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Workshop
Fri 9:30 Let’s Give Domain Experts a Choice by Creating Many Approximately-Optimal Machine Learning Models
Cynthia Rudin
Workshop
Function Approximations for Reinforcement Learning Controller for Wave Energy Converters
Soumyendu Sarkar · Vineet Gundecha · Alexander Shmakov · Sahand Ghorbanpour · Ashwin Ramesh Babu · Alexandre Pichard · Mathieu Cocho
Workshop
Function Approximations for Reinforcement Learning Controller for Wave Energy Converters
Soumyendu Sarkar · Vineet Gundecha · Alexander Shmakov · Sahand Ghorbanpour · Ashwin Ramesh Babu · Alexandre Pichard · Mathieu Cocho
Workshop
Task Modeling: Approximating Multitask Predictions for Cross-Task Transfer
Dongyue Li · Huy Nguyen · Hongyang Zhang
Workshop
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
Melrose Roderick · Felix Berkenkamp · Fatemeh Sheikholeslami · J. Zico Kolter
Workshop
Function Approximations for Reinforcement Learning Controller for Wave Energy Converters
Soumyendu Sarkar · Vineet Gundecha · Alexander Shmakov · Sahand Ghorbanpour · Ashwin Ramesh Babu · Alexandre Pichard · Mathieu Cocho
Workshop
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
Dan Qiao · Yu-Xiang Wang
Workshop
Barron's Theorem for Equivariant Networks
Hannah Lawrence
Workshop
Compositional Task Generalization with Modular Successor Feature Approximators
Wilka Carvalho Carvalho
Workshop
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan
Workshop
Approximate Bayesian Computation for Panel Data with Signature Maximum Mean Discrepancies
Joel Dyer · John Fitzgerald · Bastian Rieck · Sebastian Schmon
Affinity Workshop
Mon 8:25 Oral Presentation 7: Adapting the Function Approximation Architecture in Online Reinforcement Learning
Fatima Davelouis