10 citations · 31 across the 12 of their papers we have counts for
Showing 2023 · stat.MLShow all
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stat.ML2023
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
Adam Block, Alexander Rakhlin, Max Simchowitz
Smoothed online learning has emerged as a popular framework to mitigate the substantial loss in statistical and computational complexity that arises when one moves from classical t…
stat.ML2023★ 1 cited
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning
Adam Block, Yury Polyanskiy
Suppose we are given access to independent samples from distribution and we wish to output one of them with the goal of making the output distributed as close as possible t…
stat.ML2023
Smoothed Online Learning for Prediction in Piecewise Affine Systems
Adam Block, Max Simchowitz, Russ Tedrake
The problem of piecewise affine (PWA) regression and planning is of foundational importance to the study of online learning, control, and robotics, where it provides a theoreticall…