Stable LInear Approximations to Dynamic Programming for Stochastic Control Problems with Local Transitions. - Benjamin Van Roy; John N. Tsitsiklis (NIPS, 1995)

Analysis of Temporal-Diffference Learning with Function Approximation. - John N. Tsitsiklis; Benjamin Van Roy (NIPS, 1996)

Approximate Solutions to Optimal Stopping Problems. - John N. Tsitsiklis; Benjamin Van Roy (NIPS, 1996)

Feature-Based Methods for Large Scale Dynamic Programming. - John N. Tsitsiklis; Benjamin Van Roy (Mach. Learn., 1996)

An analysis of temporal-difference learning with function approximation. - John N. Tsitsiklis; Benjamin Van Roy (IEEE Trans. Autom. Control., 1997)

Learning and value function approximation in complex decision processes. - Benjamin Van Roy (1998)

An Analysis of Turbo Decoding with Gaussian Densities. - Paat Rusmevichientong; Benjamin Van Roy (NIPS, 1999)

Average cost temporal-difference learning. - John N. Tsitsiklis; Benjamin Van Roy (Autom., 1999)

Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives. - John N. Tsitsiklis; Benjamin Van Roy (IEEE Trans. Autom. Control., 1999)

Approximate value iteration with randomized policies. - Daniela Pucci de Farias; Benjamin Van Roy (CDC, 2000)

The optimal harvesting of environmental bads. - Nathaniel Keohane; Benjamin Van Roy; Richard Zeckhauser (CDC, 2000)

Fixed Points of Approximate Value Iteration and Temporal-Difference Learning. - Daniela Pucci de Farias; Benjamin Van Roy (ICML, 2000)

Approximate Dynamic Programming via Linear Programming. - Daniela Pucci de Farias; Benjamin Van Roy (NIPS, 2001)

A Tractable POMDP for Dynamic Sequencing with Applications to Personalized Internet Content Provision. - Paat Rusmevichientong; Benjamin Van Roy (UAI, 2001)

An analysis of belief propagation on the turbo decoding graph with Gaussian densities. - Paat Rusmevichientong; Benjamin Van Roy (IEEE Trans. Inf. Theory, 2001)

Regression methods for pricing complex American-style options. - John N. Tsitsiklis; Benjamin Van Roy (IEEE Trans. Neural Networks, 2001)

Approximate Linear Programming for Average-Cost Dynamic Programming. - Daniela Pucci de Farias; Benjamin Van Roy (NIPS, 2002)

On Average Versus Discounted Reward Temporal-Difference Learning. - John N. Tsitsiklis; Benjamin Van Roy (Mach. Learn., 2002)

On constraint sampling in the linear programming approach to approximate linear programming. - Daniela Pucci de Farias; Benjamin Van Roy (CDC, 2003)

Distributed Optimization in Adaptive Networks. - Ciamac Cyrus Moallemi; Benjamin Van Roy (NIPS, 2003)

Self-learning control of finite Markov chains: A.S. Poznyak, K. Najim, E. Gómez-Ramírez, Marcel Dekker, New York, 2000, $150, pp 298, ISBN 0-8247-9249-X. - Benjamin Van Roy (Autom., 2003)

Decentralized decision-making in a large team with local information. - Paat Rusmevichientong; Benjamin Van Roy (Games Econ. Behav., 2003)

The Linear Programming Approach to Approximate Dynamic Programming. - Daniela Pucci de Farias; Benjamin Van Roy (Oper. Res., 2003)

A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees. - Daniela Pucci de Farias; Benjamin Van Roy (NIPS, 2004)

Solitaire: Man Versus Machine. - Xiang Yan; Persi Diaconis; Paat Rusmevichientong; Benjamin Van Roy (NIPS, 2004)

Making Eigenvector-Based Reputation Systems Robust to Collusion. - Hui Zhang 002; Ashish Goel; Ramesh Govindan; Kahn Mason; Benjamin Van Roy (WAW, 2004)

On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming. - Daniela Pucci de Farias; Benjamin Van Roy (Math. Oper. Res., 2004)

A universal scheme for learning. - Vivek F. Farias; Ciamac C. Moallemi; Benjamin Van Roy; Tsachy Weissman (ISIT, 2005)

TD(0) Leads to Better Policies than Approximate Value Iteration. - Benjamin Van Roy (NIPS, 2005)

Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games. - Gabriel Y. Weintraub; C. Lanier Benkard; Benjamin Van Roy (NIPS, 2005)

