6 papers
An Empirical Bayes Perspective on Heteroskedastic Mean Estimation
Yanjun Han, Abhishek Shetty, Jacob Shkrob
Towards understanding the fundamental limits of estimation from data of varied quality, we study the problem of estimating a mean parameter from heteroskedastic Gaussian observatio…
Partition Function Estimation under Bounded f-Divergence
Adam Block, Abhishek Shetty
We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio for a target distribution. Wh…
Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given data sources, the goal is to…
Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness
Moïse Blanchard, Abhishek Shetty, Alexander Rakhlin
Understanding minimal assumptions that enable learning and generalization is perhaps the central question of learning theory. Several celebrated results in statistical learning the…
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks
Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy
We revisit online binary classification by shifting the focus from competing with the best-in-class binary loss to competing against relaxed benchmarks that capture smoothed notion…
Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective
Adam Block, Abhishek Shetty
In order to develop practical and efficient algorithms while circumventing overly pessimistic computational lower bounds, recent work has been interested in developing oracle-effic…