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Found 4 papers, 0 papers with code
Robustly Learning Single-Index Models via Alignment Sharpness
We give an efficient learning algorithm, achieving a constant factor approximation to the optimal loss, that succeeds under a range of distributions (including log-concave distributions) and a broad class of monotone and Lipschitz link functions.
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise
Our main result is a lower bound for Statistical Query (SQ) algorithms and low-degree polynomial tests suggesting that the quadratic dependence on $1/\epsilon$ in the sample complexity is inherent for computationally efficient algorithms.
Robustly Learning a Single Neuron via Sharpness
We study the problem of learning a single neuron with respect to the $L_2^2$-loss in the presence of adversarial label noise.
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
We introduce a novel potential function-based framework to study the convergence of standard methods for making the gradients small in smooth convex optimization and convex-concave min-max optimization.