Convergence of the Min-Sum Message Passing Algorithm for Quadratic Optimization - Ciamac Cyrus Moallemi; Benjamin Van Roy (CoRR, 2006)

A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning. - David Choi; Benjamin Van Roy (Discret. Event Dyn. Syst., 2006)

A Nonparametric Approach to Multiproduct Pricing. - Paat Rusmevichientong; Benjamin Van Roy; Peter W. Glynn (Oper. Res., 2006)

A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees. - Daniela Pucci de Farias; Benjamin Van Roy (Math. Oper. Res., 2006)

Performance Loss Bounds for Approximate Value Iteration with State Aggregation. - Benjamin Van Roy (Math. Oper. Res., 2006)

Approximation algorithms for dynamic resource allocation. - Vivek F. Farias; Benjamin Van Roy (Oper. Res. Lett., 2006)

Consensus Propagation. - Ciamac Cyrus Moallemi; Benjamin Van Roy (IEEE Trans. Inf. Theory, 2006)

Capacity and Zero-Error Capacity of the Chemical Channel with Feedback. - Haim H. Permuter; Paul Cuff; Benjamin Van Roy; Tsachy Weissman (ISIT, 2007)

A short proof of optimality for the MIN cache replacement algorithm. - Benjamin Van Roy (Inf. Process. Lett., 2007)

Reputation markets. - Xiang Yan; Benjamin Van Roy (NetEcon, 2008)

Capacity of the Trapdoor Channel With Feedback. - Haim H. Permuter; Paul Cuff; Benjamin Van Roy; Tsachy Weissman (IEEE Trans. Inf. Theory, 2008)

Directed Regression. - Yi-Hao Kao; Benjamin Van Roy; Xiang Yan (NIPS, 2009)

Manipulation-resistant collaborative filtering systems. - Benjamin Van Roy; Xiang Yan (RecSys, 2009)

Manipulation Robustness of Collaborative Filtering Systems - Xiang Yan; Benjamin Van Roy (CoRR, 2009)

Convergence of min-sum message passing for quadratic optimization. - Ciamac Cyrus Moallemi; Benjamin Van Roy (IEEE Trans. Inf. Theory, 2009)

On Regression-Based Stopping Times. - Benjamin Van Roy (Discret. Event Dyn. Syst., 2010)

Dynamic Pricing with a Prior on Market Response. - Vivek F. Farias; Benjamin Van Roy (Oper. Res., 2010)

Investment and Market Structure in Industries with Congestion. - Ramesh Johari; Gabriel Y. Weintraub; Benjamin Van Roy (Oper. Res., 2010)

Computational Methods for Oblivious Equilibrium. - Gabriel Y. Weintraub; C. Lanier Benkard; Benjamin Van Roy (Oper. Res., 2010)

Manipulation Robustness of Collaborative Filtering. - Benjamin Van Roy; Xiang Yan (Manag. Sci., 2010)

Universal reinforcement learning. - Vivek F. Farias; Ciamac Cyrus Moallemi; Benjamin Van Roy; Tsachy Weissman (IEEE Trans. Inf. Theory, 2010)

Convergence of min-sum message-passing for convex optimization. - Ciamac Cyrus Moallemi; Benjamin Van Roy (IEEE Trans. Inf. Theory, 2010)

Resource Allocation via Message Passing. - Ciamac Cyrus Moallemi; Benjamin Van Roy (INFORMS J. Comput., 2011)

Industry dynamics: Foundations for models with an infinite number of firms. - Gabriel Y. Weintraub; C. Lanier Benkard; Benjamin Van Roy (J. Econ. Theory, 2011)

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems. - Morteza Ibrahimi; Adel Javanmard; Benjamin Van Roy (NIPS, 2012)

A Hybrid Method for Distance Metric Learning. - Yi-Hao Kao; Benjamin Van Roy; Daniel L. Rubin; Jiajing Xu; Jessica S. Faruque; Sandy Napel (CoRR, 2012)

Directed Time Series Regression for Control - Yi-Hao Kao; Benjamin Van Roy (CoRR, 2012)

Intermediated Blind Portfolio Auctions. - Michael Padilla; Benjamin Van Roy (Manag. Sci., 2012)

(More) Efficient Reinforcement Learning via Posterior Sampling. - Ian Osband; Daniel Russo 001; Benjamin Van Roy (NIPS, 2013)

Eluder Dimension and the Sample Complexity of Optimistic Exploration. - Daniel Russo 001; Benjamin Van Roy (NIPS, 2013)

