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Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions

24 May 2024
Luca Arnaboldi
Yatin Dandi
Florent Krzakala
Luca Pesce
Ludovic Stephan
ArXivPDFHTML

Papers citing "Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions"

13 / 13 papers shown
Title
Survey on Algorithms for multi-index models
Survey on Algorithms for multi-index models
Joan Bruna
Daniel Hsu
18
0
0
07 Apr 2025
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions
Elisabetta Cornacchia
Dan Mikulincer
Elchanan Mossel
49
0
0
10 Feb 2025
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Ziang Chen
Rong Ge
MLT
51
1
0
10 Jan 2025
Gradient dynamics for low-rank fine-tuning beyond kernels
Gradient dynamics for low-rank fine-tuning beyond kernels
Arif Kerem Dayi
Sitan Chen
67
1
0
23 Nov 2024
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Berfin Simsek
Amire Bendjeddou
Daniel Hsu
32
0
0
13 Nov 2024
Pretrained transformer efficiently learns low-dimensional target
  functions in-context
Pretrained transformer efficiently learns low-dimensional target functions in-context
Kazusato Oko
Yujin Song
Taiji Suzuki
Denny Wu
23
4
0
04 Nov 2024
Robust Feature Learning for Multi-Index Models in High Dimensions
Robust Feature Learning for Multi-Index Models in High Dimensions
Alireza Mousavi-Hosseini
Adel Javanmard
Murat A. Erdogdu
OOD
AAML
37
1
0
21 Oct 2024
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics
Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics
Alireza Mousavi-Hosseini
Denny Wu
Murat A. Erdogdu
MLT
AI4CE
27
6
0
14 Aug 2024
Neural network learns low-dimensional polynomials with SGD near the
  information-theoretic limit
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee
Kazusato Oko
Taiji Suzuki
Denny Wu
MLT
71
20
0
03 Jun 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer
  Networks: Breaking the Curse of Information and Leap Exponents
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
Yatin Dandi
Emanuele Troiani
Luca Arnaboldi
Luca Pesce
Lenka Zdeborová
Florent Krzakala
MLT
51
24
0
05 Feb 2024
SGD learning on neural networks: leap complexity and saddle-to-saddle
  dynamics
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics
Emmanuel Abbe
Enric Boix-Adserà
Theodor Misiakiewicz
FedML
MLT
76
72
0
21 Feb 2023
Learning Single-Index Models with Shallow Neural Networks
Learning Single-Index Models with Shallow Neural Networks
A. Bietti
Joan Bruna
Clayton Sanford
M. Song
147
65
0
27 Oct 2022
Trainability and Accuracy of Neural Networks: An Interacting Particle
  System Approach
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Grant M. Rotskoff
Eric Vanden-Eijnden
49
114
0
02 May 2018
1