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.
Journal of artificial intelligence research , volume=
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cs.LG 3years
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A new first-order algorithm for multi-task learning with shared linear representation achieves near-optimal error rates in constant iterations, improving existing methods by a factor of k.
DKPS-based methods leverage cached model responses to achieve equivalent benchmark prediction accuracy with substantially fewer queries than standard evaluation.
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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Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
A new first-order algorithm for multi-task learning with shared linear representation achieves near-optimal error rates in constant iterations, improving existing methods by a factor of k.
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Query-efficient model evaluation using cached responses
DKPS-based methods leverage cached model responses to achieve equivalent benchmark prediction accuracy with substantially fewer queries than standard evaluation.