Efficient Exploration and Value Function Generalization in Deterministic Systems. - Zheng Wen 002; Benjamin Van Roy (NIPS, 2013)

A Tractable POMDP for a Class of Sequencing Problems - Paat Rusmevichientong; Benjamin Van Roy (CoRR, 2013)

Learning a factor model via regularized PCA. - Yi-Hao Kao; Benjamin Van Roy (Mach. Learn., 2013)

Near-optimal Reinforcement Learning in Factored MDPs. - Ian Osband; Benjamin Van Roy (NIPS, 2014)

Model-based Reinforcement Learning and the Eluder Dimension. - Ian Osband; Benjamin Van Roy (NIPS, 2014)

Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs. - Ian Osband; Benjamin Van Roy (CoRR, 2014)

Generalization and Exploration via Randomized Value Functions. - Benjamin Van Roy; Zheng Wen 002 (CoRR, 2014)

Directed Principal Component Analysis. - Yi-Hao Kao; Benjamin Van Roy (Oper. Res., 2014)

Learning to Optimize via Posterior Sampling. - Daniel Russo 001; Benjamin Van Roy (Math. Oper. Res., 2014)

Bootstrapped Thompson Sampling and Deep Exploration. - Ian Osband; Benjamin Van Roy (CoRR, 2015)

Adaptive Execution: Exploration and Learning of Price Impact. - Beomsoo Park; Benjamin Van Roy (Oper. Res., 2015)

Generalization and Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Zheng Wen 002 (ICML, 2016)

Deep Exploration via Bootstrapped DQN. - Ian Osband; Charles Blundell; Alexander Pritzel; Benjamin Van Roy (NIPS, 2016)

Conservative Contextual Linear Bandits. - Abbas Kazerouni; Mohammad Ghavamzadeh; Benjamin Van Roy (CoRR, 2016)

Posterior Sampling for Reinforcement Learning Without Episodes. - Ian Osband; Benjamin Van Roy (CoRR, 2016)

On Lower Bounds for Regret in Reinforcement Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2016)

An Information-Theoretic Analysis of Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy (J. Mach. Learn. Res., 2016)

Why is Posterior Sampling Better than Optimism for Reinforcement Learning? - Ian Osband; Benjamin Van Roy (ICML, 2017)

Conservative Contextual Linear Bandits. - Abbas Kazerouni; Mohammad Ghavamzadeh; Yasin Abbasi; Benjamin Van Roy (NIPS, 2017)

Ensemble Sampling. - Xiuyuan Lu; Benjamin Van Roy (NIPS, 2017)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband (CoRR, 2017)

Time-Sensitive Bandit Learning and Satisficing Thompson Sampling. - Daniel Russo 001; David Tse; Benjamin Van Roy (CoRR, 2017)

Gaussian-Dirichlet Posterior Dominance in Sequential Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2017)

On Optimistic versus Randomized Exploration in Reinforcement Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2017)

Learning to Price with Reference Effects. - Abbas Kazerouni; Benjamin Van Roy (CoRR, 2017)

Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization. - Zheng Wen 002; Benjamin Van Roy (Math. Oper. Res., 2017)

Coordinated Exploration in Concurrent Reinforcement Learning. - Maria Dimakopoulou; Benjamin Van Roy (ICML, 2018)

Scalable Coordinated Exploration in Concurrent Reinforcement Learning. - Maria Dimakopoulou; Ian Osband; Benjamin Van Roy (NeurIPS, 2018)

An Information-Theoretic Analysis for Thompson Sampling with Many Actions. - Shi Dong 003; Benjamin Van Roy (NeurIPS, 2018)

An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces. - Shi Dong 003; Benjamin Van Roy (CoRR, 2018)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2018)

Learning to Optimize via Information-Directed Sampling. - Daniel Russo 001; Benjamin Van Roy (Oper. Res., 2018)

On the Performance of Thompson Sampling on Logistic Bandits. - Shi Dong 003; Tengyu Ma 001; Benjamin Van Roy (COLT, 2019)

Information-Theoretic Confidence Bounds for Reinforcement Learning. - Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2019)

Comments on the Du-Kakade-Wang-Yang Lower Bounds. - Benjamin Van Roy; Shi Dong 003 (CoRR, 2019)

Provably Efficient Reinforcement Learning with Aggregated States. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (CoRR, 2019)

Deep Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Daniel J. Russo; Zheng Wen 002 (J. Mach. Learn. Res., 2019)

Hypermodels for Exploration. - Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Ian Osband; Zheng Wen 002; Benjamin Van Roy (ICLR, 2020)

