalal, B. Scherrer, and S. Mannor, “Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning”, NeurIPS 2018: 5244-5253.

2019

155. C. Tessler, D. J. Mankowitz, and S. Mannor, “Reward Constrained Policy Optimization”, ICLR 2019.

156. Y. Efroni, G. Dalal, B. Scherrer, S. Mannor,” How to Combine Tree-Search Methods in Reinforcement Learning”, AAAI 2019, the Outstanding Paper Award.

157. M. Kozdoba, J. Meracek, T. Tchrakian, and S. Mannor, “On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters”, AAAI 2019.

158. N. Merlis and S. Mannor, “Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem”, COLT 2019.

159. C. Qu, S. Mannor and H. Xu, “Nonlinear Distributional Gradient Temporal-Difference Learning”, ICML 2019.

160. C. Tessler, Y. Efroni and S. Mannor, “Action Robust Reinforcement Learning and Applications in Continuous Control”, ICML 2019.

161. G. Tennenholtz and S. Mannor, “The Natural Language of Actions”, ICML 2019.

162. L. Shani, Y. Efroni and S. Mannor, “Exploration Conscious Reinforcement Learning Revisited”, ICML 2019.

163. E. Boccara, D. Mankowitz, and S. Mannor, “A Bayesian Approach to Robust Reinforcement Learning”, UAI 2019.

 

Patents: (total: 8 patents)

  1. A. Schorr, S. Mannor and S. Fridman, “A Standalone Interactive Toy”, PCT: WO/1999/032203.
  2. Y. Heller, Z. Kedem, M. Lewin, and S. Mannor, “An Apparatus for Continuous Compression of Large Volumes of Data”, PCT: WO/2003/092166 (owned by EMC Corporation).
  3. W. Gross and S. Mannor, “Stochastic Decoding of LDPC Codes”, US Patent No. 8,108,758, published 10/2011.
  4. W. Gross, S. Mannor, and S. Tehrani, “A Method for Implementing Stochastic Equality Nodes”, US Patent No. 8095860, published 1/2012.
  5. G. F. Leduc, W. Gross, and S. Mannor “Decoding of linear codes with parity check matrix”, US Patent No. 8108760, published 1/2012.
  6. W. Gross, S. Hemati, S. Mannor, A. Naderi, and G. F. Leduc,“A Method and System for Decoding”, US Patent No. 8677227, published 3/2014.
  7. W. Gross, G. F. Leduc, S. Hemati, and S. Mannor “Method and System for Decoding”, US Patent No. 8898537, published 11/2014.
  8. D. Precup, J. Frank and S. Mannor, “Method of Identification and Devices Thereof”, US Patent No. 8935195, published 1/2015.
  9. Magnani, T. Zahavy, A. Krishnan and S. Mannor, “Systems, method, and non-transitory computer-readable storage media for multi-modal product classification”, US Patent No. 10282462, Published/: May 7, 2019.

Miscellaneous publications (Workshops with proceedings, invited papers, non-rigorously reviewed conference papers which do not overlap with previous publications)

 

