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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.
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.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.
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.
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled data is limited but one has access to a large pool of unlabeled data.
Weighted Distillation with Unlabeled Examples
Our method is hyper-parameter free, data-agnostic, and simple to implement.
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent
For the ReLU activation, we give an efficient algorithm with sample complexity $\tilde{O}(d\, \polylog(1/\epsilon))$.
Efficient Algorithms for Learning from Coarse Labels
Our main algorithmic result is that essentially any problem learnable from fine grained labels can also be learned efficiently when the coarse data are sufficiently informative.
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.
Convergence and Sample Complexity of SGD in GANs
Our main result is that by training the Generator together with a Discriminator according to the Stochastic Gradient Descent-Ascent iteration proposed by Goodfellow et al. yields a Generator distribution that approaches the target distribution of $f_*$.
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise
{\em We give the first polynomial-time algorithm for this fundamental learning problem.}
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.
Efficient Truncated Statistics with Unknown Truncation
Our main result is a computationally and sample efficient algorithm for estimating the parameters of the Gaussian under arbitrary unknown truncation sets whose performance decays with a natural measure of complexity of the set, namely its Gaussian surface area.
Learning Powers of Poisson Binomial Distributions
The $k$'th power of this distribution, for $k$ in a range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