Behaviour Suite for Reinforcement Learning. - Ian Osband; Yotam Doron; Matteo Hessel; John Aslanides; Eren Sezener; Andre Saraiva 001; Katrina McKinney; Tor Lattimore; Csaba Szepesvári; Satinder Singh 001; Benjamin Van Roy; Richard S. Sutton; et al. (ICLR, 2020)

On Efficiency in Hierarchical Reinforcement Learning. - Zheng Wen 002; Doina Precup; Morteza Ibrahimi; André Barreto 001; Benjamin Van Roy; Satinder Singh 001 (NeurIPS, 2020)

Randomized Value Functions via Posterior State-Abstraction Sampling. - Dilip Arumugam; Benjamin Van Roy (CoRR, 2020)

Deciding What to Learn: A Rate-Distortion Approach. - Dilip Arumugam; Benjamin Van Roy (ICML, 2021)

The Value of Information When Deciding What to Learn. - Dilip Arumugam; Benjamin Van Roy (NeurIPS, 2021)

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (CoRR, 2021)

A Bit Better? Quantifying Information for Bandit Learning. - Adithya M. Devraj; Benjamin Van Roy; Kuang Xu (CoRR, 2021)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Mohammad Asghari; Morteza Ibrahimi; Xiyuan Lu; Benjamin Van Roy (CoRR, 2021)

Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions. - Xiuyuan Lu; Ian Osband; Benjamin Van Roy; Zheng Wen 002 (CoRR, 2021)

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Botao Hao; Morteza Ibrahimi; Dieterich Lawson; Xiuyuan Lu; Brendan O'Donoghue; Benjamin Van Roy (CoRR, 2021)

Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. - Dilip Arumugam; Benjamin Van Roy (NeurIPS, 2022)

An Information-Theoretic Framework for Deep Learning. - Hong Jun Jeon; Benjamin Van Roy (NeurIPS, 2022)

The Neural Testbed: Evaluating Joint Predictions. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Dieterich Lawson; Botao Hao; Brendan O'Donoghue; Benjamin Van Roy (NeurIPS, 2022)

An Analysis of Ensemble Sampling. - Chao Qin; Zheng Wen 002; Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2022)

Evaluating high-order predictive distributions in deep learning. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Benjamin Van Roy (UAI, 2022)

Gaussian Imagination in Bandit Learning. - Yueyang Liu; Adithya M. Devraj; Benjamin Van Roy; Kuang Xu (CoRR, 2022)

Sample Complexity versus Depth: An Information Theoretic Analysis. - Hong Jun Jeon; Benjamin Van Roy (CoRR, 2022)

Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning. - Dilip Arumugam; Benjamin Van Roy (CoRR, 2022)

Robustness of Epinets against Distributional Shifts. - Xiuyuan Lu; Ian Osband; Seyed Mohammad Asghari; Sven Gowal; Vikranth Dwaracherla; Zheng Wen 002; Benjamin Van Roy (CoRR, 2022)

Is Stochastic Gradient Descent Near Optimal? - Yifan Zhu; Hong Jun Jeon; Benjamin Van Roy (CoRR, 2022)

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2022)

Fine-Tuning Language Models via Epistemic Neural Networks. - Ian Osband; Seyed Mohammad Asghari; Benjamin Van Roy; Nat McAleese; John Aslanides; Geoffrey Irving (CoRR, 2022)

An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws. - Hong Jun Jeon; Benjamin Van Roy (CoRR, 2022)

Inclusive Artificial Intelligence. - Dilip Arumugam; Shi Dong 003; Benjamin Van Roy (CoRR, 2022)

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States. - Shi Dong 003; Benjamin Van Roy; Zhengyuan Zhou (J. Mach. Learn. Res., 2022)

Satisficing in Time-Sensitive Bandit Learning. - Daniel Russo 001; Benjamin Van Roy (Math. Oper. Res., 2022)

Nonstationary Bandit Learning via Predictive Sampling. - Yueyang Liu; Benjamin Van Roy; Kuang Xu (AISTATS, 2023)

Scalable Neural Contextual Bandit for Recommender Systems. - Zheqing Zhu; Benjamin Van Roy (CIKM, 2023)

Leveraging Demonstrations to Improve Online Learning: Quality Matters. - Botao Hao; Rahul Jain 002; Tor Lattimore; Benjamin Van Roy; Zheng Wen 002 (ICML, 2023)

A Definition of Continual Reinforcement Learning. - David Abel; André Barreto 001; Benjamin Van Roy; Doina Precup; Hado Philip van Hasselt; Satinder Singh 001 (NeurIPS, 2023)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2023)

