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2020 – today
- 2023
[i76]Dhawal Gupta, Yinlam Chow, Mohammad Ghavamzadeh, Craig Boutilier:
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. CoRR abs/2302.10850 (2023)- [i75]Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Shixiang Shane Gu:
Aligning Text-to-Image Models using Human Feedback. CoRR abs/2302.12192 (2023) - [i74]Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi, Farhad Pourpanah, Daniel McDuff, Mohammad Ghavamzadeh, Shuicheng Yan, Abduallah Mohamed, Abbas Khosravi, Erik Cambria, Fatih Porikli:
A Review of Deep Learning for Video Captioning. CoRR abs/2304.11431 (2023) - [i73]Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik:
On Dynamic Program Decompositions of Static Risk Measures. CoRR abs/2304.12477 (2023) - [i72]Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andres Muñoz Medina, Umar Syed:
Private and Communication-Efficient Algorithms for Entropy Estimation. CoRR abs/2305.07751 (2023) - 2022
- [c95]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier:
Thompson Sampling with a Mixture Prior. AISTATS 2022: 7565-7586 - [c94]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh:
Hierarchical Bayesian Bandits. AISTATS 2022: 7724-7741 - [c93]Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh:
Collaborative Multi-agent Stochastic Linear Bandits. ACC 2022: 2761-2766 - [c92]Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh:
Mirror Descent Policy Optimization. ICLR 2022 - [c91]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Deep Hierarchy in Bandits. ICML 2022: 8833-8851 - [c90]Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh:
Feature and Parameter Selection in Stochastic Linear Bandits. ICML 2022: 15927-15958 - [c89]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh:
Fixed-Budget Best-Arm Identification in Structured Bandits. IJCAI 2022: 2798-2804 - [c88]Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis, Mahnoosh Alizadeh:
Multi-Environment Meta-Learning in Stochastic Linear Bandits. ISIT 2022: 1659-1664 - [c87]Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andrés Muñoz Medina, Umar Syed:
Private and Communication-Efficient Algorithms for Entropy Estimation. NeurIPS 2022 - [c86]Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Efficient Risk-Averse Reinforcement Learning. NeurIPS 2022 - [c85]Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh:
Robust Reinforcement Learning using Offline Data. NeurIPS 2022 - [c84]Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Operator Splitting Value Iteration. NeurIPS 2022 - [i71]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Deep Hierarchy in Bandits. CoRR abs/2202.01454 (2022) - [i70]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh, Sumeet Katariya:
Meta-Learning for Simple Regret Minimization. CoRR abs/2202.12888 (2022) - [i69]Mohammad Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh:
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. CoRR abs/2202.13001 (2022) - [i68]Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Efficient Risk-Averse Reinforcement Learning. CoRR abs/2205.05138 (2022) - [i67]Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis, Mahnoosh Alizadeh:
Multi-Environment Meta-Learning in Stochastic Linear Bandits. CoRR abs/2205.06326 (2022) - [i66]Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh:
Collaborative Multi-agent Stochastic Linear Bandits. CoRR abs/2205.06331 (2022) - [i65]Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier:
A Mixture-of-Expert Approach to RL-based Dialogue Management. CoRR abs/2206.00059 (2022) - [i64]Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh, Bing Liu:
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings. CoRR abs/2207.00468 (2022) - [i63]Kishan Panaganti, Zaiyan Xu
, Dileep M. Kalathil, Mohammad Ghavamzadeh:
Robust Reinforcement Learning using Offline Data. CoRR abs/2208.05129 (2022) - [i62]Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh, Reazul Hasan Russel:
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk. CoRR abs/2209.04067 (2022) - [i61]Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Operator Splitting Value Iteration. CoRR abs/2211.13937 (2022) - [i60]Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh:
Multi-Task Off-Policy Learning from Bandit Feedback. CoRR abs/2212.04720 (2022) - 2021
- [j17]Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul W. Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi:
A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Inf. Fusion 76: 243-297 (2021) - [j16]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Active Learning for Classification With Abstention. IEEE J. Sel. Areas Inf. Theory 2(2): 705-719 (2021) - [c83]Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Animashree Anandkumar, Yisong Yue:
Deep Bayesian Quadrature Policy Optimization. AAAI 2021: 6600-6608 - [c82]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. AISTATS 2021: 2827-2835 - [c81]Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh:
Control-Aware Representations for Model-based Reinforcement Learning. ICLR 2021 - [c80]Amir Massoud Farahmand, Mohammad Ghavamzadeh:
PID Accelerated Value Iteration Algorithm. ICML 2021: 3143-3153 - [c79]Yinlam Chow, Brandon Cui, Moonkyung Ryu, Mohammad Ghavamzadeh:
Variational Model-based Policy Optimization. IJCAI 2021: 2292-2299 - [c78]Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Neural Lyapunov Redesign. L4DC 2021: 459-470 - [c77]Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi:
Adaptive Sampling for Minimax Fair Classification. NeurIPS 2021: 24535-24544 - [i59]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Adaptive Sampling for Minimax Fair Classification. CoRR abs/2103.00755 (2021) - [i58]Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh:
Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm. CoRR abs/2106.04763 (2021) - [i57]Ahmadreza Moradipari, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh:
