Papers
1,396 papers found
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks
Itay M Safran, Gilad Yehudai, Ohad Shamir
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
Waïss Azizian, Franck Iutzeler, Jérôme Malick et al.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
Blake E Woodworth, Brian Bullins, Ohad Shamir et al.
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model
Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas et al.
The Sample Complexity of Robust Covariance Testing
Ilias Diakonikolas, Daniel M. Kane
The Sparse Vector Technique, Revisited
Haim Kaplan, Yishay Mansour, Uri Stemmer
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
Yair Carmon, Arun Jambulapati, Yujia Jin et al.
Towards a Dimension-Free Understanding of Adaptive Linear Control
Juan C Perdomo, Max Simchowitz, Alekh Agarwal et al.
Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle
Pan Peng, Jiapeng Zhang
Weak learning convex sets under normal distributions
Anindya De, Rocco Servedio
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
Niladri S. Chatterji, Philip M. Long, Peter Bartlett
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback
Chung-Wei Lee, Haipeng Luo, Mengxiao Zhang
A Corrective View of Neural Networks: Representation, Memorization and Learning
Guy Bresler, Dheeraj Nagaraj
Active Learning for Identification of Linear Dynamical Systems
Andrew Wagenmaker, Kevin Jamieson
Active Local Learning
Arturs Backurs, Avrim Blum, Neha Gupta
Adaptive Submodular Maximization under Stochastic Item Costs
Srinivasan Parthasarathy
A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates
Zhixian Lei, Kyle Luh, Prayaag Venkat et al.
A Greedy Anytime Algorithm for Sparse PCA
Guy Holtzman, Adam Soffer, Dan Vilenchik
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis et al.
An $\widetilde\mathcal{O}(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints
Yin Tat Lee, Swati Padmanabhan
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
Pritish Kamath, Omar Montasser, Nathan Srebro
Approximation Schemes for ReLU Regression
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar et al.
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices
Cédric Gerbelot, Alia Abbara, Florent Krzakala
Balancing Gaussian vectors in high dimension
Paxton Turner, Raghu Meka, Philippe Rigollet