18 citations · 42 across the 16 of their papers we have counts for
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stat.OT2026
Differentially Private Learning of Markov Chains: Near-Optimal Sample Complexity for Item and User Level Privacy
Devansh Gupta, Cristóbal Guzman, Enayat Ullah
Sequential data such as documents, conversations, and trajectories are naturally partitioned across users, motivating privacy guarantees that protect each user's entire trajectory.…
cs.LG2026
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…