A decentralized control technique for vehicle chassis control. - Carlos Villegas; Yin-Lam Chow; Martin J. Corless; Robert Shorten; Wynita M. Griggs (CDC/ECC, 2011)

Stochastic optimal control with dynamic, time-consistent risk constraints. - Yin-Lam Chow; Marco Pavone 001 (ACC, 2013)

A uniform-grid discretization algorithm for stochastic optimal control with risk constraints. - Yin-Lam Chow; Marco Pavone 001 (CDC, 2013)

A framework for time-consistent, risk-averse model predictive control: Theory and algorithms. - Yin-Lam Chow; Marco Pavone 001 (ACC, 2014)

Weighted difference approximation of value functions for slow-discounting Markov Decision Processes. - Yinlam Chow; Junjie Qin (CDC, 2014)

Modeling and online control of generalized energy storage networks. - Junjie Qin; Yinlam Chow; Jiyan Yang; Ram Rajagopal (e-Energy, 2014)

Algorithms for CVaR Optimization in MDPs. - Yinlam Chow; Mohammad Ghavamzadeh (NIPS, 2014)

Real-time Bidding based Vehicle Sharing. - Yinlam Chow; Jia Yuan Yu (AAMAS, 2015)

Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach. - Yinlam Chow; Aviv Tamar; Shie Mannor; Marco Pavone 001 (NIPS, 2015)

Policy Gradient for Coherent Risk Measures. - Aviv Tamar; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (NIPS, 2015)

Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms with Robust Performance Constraints. - Yinlam Chow; Marco Pavone 001; Brian M. Sadler; Stefano Carpin (CoRR, 2015)

Risk aversion in finite Markov Decision Processes using total cost criteria and average value at risk. - Stefano Carpin; Yinlam Chow; Marco Pavone 001 (ICRA, 2016)

Safe Policy Improvement by Minimizing Robust Baseline Regret. - Mohammad Ghavamzadeh; Marek Petrik; Yinlam Chow (NIPS, 2016)

Weighted SGD for ℓp Regression with Randomized Preconditioning. - Jiyan Yang; Yinlam Chow; Christopher Ré; Michael W. Mahoney (SODA, 2016)

Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty. - Junjie Qin; Yinlam Chow; Jiyan Yang; Ram Rajagopal (IEEE Trans. Smart Grid, 2016)

Sequential Multiple Hypothesis Testing with Type I Error Control. - Alan Malek; Sumeet Katariya; Yinlam Chow; Mohammad Ghavamzadeh (AISTATS, 2017)

A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms. - Yin-Lam Chow; Sumeet Singh; Anirudha Majumdar; Marco Pavone 001 (CoRR, 2017)

Risk-Constrained Reinforcement Learning with Percentile Risk Criteria. - Yinlam Chow; Mohammad Ghavamzadeh; Lucas Janson; Marco Pavone 001 (J. Mach. Learn. Res., 2017)

Weighted SGD for $ ell_p$ Regression with Randomized Preconditioning. - Jiyan Yang; Yin-Lam Chow; Christopher Ré; Michael W. Mahoney (J. Mach. Learn. Res., 2017)

Sequential Decision Making With Coherent Risk. - Aviv Tamar; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (IEEE Trans. Autom. Control., 2017)

Imitation Learning from Visual Data with Multiple Intentions. - Aviv Tamar; Khashayar Rohanimanesh; Yinlam Chow; Chris Vigorito; Ben Goodrich; Michael Kahane; Derik Pridmore (ICLR (Poster), 2018)

Path Consistency Learning in Tsallis Entropy Regularized MDPs. - Yinlam Chow; Ofir Nachum; Mohammad Ghavamzadeh (ICML, 2018)

More Robust Doubly Robust Off-policy Evaluation. - Mehrdad Farajtabar; Yinlam Chow; Mohammad Ghavamzadeh (ICML, 2018)

A Lyapunov-based Approach to Safe Reinforcement Learning. - Yinlam Chow; Ofir Nachum; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (NeurIPS, 2018)

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. - Tengyang Xie; Bo Liu 006; Yangyang Xu; Mohammad Ghavamzadeh; Yinlam Chow; Daoming Lyu; Daesub Yoon (NeurIPS, 2018)

Risk-Sensitive Generative Adversarial Imitation Learning. - Jonathan Lacotte; Mohammad Ghavamzadeh; Yinlam Chow; Marco Pavone 001 (AISTATS, 2019)

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. - Ofir Nachum; Yinlam Chow; Bo Dai 001; Lihong Li 001 (NeurIPS, 2019)

Lyapunov-based Safe Policy Optimization for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Mohammad Ghavamzadeh; Edgar A. Duéñez-Guzmán (CoRR, 2019)

AlgaeDICE: Policy Gradient from Arbitrary Experience. - Ofir Nachum; Bo Dai 001; Ilya Kostrikov; Yinlam Chow; Lihong Li 001; Dale Schuurmans (CoRR, 2019)

A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms. - Sumeet Singh; Yinlam Chow; Anirudha Majumdar; Marco Pavone 001 (IEEE Trans. Autom. Control., 2019)

Safe Policy Learning for Continuous Control. - Yinlam Chow; Ofir Nachum; Aleksandra Faust; Edgar A. Duéñez-Guzmán; Mohammad Ghavamzadeh (CoRL, 2020)

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control. - Nir Levine; Yinlam Chow; Rui Shu; Ang Li; Mohammad Ghavamzadeh; Hung Bui (ICLR, 2020)

