Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

22 June 2020

Papers citing "Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent"

23 / 23 papers shown

Title
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Elisabetta Cornacchia Dan Mikulincer Elchanan Mossel 77 1 0 10 Feb 2025
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning Noah Golowich Ankur Moitra Dhruv Rohatgi OffRL 35 4 0 04 Apr 2024
On Single Index Models beyond Gaussian Data Joan Bruna Loucas Pillaud-Vivien Aaron Zweig 18 10 0 28 Jul 2023
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials Ilias Diakonikolas D. Kane 32 4 0 24 Jul 2023
A faster and simpler algorithm for learning shallow networks Sitan Chen Shyam Narayanan 41 7 0 24 Jul 2023
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models Alexandru Damian Eshaan Nichani Rong Ge Jason D. Lee MLT 42 33 0 18 May 2023
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely Nathan Srebro Gal Vardi 33 4 0 15 Feb 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 170 68 0 27 Oct 2022
Global Convergence of SGD On Two Layer Neural Nets Pulkit Gopalani Anirbit Mukherjee 26 5 0 20 Oct 2022
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 25 114 0 30 Jun 2022
Learning ReLU networks to high uniform accuracy is intractable Julius Berner Philipp Grohs F. Voigtlaender 32 4 0 26 May 2022
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks Sitan Chen Aravind Gollakota Adam R. Klivans Raghu Meka 24 30 0 10 Feb 2022
Lattice-Based Methods Surpass Sum-of-Squares in Clustering Ilias Zadik M. Song Alexander S. Wein Joan Bruna 17 35 0 07 Dec 2021
Efficiently Learning Any One Hidden Layer ReLU Network From Queries Sitan Chen Adam R. Klivans Raghu Meka MLAU MLT 45 8 0 08 Nov 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks Eran Malach Gilad Yehudai Shai Shalev-Shwartz Ohad Shamir 21 20 0 31 Jan 2021
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise Spencer Frei Yuan Cao Quanquan Gu FedML MLT 64 19 0 04 Jan 2021
Achieving Adversarial Robustness Requires An Active Teacher Chao Ma Lexing Ying 27 1 0 14 Dec 2020
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization Sitan Chen Xiaoxiao Li Zhao Song Danyang Zhuo 27 13 0 23 Nov 2020
Learning Deep ReLU Networks Is Fixed-Parameter Tractable Sitan Chen Adam R. Klivans Raghu Meka 22 36 0 28 Sep 2020
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent Matthew Brennan Guy Bresler Samuel B. Hopkins Jingkai Li T. Schramm 19 62 0 13 Sep 2020
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals Ilias Diakonikolas D. Kane Nikos Zarifis 19 66 0 29 Jun 2020