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06/24/2023
A Unified Approach to Controlling Implicit Regularization via Mirror Descent
Inspired by the remarkable success of deep neural networks, there has be...
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06/02/2023
Smooth Model Predictive Control with Applications to Statistical Learning
Statistical learning theory and high dimensional statistics have had a t...
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06/01/2023
Transformers learn to implement preconditioned gradient descent for in-context learning
Motivated by the striking ability of transformers for in-context learnin...
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05/25/2023
How to escape sharp minima
Modern machine learning applications have seen a remarkable success of o...
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12/14/2022
Learning threshold neurons via the "edge of stability"
Existing analyses of neural network training often operate under the unr...
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10/17/2022
Model Predictive Control via On-Policy Imitation Learning
In this paper, we leverage the rapid advances in imitation learning, a t...
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07/28/2022
One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares
While deep neural networks are capable of achieving state-of-the-art per...
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05/25/2022
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently
Driven by the empirical success and wide use of deep neural networks, un...
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04/03/2022
Understanding the unstable convergence of gradient descent
Most existing analyses of (stochastic) gradient descent rely on the cond...
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02/09/2022
Reproducibility in Optimization: Theoretical Framework and Limits
We initiate a formal study of reproducibility in optimization. We define...
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01/31/2022
Agnostic Learnability of Halfspaces via Logistic Loss
We investigate approximation guarantees provided by logistic regression ...
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02/01/2021
Riemannian Perspective on Matrix Factorization
We study the non-convex matrix factorization approach to matrix completi...
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12/23/2020
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diff...
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10/30/2020
Efficient constrained sampling via the mirror-Langevin algorithm
We propose a new discretization of the mirror-Langevin diffusion and giv...
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08/08/2020
A simpler strong refutation of random k-XOR
Strong refutation of random CSPs is a fundamental question in theoretica...
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06/12/2020
SGD with shuffling: optimal rates without component convexity and large epoch requirements
We study without-replacement SGD for solving finite-sum optimization pro...
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05/17/2020
From Proximal Point Method to Nesterov's Acceleration
The proximal point method (PPM) is a fundamental method in optimization ...
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04/18/2020
On Tight Convergence Rates of Without-replacement SGD
For solving finite-sum optimization problems, SGD without replacement sa...
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01/24/2020
From Nesterov's Estimate Sequence to Riemannian Acceleration
We propose the first global accelerated gradient method for Riemannian m...
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05/23/2018
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
Spectral clustering is a celebrated algorithm that partitions objects ba...
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10/14/2017
Computing the maximum matching width is NP-hard
The maximum matching width is a graph width parameter that is defined on...
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09/12/2017
