Education
Decentralized, Communication-and Coordination-free Learning in Structured Matching Markets
We study the problem of online learning in competitive settings in the context of two-sided matching markets.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Meta learning is a promising paradigm to enable skill transfer across tasks.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm Qi Wang
Meta learning is a promising paradigm to enable skill transfer across tasks.
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In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint.
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In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint.
Drop-DTW: AligningCommonSignalBetween SequencesWhileDroppingOutliers
With Drop-DTW,we address temporal step localization on instructional videos, representation learning from noisy videos, and cross-modal representation learning for audio-visual retrievaland localization.
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm.