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Yinlam Chow
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2020 – today
- 2024
- [c37]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier:
Demystifying Embedding Spaces using Large Language Models. ICLR 2024 - [i37]Anthony Liang, Guy Tennenholtz, Chih-Wei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier:
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning. CoRR abs/2402.15957 (2024) - [i36]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier:
Embedding-Aligned Language Models. CoRR abs/2406.00024 (2024) - 2023
- [c36]Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier:
A Mixture-of-Expert Approach to RL-based Dialogue Management. ICLR 2023 - [c35]Dhawal Gupta, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier:
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. NeurIPS 2023 - [i35]Dhawal Gupta, Yinlam Chow, Mohammad Ghavamzadeh, Craig Boutilier:
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. CoRR abs/2302.10850 (2023) - [i34]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier:
Demystifying Embedding Spaces using Large Language Models. CoRR abs/2310.04475 (2023) - [i33]Jihwan Jeong, Yinlam Chow, Guy Tennenholtz, Chih-Wei Hsu, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier:
Factual and Personalized Recommendations using Language Models and Reinforcement Learning. CoRR abs/2310.06176 (2023) - [i32]Erdem Biyik, Fan Yao, Yinlam Chow, Alex Haig, Chih-Wei Hsu, Mohammad Ghavamzadeh, Craig Boutilier:
Preference Elicitation with Soft Attributes in Interactive Recommendation. CoRR abs/2311.02085 (2023) - 2022
- [c34]Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Efficient Risk-Averse Reinforcement Learning. NeurIPS 2022 - [c33]Christina Göpfert, Yinlam Chow, Chih-Wei Hsu
, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. WWW 2022: 2411-2421 - [i31]Christina Göpfert, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. CoRR abs/2202.02830 (2022) - [i30]Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers:
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. CoRR abs/2202.04849 (2022) - [i29]Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Efficient Risk-Averse Reinforcement Learning. CoRR abs/2205.05138 (2022) - [i28]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) - [i27]Deborah Cohen, Moonkyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, Gal Elidan:
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. CoRR abs/2208.02294 (2022) - 2021
- [c32]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed:
Non-Stationary Off-Policy Optimization. AISTATS 2021: 2494-2502 - [c31]Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh:
Control-Aware Representations for Model-based Reinforcement Learning. ICLR 2021 - [c30]Yinlam Chow, Brandon Cui, Moonkyung Ryu, Mohammad Ghavamzadeh:
Variational Model-based Policy Optimization. IJCAI 2021: 2292-2299 - [c29]Tsung-Yen Yang, Michael Y. Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan:
Safe Reinforcement Learning with Natural Language Constraints. NeurIPS 2021: 13794-13808 - 2020
- [c28]Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
Safe Policy Learning for Continuous Control. CoRL 2020: 801-821 - [c27]Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui:
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. ICLR 2020 - [c26]Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier:
CAQL: Continuous Action Q-Learning. ICLR 2020 - [c25]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 - [c24]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed H. Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. IJCAI 2020: 2824-2830 - [c23]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS 2020 - [c22]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier:
Latent Bandits Revisited. NeurIPS 2020 - [i26]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed H. Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. CoRR abs/2002.05522 (2020) - [i25]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) - [i24]Yinlam Chow, Brandon Cui, Moonkyung Ryu, Mohammad Ghavamzadeh:
Variational Model-based Policy Optimization. CoRR abs/2006.05443 (2020) - [i23]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed:
Piecewise-Stationary Off-Policy Optimization. CoRR abs/2006.08236 (2020) - [i22]Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier:
Latent Bandits Revisited. CoRR abs/2006.08714 (2020) - [i21]Brandon Cui, Yinlam Chow, Mohammad Ghavamzadeh:
Control-Aware Representations for Model-based Reinforcement Learning. CoRR abs/2006.13408 (2020) - [i20]Tsung-Yen Yang, Michael Y. Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan:
Safe Reinforcement Learning with Natural Language Constraints. CoRR abs/2010.05150 (2020) - [i19]Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
CoinDICE: Off-Policy Confidence Interval Estimation. CoRR abs/2010.11652 (2020) - [i18]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
- [j5]Sumeet Singh
, Yinlam Chow
, Anirudha Majumdar
, Marco Pavone
:
A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms. IEEE Trans. Autom. Control. 64(7): 2905-2912 (2019) - [c21]Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone:
