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Found 18 papers, 0 papers with code
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions.
Statistical Query Lower Bounds for Learning Truncated Gaussians
We study the problem of estimating the mean of an identity covariance Gaussian in the truncated setting, in the regime when the truncation set comes from a low-complexity family $\mathcal{C}$ of sets.
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.
Agnostically Learning Multi-index Models with Queries
In contrast, algorithms that rely only on random examples inherently require $d^{\mathrm{poly}(1/\epsilon)}$ samples and runtime, even for the basic problem of agnostically learning a single ReLU or a halfspace.
Self-Directed Linear Classification
In contrast, under a worst- or random-ordering, the number of mistakes must be at least $\Omega(d \log n)$, even when the points are drawn uniformly from the unit sphere and the learner only needs to predict the labels for $1\%$ of them.
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.
SQ Lower Bounds for Learning Bounded Covariance GMMs
In the special case where the separation is on the order of $k^{1/2}$, we additionally obtain fine-grained SQ lower bounds with the correct exponent.
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.
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent
For the ReLU activation, we give an efficient algorithm with sample complexity $\tilde{O}(d\, \polylog(1/\epsilon))$.
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution
We study the general problem and establish the following: For $\eta <1/2$, we give a learning algorithm for general halfspaces with sample and computational complexity $d^{O_{\eta}(\log(1/\gamma))}\mathrm{poly}(1/\epsilon)$, where $\gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \}$ is the bias of the target halfspace $f$.
Agnostic Proper Learning of Halfspaces under Gaussian Marginals
We study the problem of agnostically learning halfspaces under the Gaussian distribution.
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals
We study the problem of agnostic learning under the Gaussian distribution.
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise
{\em We give the first polynomial-time algorithm for this fundamental learning problem.}
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
We study the fundamental problems of agnostically learning halfspaces and ReLUs under Gaussian marginals.
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
For the case of positive coefficients, we give the first polynomial-time algorithm for this learning problem for $k$ up to $\tilde{O}(\sqrt{\log d})$.
Learning Halfspaces with Tsybakov Noise
In the Tsybakov noise model, each label is independently flipped with some probability which is controlled by an adversary.
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
We study the problem of agnostically learning homogeneous halfspaces in the distribution-specific PAC model.
Learning Halfspaces with Massart Noise Under Structured Distributions
We study the problem of learning halfspaces with Massart noise in the distribution-specific PAC model.
