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Found 15 papers, 1 papers with code
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
We study the problem of learning under arbitrary distribution shift, where the learner is trained on a labeled set from one distribution but evaluated on a different, potentially adversarially generated test distribution.
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.
Omnipredictors for Regression and the Approximate Rank of Convex Functions
An \textit{omnipredictor} for a class $\mathcal L$ of loss functions and a class $\mathcal C$ of hypotheses is a predictor whose predictions incur less expected loss than the best hypothesis in $\mathcal C$ for every loss in $\mathcal L$.
Smooth Nash Equilibria: Algorithms and Complexity
We show that both weak and strong $\sigma$-smooth Nash equilibria have superior computational properties to Nash equilibria: when $\sigma$ as well as an approximation parameter $\epsilon$ and the number of players are all constants, there is a constant-time randomized algorithm to find a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in normal-form games.
Adversarial Resilience in Sequential Prediction via Abstention
We study the problem of sequential prediction in the stochastic setting with an adversary that is allowed to inject clean-label adversarial (or out-of-distribution) examples.
Optimal PAC Bounds Without Uniform Convergence
In this paper, we address this issue by providing optimal high probability risk bounds through a framework that surpasses the limitations of uniform convergence arguments.
The One-Inclusion Graph Algorithm is not Always Optimal
In one of the first COLT open problems, Warmuth conjectured that this prediction strategy always implies an optimal high probability bound on the risk, and hence is also an optimal PAC algorithm.
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries
For the smoothed analysis setting, our results give the first oracle-efficient algorithm for online learning with smoothed adversaries [HRS22].
Distribution Compression in Near-linear Time
Near-optimal thinning procedures achieve this goal by sampling $n$ points from a Markov chain and identifying $\sqrt{n}$ points with $\widetilde{\mathcal{O}}(1/\sqrt{n})$ discrepancy to $\mathbb{P}$.
Smoothed Analysis with Adaptive Adversaries
-Online discrepancy minimization: We consider the online Koml\'os problem, where the input is generated from an adaptive sequence of $\sigma$-smooth and isotropic distributions on the $\ell_2$ unit ball.
Smoothed Analysis of Online and Differentially Private Learning
Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms.
Effect of Activation Functions on the Training of Overparametrized Neural Nets
In the present paper, we provide theoretical results about the effect of activation function on the training of highly overparametrized 2-layer neural networks.
Non-Gaussian Component Analysis using Entropy Methods
NGCA is also related to dimension reduction and to other data analysis problems such as ICA.
Exponential Weights on the Hypercube in Polynomial Time
This problem is equivalent to OLO on the $\{0, 1\}^n$ hypercube.
