Kwangjun Ahn | DeepAI

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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...

0 Haoyuan Sun, et al. ∙

∙ 06/02/2023

Smooth Model Predictive Control with Applications to Statistical Learning

Statistical learning theory and high dimensional statistics have had a t...

0 Kwangjun Ahn, et al. ∙

∙ 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...

0 Kwangjun Ahn, et al. ∙

∙ 05/25/2023

How to escape sharp minima

Modern machine learning applications have seen a remarkable success of o...

0 Kwangjun Ahn, et al. ∙

∙ 12/14/2022

Learning threshold neurons via the "edge of stability"

Existing analyses of neural network training often operate under the unr...

0 Kwangjun Ahn, et al. ∙

∙ 10/17/2022

Model Predictive Control via On-Policy Imitation Learning

In this paper, we leverage the rapid advances in imitation learning, a t...

0 Kwangjun Ahn, et al. ∙

∙ 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...

0 Youngjae Min, et al. ∙

∙ 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...

0 Haoyuan Sun, et al. ∙

∙ 04/03/2022

Understanding the unstable convergence of gradient descent

Most existing analyses of (stochastic) gradient descent rely on the cond...

0 Kwangjun Ahn, et al. ∙

∙ 02/09/2022

Reproducibility in Optimization: Theoretical Framework and Limits

We initiate a formal study of reproducibility in optimization. We define...

5 Kwangjun Ahn, et al. ∙

∙ 01/31/2022

Agnostic Learnability of Halfspaces via Logistic Loss

We investigate approximation guarantees provided by logistic regression ...

0 Ziwei Ji, et al. ∙

∙ 02/01/2021

Riemannian Perspective on Matrix Factorization

We study the non-convex matrix factorization approach to matrix completi...

0 Kwangjun Ahn, et al. ∙

∙ 12/23/2020

Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm

Conventional wisdom in the sampling literature, backed by a popular diff...

0 Sinho Chewi, et al. ∙

∙ 10/30/2020

Efficient constrained sampling via the mirror-Langevin algorithm

We propose a new discretization of the mirror-Langevin diffusion and giv...

0 Kwangjun Ahn, et al. ∙

∙ 08/08/2020

A simpler strong refutation of random k-XOR

Strong refutation of random CSPs is a fundamental question in theoretica...

0 Kwangjun Ahn, et al. ∙

∙ 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...

0 Kwangjun Ahn, et al. ∙

∙ 05/17/2020

From Proximal Point Method to Nesterov's Acceleration

The proximal point method (PPM) is a fundamental method in optimization ...

0 Kwangjun Ahn, et al. ∙

∙ 04/18/2020

On Tight Convergence Rates of Without-replacement SGD

For solving finite-sum optimization problems, SGD without replacement sa...

0 Kwangjun Ahn, et al. ∙

∙ 01/24/2020

From Nesterov's Estimate Sequence to Riemannian Acceleration

We propose the first global accelerated gradient method for Riemannian m...

0 Kwangjun Ahn, et al. ∙

∙ 05/23/2018

Hypergraph Spectral Clustering in the Weighted Stochastic Block Model

Spectral clustering is a celebrated algorithm that partitions objects ba...

0 Kwangjun Ahn, et al. ∙

∙ 10/14/2017

Computing the maximum matching width is NP-hard

The maximum matching width is a graph width parameter that is defined on...

0 Kwangjun Ahn, et al. ∙

∙ 09/12/2017

Community Recovery in Hypergraphs

Community recovery is a central problem that arises in a wide variety of...

0 Kwangjun Ahn, et al. ∙

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