10 citations · 31 across the 12 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Adam Block, Max Simchowitz
Due to the drastic gap in complexity between sequential and batch statistical learning, recent work has studied a smoothed sequential learning setting, where Nature is constrained…
Smoothed Online Learning is as Easy as Statistical Learning
Adam Block, Yuval Dagan, Noah Golowich +1
Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversa…
Intrinsic Dimension Estimation Using Wasserstein Distances
Adam Block, Zeyu Jia, Yury Polyanskiy +1
It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i.e., the manifold hypothesis holds. A natu…
Majorizing Measures, Sequential Complexities, and Online Learning
Adam Block, Yuval Dagan, Sasha Rakhlin
We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity. We relate majorizing measures to the notion of fractional c…
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Adam Block, Youssef Mroueh, Alexander Rakhlin +1
Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dyna…
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
Adam Block, Youssef Mroueh, Alexander Rakhlin
We study convergence of a generative modeling method that first estimates the score function of the distribution using Denoising Auto-Encoders (DAE) or Denoising Score Matching (DS…