Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
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A feature-based Q-learning method for risk-averse finite-horizon MDPs using newly defined mini-batch coherent risk measures and multipattern Q-factor approximation achieves a regret bound of O(H² N^H √K).
Derives privacy-dependent lower bounds for fixed-confidence BAI and gives asymptotically optimal DP Top-Two algorithms for local and global models.
citing papers explorer
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Boundedly Rational Meta-Learning in Sequential Consumer Choice
Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
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Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
A feature-based Q-learning method for risk-averse finite-horizon MDPs using newly defined mini-batch coherent risk measures and multipattern Q-factor approximation achieves a regret bound of O(H² N^H √K).
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Differentially Private Best-Arm Identification
Derives privacy-dependent lower bounds for fixed-confidence BAI and gives asymptotically optimal DP Top-Two algorithms for local and global models.