  1. T. Weissman and S. Mannor, “On Universal Compression of Multidimensional Data Arrays Using Self Similar Curves”, in the Allerton Conference on Communication, Control, and Computing, 2000.
  2. Y. Engel and S. Mannor, “On Finding Good State Aggregation Functions”, Reinforcement Learning Workshop at the 18th International Conference on Machine Learning, 2001.
  3. S. Mannor and N. Shimkin, “Generalized Approachability Results for Stochastic Games with a Single Reachable State”, ORP3 (Operations Research peripatetic postdoctoral conference), 2001.
  4. S. Mannor and R. Meir, “On the Consistency of Boosting”, Haifa Winter Workshop on Computer Science and Statistics, 2001.
  5. F. Li, S. Mannor and A. Lippman. “Random Tree Optimization for Energy-Efficient Broadcast in All-Wireless Networks”, the First IEEE Conference on Sensor and Ad-Hoc Communications and Networks, 2004.
  6. Y. Engel, S. Mannor and R. Meir, “Reinforcement Learning with Kernels and Gaussian Processes”, Reinforcement Learning workshop at the 22nd International Conference on Machine Learning, 2005.
  7. S. Sharifi Tehrani, S. Mannor and W. Gross, “An Area-Efficient FPGA-Based Architecture for Fully-Parallel Stochastic LDPC Decoding”, IEEE 2007 Workshop on Signal Processing Systems (SiPS).
  8. S. Sharifi Tehrani, S. Mannor and W. Gross, “Survey of Stochastic Computation on Factor Graphs”, IEEE International Symposia on Multiple-Valued Logic, 2007.
  9. B. Kveton, J. Y. Yu, G. Theocharous and S. Mannor, “A Lazy Approach to Online Learning with Constraints”, in Proceedings of the 10th International Symposium on Artificial Intelligence and Mathematics, January 2008.
  10. Danak and S. Mannor, “Identification in Market-Based Multi-Robot Coordination”, in the IEEE International Conference on Distributed Human-Machine Systems, 2008.
  11. M. Farahmand, M. Ghavamzadeh , C. Szepesvari and S. Mannor, “Regularized Fitted Q-iteration: Application to Bounded Resource Planning”, 8th European Workshop on Reinforcement Learning, 2008.
  12. M. Farahmand, M. Ghavamzadeh , C. Szepesvari and S. Mannor, “Regularized Policy Iteration”, 8th European Workshop on Reinforcement Learning, 2008.
  13. K. Dyagilev, S. Mannor and N. Shimkin, “Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case”, 8th European Workshop on Reinforcement Learning, 2008.
  14. J.Y. Yu, S. Mannor and N. Shimkin, “Markov Decision Processes with Arbitrary Reward Processes”, 8th European Workshop on Reinforcement Learning, 2008.
  15. K. Cushon, W. Gross, and S. Mannor, “Bidirectional Interleavers for LDPC Decoders Using Transmission Gates”, IEEE SiPS, 2009.
  16. Milling, C. Caramanis, S. Mannor, S. Shakkottai, “On identifying the causative network of an epidemic”, invited to the Allerton Conference on Communication, Control, and Computing, 2012.
  17. T. Mann and S. Mannor, “The Advantage of Planning with Options”, the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2013. Best paper award.
  18. Gopalan, S. Mannor and Y. Mansour, “Complex Bandit Problems and Thompson Sampling”, the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2013.
  19. F. Schnitzler, T. Liebig, S. Mannor, K. Morik, “Combining a Gauss-Markov model and Gaussian process for traffic prediction in Dublin city center”, EDBT/ICDT Workshops 2014.
  20. Artikis, M. Weidlich, F. Schnitzler, I. Boutsis, T. Liebig, N. Piatkowski, C. Bockermann, K. Morik, V. Kalogeraki, J. Marecek, A. Gal, S. M., D. Gunopulos, D. Kinane, “Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management”, EDBT/ICDT, 2014.
  21. N. Levin, T. Mann, and S. Mannor, “Actively Learning to Attract Followers on Twitter”, in the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
  22. D. Mankowitz, T. Mann, and S. Mannor, “Bootstrapping Skills”, in the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.
  23. R. Canyasse, G. Dalal, and S. Mannor, “Supervised learning for optimal power flow as a real-time proxy”, ISGT 2017: 1-5.
  24. Y. David, B. Szörényi, M. Ghavamzadeh, S. Mannor, and N. Shimkin, “PAC Bandits with Risk Constraints”, ISAIM 2018.

 

Books and Book Chapters

 

  1. D. de Farias, S. Mannor, D. Precup, and G. Theocharous, “Learning and Planning in Markov Processes — Advances and Challenges: Papers from the 2004 AAAI Workshop”, AAAI press, ISBN 1-57735-209-2.
  2. S. Mannor, “K-armed Bandits”, The Machine Learning Encyclopedia, Springer, 2010, 561-563.
  3. C. Caramanis, S. Mannor and H. Xu, “Robust Optimization and Machine Learning”, in Optimization for Machine Learning, EdsSuvritSra, Sebastian Nowozin and Steve Wright, MIT Press, 2011.
  4. M. Ghamvazadeh, P. Poupart, S. Mannor and N. Vlassis, “Bayesian Reinforcement Learning”, in Reinforcement Learning: State of the art, eds. Marco Wiering and Martijn van Otterlo, Springer, 2012.
  5. S. Mannor, N. Srebro, and R. Williamson, eds. Proceedings of the 25th Annual Conference on Learning Theory June 25-27, 2012, Edinburgh, Scotland. JMLR Workshop and Conference Proceedings, Volume 23.
  6. I. Katakis, F. Schnitzler, T. Liebig, D. Gunopulos, K. Morik, G. L. Andrienko, and S. Mannor, eds. Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning. Lille France, 2015.
  7. M. Ghavamzadeh, S. Mannor, J. Pineau and A. Tamar, “Bayesian Reinforcement Learning: A Survey”, Foundations and Trends in Machine Learning, 8(5-6): 359-483, 2015.
  8. S. Mannor, “k-Armed Bandit”. Encyclopedia of Machine Learning and Data Mining 2017: 687-690.

 

Thesis

  1. Mannor, Reinforcement Learning and Adaptation in Competitive Dynamic Environments, PhD thesis, May 2002, The Technion.

 

Magazine Articles

  1. Ion Muslea, Virginia Dignum, Daniel D. Corkill, Catholijn M. Jonker, Frank Dignum, Silvia Coradeschi, Alessandro Saffiotti, Dan Fu, Jeff Orkin, William Cheetham, Kai Goebel, Piero P. Bonissone, Leen-KiatSoh, Randolph M. Jones, Robert E. Wray III, Matthias Scheutz, Daniela Pucci de Farias, Shie Mannor, Georgios Theocharous, Doina Precup, Bamshad Mobasher, Sarabjot S. Anand, Bettina Berendt, Andreas Hotho, Hans W. Guesgen, Michael T. Rosenstein, Mohammad Ghavamzadeh, “The Workshop Program at the Nineteenth National Conference on Artificial Intelligence”, AI Magazine 26(1): 103-108 (2005)

 

Media Coverage

  1. “Auto-diary turns every action into part of your story”, the New Scientist,https://www.newscientist.com/article/mg21929324-100-auto-diary-turns-every-action-into-part-of-your-story/
  2. “The mind ofa machine”, the New Scientist, February 2016, http://www.sciencedirect.com/science/article/pii/S026240791630361X