4 papers
Self-Bounding Regret Matching+ in Potential Games and Product-Simplex Optimization
Pahan Dewasurendra, Subhashini Jayawardhana
Regret matching+ (RM+) is parameter free, scale invariant, and central to large game solving, but its only general individual-regret guarantee grows as . A recent ICLR re…
Convex Networks Remain Hard to Certify: Dimension-Accuracy Barriers for Lipschitz Constants
Pahan Dewasurendra, Subhashini Jayawardhana
Input-convex neural networks permit globally tractable minimization over their inputs, so one might expect their global regularity to be tractable in low input dimension. We prove…
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…
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…