Parameter and Feature Selection in Stochastic Linear Bandits. CoRR abs/2106.05378 (2021) - [i56]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier:
Thompson Sampling with a Mixture Prior. CoRR abs/2106.05608 (2021) - [i55]Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh:
Hierarchical Bayesian Bandits. CoRR abs/2111.06929 (2021) - 2020
- [c76]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Improved Algorithms for Conservative Exploration in Bandits. AAAI 2020: 3962-3969 - [c75]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. AISTATS 2020: 1431-1441 - [c74]Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier:
Randomized Exploration in Generalized Linear Bandits. AISTATS 2020: 2066-2076 - [c73]Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
Safe Policy Learning for Continuous Control. CoRL 2020: 801-821 - [c72]Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui:
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. ICLR 2020 - [c71]Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh:
Adaptive Sampling for Estimating Probability Distributions. ICML 2020: 8687-8696 - [c70]Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui:
Predictive Coding for Locally-Linear Control. ICML 2020: 8862-8871 - [c69]Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh:
Multi-step Greedy Reinforcement Learning Algorithms. ICML 2020: 9504-9513 - [c68]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Active Learning for Classification with Abstention. ISIT 2020: 2801-2806 - [c67]Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor:
Online Planning with Lookahead Policies. NeurIPS 2020 - [c66]Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Model Estimation in Markov Decision Processes. UAI 2020: 1019-1028 - [i54]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Conservative Exploration in Reinforcement Learning. CoRR abs/2002.03218 (2020) - [i53]Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta:
Improved Algorithms for Conservative Exploration in Bandits. CoRR abs/2002.03221 (2020) - [i52]Romina Abachi, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Policy-Aware Model Learning for Policy Gradient Methods. CoRR abs/2003.00030 (2020) - [i51]Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui:
Predictive Coding for Locally-Linear Control. CoRR abs/2003.01086 (2020) - [i50]Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Model Estimation in Markov Decision Processes. CoRR abs/2003.03297 (2020) - [i49]Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh:
Mirror Descent Policy Optimization. CoRR abs/2005.09814 (2020) - [i48]Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems. CoRR abs/2006.03947 (2020) - [i47]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. CoRR abs/2006.03976 (2020) - [i46]Yinlam Chow, Brandon Cui, Moonkyung Ryu, Mohammad Ghavamzadeh:
Variational Model-based Policy Optimization. CoRR abs/2006.05443 (2020) - [i45]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. CoRR abs/2006.10185 (2020) - [i44]Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh:
Control-Aware Representations for Model-based Reinforcement Learning. CoRR abs/2006.13408 (2020) - [i43]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Finite-Sample Analysis of GTD Algorithms. CoRR abs/2006.14364 (2020) - [i42]Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue:
Deep Bayesian Quadrature Policy Optimization. CoRR abs/2006.15637 (2020) - [i41]Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu:
Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR abs/2009.06548 (2020) - [i40]Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul W. Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi:
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges. CoRR abs/2011.06225 (2020) - [i39]Elita A. Lobo, Mohammad Ghavamzadeh, Marek Petrik:
Soft-Robust Algorithms for Handling Model Misspecification. CoRR abs/2011.14495 (2020) - [i38]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Mohammad Ghavamzadeh, Craig Boutilier:
Non-Stationary Latent Bandits. CoRR abs/2012.00386 (2020)
2010 – 2019
- 2019
- [c65]Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone:
Risk-Sensitive Generative Adversarial Imitation Learning. AISTATS 2019: 2154-2163 - [c64]Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis:
Optimizing over a Restricted Policy Class in MDPs. AISTATS 2019: 3042-3050 - [c63]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 - [c62]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Multi-Armed Bandits. IJCAI 2019: 2786-2793 - [c61]Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor:
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. NeurIPS 2019: 12203-12213 - [c60]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Linear Bandits. UAI 2019: 530-540 - [i37]Yinlam Chow, Ofir Nachum, Aleksandra Faust, Mohammad Ghavamzadeh, Edgar A. Duéñez-Guzmán:
Lyapunov-based Safe Policy Optimization for Continuous Control. CoRR abs/1901.10031 (2019) - [i36]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Multi-Armed Bandits. CoRR abs/1902.10089 (2019) - [i35]Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier:
Perturbed-History Exploration in Stochastic Linear Bandits. CoRR abs/1903.09132 (2019) - [i34]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Binary Classification with Bounded Abstention Rate. CoRR abs/1905.09561 (2019) - [i33]Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor:
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. CoRR abs/1905.11527 (2019) - [i32]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Active Learning for Binary Classification with Abstention. CoRR abs/1906.00303 (2019) - [i31]Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier:
Randomized Exploration in Generalized Linear Bandits. CoRR abs/1906.08947 (2019) - [i30]Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui:
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. CoRR abs/1909.01506 (2019) - [i29]Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor:
