9 papers
Asymptotically Optimal Sequential Testing with Markovian Data
Alhad Sethi, Kavali Sofia Sagar, Shubhada Agrawal +2
We study one-sided and -correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov chain. The null hypothesis is that the unknown transition ma…
Cover meets Robbins while Betting on Bounded Data: Regret and Almost Sure Regret
Shubhada Agrawal, Aaditya Ramdas
Consider betting against a sequence of data in , where one is allowed to make any bet that is fair if the data have a conditional mean . Cover's universal por…
Almost sure null bankruptcy of testing-by-betting strategies
Hongjian Wang, Shubhada Agrawal, Aaditya Ramdas
The bounded mean betting procedure serves as a crucial interface between the domains of (1) sequential, anytime-valid statistical inference, and (2) online learning and portfolio s…
Eventually LIL Regret: Almost Sure Regret for a sub-Gaussian Mixture on Unbounded Data
Shubhada Agrawal, Aaditya Ramdas
We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``…
Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards
Subhodip Panda, Shubhada Agrawal
We study the tail behavior of regret in stochastic multi-armed bandits for algorithms that are asymptotically optimal in expectation. While minimizing expected regret is the classi…
Dual Representation of Minimum Divergence Under Integral Constraints
Shubhanshu Shekhar, Shubhada Agrawal
Minimum divergence problems under integral constraints appear throughout statistics and probability, including sequential inference, bandit theory, and distributionally robust opti…