Introduces BAI with post-action context in fixed-confidence stochastic bandits, derives instance-dependent lower bounds, and gives asymptotically optimal algorithms for separator and non-separator cases.
Pac subset selection in stochastic multi-armed bandits
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RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
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Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context
Introduces BAI with post-action context in fixed-confidence stochastic bandits, derives instance-dependent lower bounds, and gives asymptotically optimal algorithms for separator and non-separator cases.
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RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.