Multi-Step Greedy and Approximate Real Time Dynamic Programming. CoRR abs/1909.04236 (2019) - [i28]Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau:
Benchmarking Batch Deep Reinforcement Learning Algorithms. CoRR abs/1910.01708 (2019) - [i27]Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh:
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning. CoRR abs/1910.02919 (2019) - [i26]Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi:
Adaptive Sampling for Estimating Multiple Probability Distributions. CoRR abs/1910.12406 (2019) - 2018
- [j15]Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity. J. Artif. Intell. Res. 63: 461-494 (2018) - [c59]Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi:
Robust Locally-Linear Controllable Embedding. AISTATS 2018: 1751-1759 - [c58]Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. ICML 2018: 978-987 - [c57]Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh:
More Robust Doubly Robust Off-policy Evaluation. ICML 2018: 1446-1455 - [c56]Yahel David, Balázs Szörényi, Mohammad Ghavamzadeh, Shie Mannor, Nahum Shimkin:
PAC Bandits with Risk Constraints. ISAIM 2018 - [c55]Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon:
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. NeurIPS 2018: 1073-1083 - [c54]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. NeurIPS 2018: 8103-8112 - [i25]Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh:
More Robust Doubly Robust Off-policy Evaluation. CoRR abs/1802.03493 (2018) - [i24]Ofir Nachum, Yinlam Chow, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. CoRR abs/1802.03501 (2018) - [i23]Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis:
Optimizing over a Restricted Policy Class in Markov Decision Processes. CoRR abs/1802.09646 (2018) - [i22]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. CoRR abs/1805.07708 (2018) - [i21]Jonathan Lacotte, Yinlam Chow, Mohammad Ghavamzadeh, Marco Pavone:
Risk-Sensitive Generative Adversarial Imitation Learning. CoRR abs/1808.04468 (2018) - [i20]Bo Liu, Tengyang Xie, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon:
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. CoRR abs/1809.02292 (2018) - [i19]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) - 2017
- [j14]Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone:
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria. J. Mach. Learn. Res. 18: 167:1-167:51 (2017) - [j13]Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Sequential Decision Making With Coherent Risk. IEEE Trans. Autom. Control. 62(7): 3323-3338 (2017) - [c53]Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill:
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. AAAI 2017: 4740-4745 - [c52]Ian Gemp, Georgios Theocharous, Mohammad Ghavamzadeh:
Automated Data Cleansing through Meta-Learning. AAAI 2017: 4760-4761 - [c51]Alan Malek, Sumeet Katariya, Yinlam Chow, Mohammad Ghavamzadeh:
Sequential Multiple Hypothesis Testing with Type I Error Control. AISTATS 2017: 1468-1476 - [c50]Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Learning for Accurate Estimation of Linear Models. ICML 2017: 2931-2939 - [c49]Rui Shu, Hung Hai Bui, Mohammad Ghavamzadeh:
Bottleneck Conditional Density Estimation. ICML 2017: 3164-3172 - [c48]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Model-Independent Online Learning for Influence Maximization. ICML 2017: 3530-3539 - [c47]Masrour Zoghi, Tomás Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvári, Zheng Wen:
Online Learning to Rank in Stochastic Click Models. ICML 2017: 4199-4208 - [c46]Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh:
Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display. MLDM 2017: 32-46 - [c45]Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi, Benjamin Van Roy:
Conservative Contextual Linear Bandits. NIPS 2017: 3910-3919 - [i18]Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt:
Diffusion Independent Semi-Bandit Influence Maximization. CoRR abs/1703.00557 (2017) - [i17]Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric:
Active Learning for Accurate Estimation of Linear Models. CoRR abs/1703.00579 (2017) - [i16]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) - [i15]Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi:
Robust Locally-Linear Controllable Embedding. CoRR abs/1710.05373 (2017) - [i14]Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, Mohammad Ghavamzadeh:
Disentangling Dynamics and Content for Control and Planning. CoRR abs/1711.09165 (2017) - 2016
- [j12]Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Analysis of Classification-based Policy Iteration Algorithms. J. Mach. Learn. Res. 17: 19:1-19:30 (2016) - [j11]Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko:
Bayesian Policy Gradient and Actor-Critic Algorithms. J. Mach. Learn. Res. 17: 66:1-66:53 (2016) - [j10]Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
Regularized Policy Iteration with Nonparametric Function Spaces. J. Mach. Learn. Res. 17: 139:1-139:66 (2016) - [j9]Prashanth L. A., Mohammad Ghavamzadeh:
Variance-constrained actor-critic algorithms for discounted and average reward MDPs. Mach. Learn. 105(3): 367-417 (2016) - [c44]Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett:
Improved Learning Complexity in Combinatorial Pure Exploration Bandits. AISTATS 2016: 1004-1012 - [c43]Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik:
Proximal Gradient Temporal Difference Learning Algorithms. IJCAI 2016: 4195-4199 - [c42]Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow:
Safe Policy Improvement by Minimizing Robust Baseline Regret. NIPS 2016: 2298-2306 - [c41]Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun:
Graphical Model Sketch. ECML/PKDD (1) 2016: 81-97 - [i13]Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun:
Graphical Model Sketch. CoRR abs/1602.03105 (2016) - [i12]Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh:
Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem. CoRR abs/1603.01870 (2016) - [i11]