517 citations · 1.1k across the 28 of their papers we have counts for
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The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
Nikhil Ghosh, Song Mei, Bin Yu
To understand how deep learning works, it is crucial to understand the training dynamics of neural networks. Several interesting hypotheses about these dynamics have been made base…
Learning with convolution and pooling operations in kernel methods
Theodor Misiakiewicz, Song Mei
Recent empirical work has shown that hierarchical convolutional kernels inspired by convolutional neural networks (CNNs) significantly improve the performance of kernel methods in…
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Ziang Song, Song Mei, Yu Bai
Multi-agent reinforcement learning has made substantial empirical progresses in solving games with a large number of players. However, theoretically, the best known sample complexi…
Understanding the Under-Coverage Bias in Uncertainty Estimation
Yu Bai, Song Mei, Huan Wang +1
Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input. It is frequen…
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
Michael Celentano, Zhou Fan, Song Mei
We study mean-field variational Bayesian inference using the TAP approach, for Z2-synchronization as a prototypical example of a high-dimensional Bayesian model. We show that for a…
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Zitong Yang, Yu Bai, Song Mei
Recent work showed that there could be a large gap between the classical uniform convergence bound and the actual test error of zero-training-error predictors (interpolators) such…
Learning with invariances in random features and kernel models
Song Mei, Theodor Misiakiewicz, Andrea Montanari
A number of machine learning tasks entail a high degree of invariance: the data distribution does not change if we act on the data with a certain group of transformations. For inst…
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Yu Bai, Song Mei, Huan Wang +1
Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident. It…