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Found 8 papers, 1 papers with code
Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap
In this paper, we re-visit the task of off-policy evaluation in MDPs under a weaker notion of distributional overlap, and introduce a class of truncated doubly robust (TDR) estimators which we find to perform well in this setting.
A Model-free Closeness-of-influence Test for Features in Supervised Learning
Ideally, it is desired to understand how a set of collected features combine together and influence the response value, but this problem is notoriously difficult, due to the high-dimensionality of data and limited number of labeled data points, among many others.
GRASP: A Goodness-of-Fit Test for Classification Learning
Performance of classifiers is often measured in terms of average accuracy on test data.
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff
We develop a theory to show that the low-dimensional manifold structure allows one to obtain models that are nearly optimal with respect to both, the standard accuracy and the robust accuracy measures.
Error-Correction for Sparse Support Recovery Algorithms
Experimental results with random Gaussian design matrices show that LiRE substantially reduces the number of measurements needed for perfect support recovery via Compressive Sampling Matching Pursuit, Basis Pursuit (BP), and OMP.
Information Theory Information Theory
Fundamental Tradeoffs in Distributionally Adversarial Training
We study the tradeoff between standard risk and adversarial risk and derive the Pareto-optimal tradeoff, achievable over specific classes of models, in the infinite data limit with features dimension kept fixed.
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis
Adaptive collection of data is commonplace in applications throughout science and engineering.
Bounds on the Approximation Power of Feedforward Neural Networks
The approximation power of general feedforward neural networks with piecewise linear activation functions is investigated.
