5 papers · 1 filter
Non-partitioned e-detectors for nonparametric sequential change detection
Aytijhya Saha, Aaditya Ramdas
We study the problem of sequential change detection over a general class of probability distributions (), where both the pre-change and post-change distributions are un…
Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Aytijhya Saha, Stephen Bates, Devavrat Shah
Unobserved confounding is a fundamental challenge for estimating causal effects. To address unobserved confounding, recent literature has turned to two different approaches -- prox…
Optimal e-variables under constraints
Aytijhya Saha, Aaditya Ramdas
E-variables enable safe and anytime-valid inference, with log-optimal e-variables given by the likelihood ratio of the least favorable distributions (LFDs) when they exist in compo…
Density estimation with atoms, and functional estimation for mixed discrete-continuous data
Aytijhya Saha, Aaditya Ramdas
In classical density (or density-functional) estimation, it is standard to assume that the underlying distribution has a density with respect to the Lebesgue measure. However, when…
Huber-robust likelihood ratio tests for composite nulls and alternatives
Aytijhya Saha, Aaditya Ramdas
We propose an e-value based framework for testing arbitrary composite nulls against composite alternatives, when an fraction of the data can be arbitrarily corrupted. Our tests…