Search Results for author:
Found 14 papers, 0 papers with code
On the Performance of Empirical Risk Minimization with Smoothed Data
In order to circumvent statistical and computational hardness results in sequential decision-making, recent work has considered smoothed online learning, where the distribution of data at each time is assumed to have bounded likeliehood ratio with respect to a base measure when conditioned on the history.
Oracle-Efficient Differentially Private Learning with Public Data
Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms.
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
This work studies training instabilities of behavior cloning with deep neural networks.
Efficient Model-Free Exploration in Low-Rank MDPs
A major challenge in reinforcement learning is to develop practical, sample-efficient algorithms for exploration in high-dimensional domains where generalization and function approximation is required.
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
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 to adversarial learning.
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning
Suppose we are given access to $n$ independent samples from distribution $\mu$ and we wish to output one of them with the goal of making the output distributed as close as possible to a target distribution $\nu$.
Smoothed Online Learning for Prediction in Piecewise Affine Systems
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 theoretically and empirically tractable setting to study systems undergoing sharp changes in the dynamics.
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
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 to select contexts with density bounded by 1/{\sigma} with respect to a known measure {\mu}.
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions.
Smoothed Online Learning is as Easy as Statistical Learning
We then prove a lower bound on the oracle complexity of any proper learning algorithm, which matches the oracle-efficient upper bounds up to a polynomial factor, thus demonstrating the existence of a statistical-computational gap in smooth online learning.
Intrinsic Dimension Estimation Using Wasserstein Distances
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.
Majorizing Measures, Sequential Complexities, and Online Learning
We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity.
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Recently, the task of image generation has attracted much attention.
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
We show that both DAE and DSM provide estimates of the score of the Gaussian smoothed population density, allowing us to apply the machinery of Empirical Processes.