Risk-Sensitive Generative Adversarial Imitation Learning. AISTATS 2019: 2154-2163 - [c20]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. NeurIPS 2019: 2315-2325 - [i17]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) - [i16]Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. CoRR abs/1906.04733 (2019) - [i15]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) - [i14]Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier:
CAQL: Continuous Action Q-Learning. CoRR abs/1909.12397 (2019) - [i13]Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, Dale Schuurmans:
AlgaeDICE: Policy Gradient from Arbitrary Experience. CoRR abs/1912.02074 (2019) - 2018
- [c19]Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore:
Imitation Learning from Visual Data with Multiple Intentions. ICLR (Poster) 2018 - [c18]Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. ICML 2018: 978-987 - [c17]Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh:
More Robust Doubly Robust Off-policy Evaluation. ICML 2018: 1446-1455 - [c16]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 - [c15]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. NeurIPS 2018: 8103-8112 - [i12]Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh:
More Robust Doubly Robust Off-policy Evaluation. CoRR abs/1802.03493 (2018) - [i11]Ofir Nachum, Yinlam Chow, Mohammad Ghavamzadeh:
Path Consistency Learning in Tsallis Entropy Regularized MDPs. CoRR abs/1802.03501 (2018) - [i10]Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh:
A Lyapunov-based Approach to Safe Reinforcement Learning. CoRR abs/1805.07708 (2018) - [i9]Jonathan Lacotte, Yinlam Chow, Mohammad Ghavamzadeh, Marco Pavone:
Risk-Sensitive Generative Adversarial Imitation Learning. CoRR abs/1808.04468 (2018) - [i8]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) - 2017
- [j4]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) - [j3]Jiyan Yang, Yin-Lam Chow, Christopher Ré, Michael W. Mahoney:
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning. J. Mach. Learn. Res. 18: 211:1-211:43 (2017) - [j2]Aviv Tamar, Yinlam Chow
, Mohammad Ghavamzadeh, Shie Mannor
:
Sequential Decision Making With Coherent Risk. IEEE Trans. Autom. Control. 62(7): 3323-3338 (2017) - [c14]Alan Malek, Sumeet Katariya, Yinlam Chow, Mohammad Ghavamzadeh:
Sequential Multiple Hypothesis Testing with Type I Error Control. AISTATS 2017: 1468-1476 - [i7]Yin-Lam Chow, Sumeet Singh, Anirudha Majumdar, Marco Pavone:
A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms. CoRR abs/1703.01029 (2017) - 2016
- [j1]Junjie Qin, Yinlam Chow, Jiyan Yang, Ram Rajagopal:
Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty. IEEE Trans. Smart Grid 7(2): 1106-1118 (2016) - [c13]Stefano Carpin, Yinlam Chow, Marco Pavone:
Risk aversion in finite Markov Decision Processes using total cost criteria and average value at risk. ICRA 2016: 335-342 - [c12]Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow:
Safe Policy Improvement by Minimizing Robust Baseline Regret. NIPS 2016: 2298-2306 - [c11]Jiyan Yang, Yinlam Chow, Christopher Ré, Michael W. Mahoney:
Weighted SGD for ℓp Regression with Randomized Preconditioning. SODA 2016: 558-569 - 2015
- [c10]Yinlam Chow, Jia Yuan Yu:
Real-time Bidding based Vehicle Sharing. AAMAS 2015: 1829-1830 - [c9]Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Policy Gradient for Coherent Risk Measures. NIPS 2015: 1468-1476 - [c8]Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone:
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach. NIPS 2015: 1522-1530 - [i6]Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor:
Policy Gradient for Coherent Risk Measures. CoRR abs/1502.03919 (2015) - [i5]Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone:
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach. CoRR abs/1506.02188 (2015) - [i4]Yinlam Chow, Jia Yuan Yu:
Real-time Bidding based Vehicle Sharing. CoRR abs/1509.08932 (2015) - [i3]Yinlam Chow, Marco Pavone, Brian M. Sadler, Stefano Carpin:
Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms with Robust Performance Constraints. CoRR abs/1511.06982 (2015) - [i2]Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone:
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria. CoRR abs/1512.01629 (2015) - 2014
- [c7]Yin-Lam Chow, Marco Pavone:
A framework for time-consistent, risk-averse model predictive control: Theory and algorithms. ACC 2014: 4204-4211 - [c6]Yinlam Chow, Junjie Qin:
Weighted difference approximation of value functions for slow-discounting Markov Decision Processes. CDC 2014: 1085-1090 - [c5]Junjie Qin, Yinlam Chow, Jiyan Yang, Ram Rajagopal:
Modeling and online control of generalized energy storage networks. e-Energy 2014: 27-38 - [c4]Yinlam Chow, Mohammad Ghavamzadeh:
Algorithms for CVaR Optimization in MDPs. NIPS 2014: 3509-3517 - [i1]Yinlam Chow, Mohammad Ghavamzadeh:
Algorithms for CVaR Optimization in MDPs. CoRR abs/1406.3339 (2014) - 2013
- [c3]Yin-Lam Chow, Marco Pavone:
Stochastic optimal control with dynamic, time-consistent risk constraints. ACC 2013: 390-395 - [c2]Yin-Lam Chow, Marco Pavone:
A uniform-grid discretization algorithm for stochastic optimal control with risk constraints. CDC 2013: 2465-2470 - 2011
- [c1]Carlos Villegas, Yin-Lam Chow, Martin J. Corless, Robert Shorten, Wynita M. Griggs
:
A decentralized control technique for vehicle chassis control. CDC/ECC 2011: 2535-2540
Coauthor Index
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last updated on 2024-08-08 20:12 CEST by the dblp team
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