119 citations · 244 across the 26 of their papers we have counts for
26 papers
Calibeating for general proper losses: A Bregman divergence approach
Maximilian Fichtl, Cristóbal Guzmán, Nishant A. Mehta
This work introduces a general framework for calibeating based on regret minimization. As compared to Foster and Hart's seminal calibeating work which had specialized treatments of…
Toward Simultaneously Optimal Regret in U-Calibration
Rafael Frongillo, Haipeng Luo, Nishant A. Mehta +1
U-calibration studies online forecasting algorithms whose predictions can be consumed by any unknown downstream agent, guaranteeing sublinear regret simultaneously for all proper l…
No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts
Junpei Komiyama, Nishant A. Mehta, Ali Mortazavi
We study an online forecasting setting in which, over rounds, strategic experts each report a forecast to a mechanism, the mechanism selects one forecast, and then the outc…
Data-dependent Bounds with -Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
Quan Nguyen, Shinji Ito, Junpei Komiyama +1
Existing data-dependent and best-of-both-worlds regret bounds for multi-armed bandits problems have limited adaptivity as they are either data-dependent but not best-of-both-worlds…
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
Quan Nguyen, Nishant A. Mehta, Cristóbal Guzmán
The minimax sample complexity of group distributionally robust optimization (GDRO) has been determined up to a factor, where is the number of groups. In this work, we…
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
Ali Mortazavi, Junhao Lin, Nishant A. Mehta
In one view of the classical game of prediction with expert advice with binary outcomes, in each round, each expert maintains an adversarially chosen belief and honestly reports th…