4 papers
Reliable Scale-Free Bandits via Empirical Likelihood
Pratham Gupta, Alhad Sethi, Shubhada Agrawal
We study stochastic bandits without knowledge of reward scales or tail parameters. Almost-sure regret guarantees describe eventual learning but do not quantify expected performance…
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 mat…
Perfect Secret Key Generation for a class of Hypergraphical Sources
Manuj Mukherjee, Sagnik Chatterjee, Alhad Sethi
Nitinawarat and Narayan proposed a perfect secret key generation scheme for the so-called \emph{pairwise independent network (PIN) model} by exploiting the combinatorial properties…
Generalization Bounds for Dependent Data using Online-to-Batch Conversion
Sagnik Chatterjee, Manuj Mukherjee, Alhad Sethi
In this work, we upper bound the generalization error of batch learning algorithms trained on samples drawn from a mixing stochastic process (i.e., a dependent data source) both in…