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cs.LG2026
Multiscale Reward Hedging from Correct Demonstrations
Pahan Dewasurendra
Learning from correct demonstrations is harder than supervised learning when many answers are correct: after predicting, the learner sees one valid answer but not whether its own a…
cs.LG2026
Dirichlet Follow-the-Leader Closes the Gap in Simultaneous Multiclass U-Calibration
Pahan Dewasurendra
Can one forecaster attain the optimal regret rate for every bounded proper loss and also adapt to every smooth proper loss? Recent work answered this up to a dimension gap. Its sel…
cs.LG2024
Unsupervised Replay Strategies for Continual Learning with Limited Data
Anthony Bazhenov, Pahan Dewasurendra, Giri P. Krishnan +1
Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learn…