CAQL: Continuous Action Q-Learning. - Moonkyung Ryu; Yinlam Chow; Ross Anderson; Christian Tjandraatmadja; Craig Boutilier (ICLR, 2020)

Predictive Coding for Locally-Linear Control. - Rui Shu; Tung Nguyen; Yinlam Chow; Tuan Pham; Khoat Than; Mohammad Ghavamzadeh; Stefano Ermon; Hung H. Bui (ICML, 2020)

BRPO: Batch Residual Policy Optimization. - Sungryull Sohn; Yinlam Chow; Jayden Ooi; Ofir Nachum; Honglak Lee; Ed H. Chi; Craig Boutilier (IJCAI, 2020)

CoinDICE: Off-Policy Confidence Interval Estimation. - Bo Dai 001; Ofir Nachum; Yinlam Chow; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

Latent Bandits Revisited. - Joey Hong; Branislav Kveton; Manzil Zaheer; Yinlam Chow; Amr Ahmed 001; Craig Boutilier (NeurIPS, 2020)

Piecewise-Stationary Off-Policy Optimization. - Joey Hong; Branislav Kveton; Manzil Zaheer; Yinlam Chow; Amr Ahmed 001 (CoRR, 2020)

Non-Stationary Latent Bandits. - Joey Hong; Branislav Kveton; Manzil Zaheer; Yinlam Chow; Amr Ahmed 001; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2020)

Non-Stationary Off-Policy Optimization. - Joey Hong; Branislav Kveton; Manzil Zaheer; Yinlam Chow; Amr Ahmed 001 (AISTATS, 2021)

Control-Aware Representations for Model-based Reinforcement Learning. - Brandon Cui; Yinlam Chow; Mohammad Ghavamzadeh (ICLR, 2021)

Variational Model-based Policy Optimization. - Yinlam Chow; Brandon Cui; Moonkyung Ryu; Mohammad Ghavamzadeh (IJCAI, 2021)

Safe Reinforcement Learning with Natural Language Constraints. - Tsung-Yen Yang; Michael Y. Hu; Yinlam Chow; Peter J. Ramadge; Karthik Narasimhan (NeurIPS, 2021)

Efficient Risk-Averse Reinforcement Learning. - Ido Greenberg; Yinlam Chow; Mohammad Ghavamzadeh; Shie Mannor (NeurIPS, 2022)

Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. - Christina Göpfert; Yinlam Chow; Chih-Wei Hsu; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Craig Boutilier (WWW, 2022)

SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. - Dylan Slack; Yinlam Chow; Bo Dai 001; Nevan Wichers (CoRR, 2022)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2022)

Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. - Deborah Cohen; Moonkyung Ryu; Yinlam Chow; Orgad Keller; Ido Greenberg; Avinatan Hassidim; Michael Fink; Yossi Matias; Idan Szpektor; Craig Boutilier; Gal Elidan (CoRR, 2022)

A Mixture-of-Expert Approach to RL-based Dialogue Management. - Yinlam Chow; Aza Tulepbergenov; Ofir Nachum; Dhawal Gupta; Moonkyung Ryu; Mohammad Ghavamzadeh; Craig Boutilier (ICLR, 2023)

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. - Dhawal Gupta; Yinlam Chow; Azamat Tulepbergenov; Mohammad Ghavamzadeh; Craig Boutilier (NeurIPS, 2023)

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management. - Dhawal Gupta; Yinlam Chow; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Factual and Personalized Recommendations using Language Models and Reinforcement Learning. - Jihwan Jeong; Yinlam Chow; Guy Tennenholtz; Chih-Wei Hsu; Azamat Tulepbergenov; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Preference Elicitation with Soft Attributes in Interactive Recommendation. - Erdem Biyik; Fan Yao; Yinlam Chow; Alex Haig; Chih-Wei Hsu; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

Demystifying Embedding Spaces using Large Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Jihwan Jeong; Lior Shani; Azamat Tulepbergenov; Deepak Ramachandran; Martin Mladenov; Craig Boutilier (ICLR, 2024)

DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning. - Anthony Liang; Guy Tennenholtz; Chih-Wei Hsu; Yinlam Chow; Erdem Biyik; Craig Boutilier (NeurIPS, 2024)

Embedding-Aligned Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Lior Shani; Yi Liang; Craig Boutilier (NeurIPS, 2024)

Embedding-Aligned Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Lior Shani; Ethan Liang; Craig Boutilier (CoRR, 2024)

Personalized and Sequential Text-to-Image Generation. - Ofir Nabati; Guy Tennenholtz; Chih-Wei Hsu; Moonkyung Ryu; Deepak Ramachandran; Yinlam Chow; Xiang Li; Craig Boutilier (CoRR, 2024)

Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors. - Christina Göpfert; Alex Haig; Chih-Wei Hsu; Yinlam Chow; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Hubert Pham; Mohammad Ghavamzadeh; Craig Boutilier (Trans. Recomm. Syst., 2024)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. - Yinlam Chow; Guy Tennenholtz; Izzeddin Gur; Vincent Zhuang; Bo Dai 001; Aviral Kumar; Rishabh Agarwal; Sridhar Thiagarajan; Craig Boutilier; Aleksandra Faust (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

Descriptive History Representations: Learning Representations by Answering Questions. - Guy Tennenholtz; Jihwan Jeong; Chih-Wei Hsu; Yinlam Chow; Craig Boutilier (CoRR, 2025)