Deep Exploration for Recommendation Systems. - Zheqing Zhu; Benjamin Van Roy (RecSys, 2023)

Approximate Thompson Sampling via Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (UAI, 2023)

A Definition of Non-Stationary Bandits. - Yueyang Liu; Benjamin Van Roy; Kuang Xu (CoRR, 2023)

Bayesian Reinforcement Learning with Limited Cognitive Load. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2023)

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. - Wanqiao Xu; Shi Dong 003; Dilip Arumugam; Benjamin Van Roy (CoRR, 2023)

On the Convergence of Bounded Agents. - David Abel; André Barreto 001; Hado van Hasselt; Benjamin Van Roy; Doina Precup; Satinder Singh 001 (CoRR, 2023)

Maintaining Plasticity via Regenerative Regularization. - Saurabh Kumar 004; Henrik Marklund; Benjamin Van Roy (CoRR, 2023)

Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling. - Zheqing Zhu; Yueyang Liu; Xu Kuang; Benjamin Van Roy (CoRR, 2023)

RLHF and IIA: Perverse Incentives. - Wanqiao Xu; Shi Dong 003; Xiuyuan Lu; Grace Lam; Zheng Wen 002; Benjamin Van Roy (CoRR, 2023)

Reinforcement Learning, Bit by Bit. - Xiuyuan Lu; Benjamin Van Roy; Vikranth Dwaracherla; Morteza Ibrahimi; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2023)

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. - Vikranth Dwaracherla; Zheng Wen 002; Ian Osband; Xiuyuan Lu; Seyed Mohammad Asghari; Benjamin Van Roy (Trans. Mach. Learn. Res., 2023)

Langevin DQN. - Vikranth Dwaracherla; Benjamin Van Roy (CDC, 2024)

Maintaining Plasticity in Continual Learning via Regenerative Regularization. - Saurabh Kumar 004; Henrik Marklund; Benjamin Van Roy (CoLLAs, 2024)

Efficient Exploration for LLMs. - Vikranth Dwaracherla; Seyed Mohammad Asghari; Botao Hao; Benjamin Van Roy (ICML, 2024)

An Information-Theoretic Analysis of In-Context Learning. - Hong Jun Jeon; Jason D. Lee; Qi Lei; Benjamin Van Roy (ICML, 2024)

Posterior Sampling for Continuing Environments. - Wanqiao Xu; Shi Dong 003; Benjamin Van Roy (RLJ, 2024)

Adaptive Crowdsourcing Via Self-Supervised Learning. - Anmol Kagrecha; Henrik Marklund; Benjamin Van Roy; Hong Jun Jeon; Richard Zeckhauser (CoRR, 2024)

Information-Theoretic Foundations for Neural Scaling Laws. - Hong Jun Jeon; Benjamin Van Roy (CoRR, 2024)

Exploration Unbound. - Dilip Arumugam; Wanqiao Xu; Benjamin Van Roy (CoRR, 2024)

Satisficing Exploration for Deep Reinforcement Learning. - Dilip Arumugam; Saurabh Kumar 004; Ramki Gummadi; Benjamin Van Roy (CoRR, 2024)

Information-Theoretic Foundations for Machine Learning. - Hong Jun Jeon; Benjamin Van Roy (CoRR, 2024)

The Need for a Big World Simulator: A Scientific Challenge for Continual Learning. - Saurabh Kumar 004; Hong Jun Jeon; Alex Lewandowski; Benjamin Van Roy (CoRR, 2024)

Aligning AI Agents via Information-Directed Sampling. - Hong Jun Jeon; Benjamin Van Roy (CoRR, 2024)

Choice between Partial Trajectories. - Henrik Marklund; Benjamin Van Roy (CoRR, 2024)

Continual Learning as Computationally Constrained Reinforcement Learning. - Saurabh Kumar 004; Henrik Marklund; Ashish Rao; Yifan Zhu; Hong Jun Jeon; Yueyang Liu; Benjamin Van Roy (Found. Trends Mach. Learn., 2025)

Misalignment from Treating Means as Ends. - Henrik Marklund; Alex Infanger; Benjamin Van Roy (CoRR, 2025)

Granular feedback merits sophisticated aggregation. - Anmol Kagrecha; Henrik Marklund; Potsawee Manakul; Richard Zeckhauser; Benjamin Van Roy (CoRR, 2025)

Capacity-Constrained Continual Learning. - Zheng Wen; Doina Precup; Benjamin Van Roy; Satinder Singh 001 (CoRR, 2025)