Foundations of Learning from Positive Samples by Dr. Anay Mehrotra

Abstract: What can be learned from data? Traditional answers to this question assume an idealized data-generating process where test and training distributions are symmetric, which is rarely the case in applications.
In this talk, we will revisit this question for positive-only learning, a setting where only positive examples are observed. This is a challenging problem arising in bioinformatics and causal inference. Classical results show that learning is impossible in general. However, we will show that the hard instances are fragile: under a smoothed analysis, which rules out pathological distributions, efficient learning is possible. The same ideas also lead to faster and more general algorithms for estimation and regression from truncated data, a foundational problem in statistics.
The talk is based on joint work with Shai Ben-David, Yang Cai, Constantine Caramanis, Alkis Kalavasis, Alex Kouridakis, Jane H. Lee, Katerina Mamali, Farnam Mansouri, and Manolis Zampetakis.

Speaker bio: Anay Mehrotra (https://anaymehrotra.com) is a Motwani Postdoctoral Fellow at Stanford working with Amin Saberi. He recently completed his PhD at Yale, advised by Amin Karbasi and Manolis Zampetakis. His work has received the Best Paper Award at COLT and the Sri Binay Kumar Sinha Award from IIT Kanpur, was selected for the TCS4All Rising Star session at STOC 2026, and has been featured in WIRED.