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09/09/2023
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning
The rapid progress in machine learning in recent years has been based on...
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08/27/2023
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
Recent years witnessed the development of powerful generative models bas...
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05/29/2023
Learning Two-Layer Neural Networks, One (Giant) Step at a Time
We study the training dynamics of shallow neural networks, investigating...
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02/17/2023
Universality laws for Gaussian mixtures in generalized linear models
Let (x_i, y_i)_i=1,…,n denote independent samples from a general mixture...
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04/13/2022
Data-heterogeneity-aware Mixing for Decentralized Learning
Decentralized learning provides an effective framework to train machine ...
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11/07/2021
NeurInt : Learning to Interpolate through Neural ODEs
A wide range of applications require learning image generation models wh...
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11/06/2021
Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis
Spectral analysis is a powerful tool, decomposing any function into simp...
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06/25/2021
Implicit Gradient Alignment in Distributed and Federated Learning
A major obstacle to achieving global convergence in distributed and fede...
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12/04/2020
Model-Agnostic Learning to Meta-Learn
In this paper, we propose a learning algorithm that enables a model to q...
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06/15/2020
Generalized Adversarially Learned Inference
Allowing effective inference of latent vectors while training GANs can g...
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12/17/2019
