1 citations · 2 across the 34 of their papers we have counts for
12 papers · 1 filter
Active Learning of General Halfspaces: Label Queries vs Membership Queries
Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma
We study the problem of learning general (i.e., not necessarily homogeneous) halfspaces under the Gaussian distribution on in the presence of some form of query access. In th…
SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More
Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia +1
We study for random rectangular matrices. If is an matrix with independent Gaussian entries, we give a new family of…
Reliable Learning of Halfspaces under Gaussian Marginals
Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis
We study the problem of PAC learning halfspaces in the reliable agnostic model of Kalai et al. (2012). The reliable PAC model captures learning scenarios where one type of error is…
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas +1
We study the problem of learning a single neuron with respect to the -loss in the presence of adversarial distribution shifts, where the labels can be arbitrary, and the goa…
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
Puqian Wang, Nikos Zarifis, Ilias Diakonikolas +1
A single-index model (SIM) is a function of the form , where is a known link function and …
Sum-of-squares lower bounds for Non-Gaussian Component Analysis
Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang +1
Non-Gaussian Component Analysis (NGCA) is the statistical task of finding a non-Gaussian direction in a high-dimensional dataset. Specifically, given i.i.d.\ samples from a distrib…
Efficient Testable Learning of General Halfspaces with Adversarial Label Noise
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu +1
We study the task of testable learning of general -- not necessarily homogeneous -- halfspaces with adversarial label noise with respect to the Gaussian distribution. In the testab…
Online Learning of Halfspaces with Massart Noise
Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos +1
We study the task of online learning in the presence of Massart noise. Instead of assuming that the online adversary chooses an arbitrary sequence of labels, we assume that the con…
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis +2
We study the efficient learnability of low-degree polynomial threshold functions (PTFs) in the presence of a constant fraction of adversarial corruptions. Our main algorithmic resu…
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Shuyao Li, Yu Cheng, Ilias Diakonikolas +3
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learnin…
Robustly Learning Single-Index Models via Alignment Sharpness
Nikos Zarifis, Puqian Wang, Ilias Diakonikolas +1
We study the problem of learning Single-Index Models under the loss in the agnostic model. We give an efficient learning algorithm, achieving a constant factor approximatio…
How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
Xuefeng Du, Zhen Fang, Ilias Diakonikolas +1
Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing…