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08/05/2023
Learning physics-based reduced-order models from data using nonlinear manifolds
We present a novel method for learning reduced-order models of dynamical...
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02/08/2023
Cut your Losses with Squentropy
Nearly all practical neural models for classification are trained using ...
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01/26/2023
A Fully First-Order Method for Stochastic Bilevel Optimization
We consider stochastic unconstrained bilevel optimization problems when ...
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09/19/2022
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Bilevel optimization (BO) is useful for solving a variety of important m...
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05/04/2022
Operator inference for non-intrusive model reduction with nonlinear manifolds
This paper proposes a novel approach for learning a data-driven quadrati...
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10/06/2021
On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime
Finding the optimal configuration of parameters in ResNet is a nonconvex...
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05/30/2021
Overparameterization of deep ResNet: zero loss and mean-field analysis
Finding parameters in a deep neural network (NN) that fit training data ...
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05/12/2020
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the L_1-regularized maximum likelihood estimator/estimation (ML...
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08/18/2019
Computing Estimators of Dantzig Selector type via Column and Constraint Generation
We consider a class of linear-programming based estimators in reconstruc...
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05/22/2019
Convergence and Margin of Adversarial Training on Separable Data
Adversarial training is a technique for training robust machine learning...
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01/08/2019
Bilinear Bandits with Low-rank Structure
We introduce the bilinear bandit problem with low-rank structure where a...
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06/11/2018
ATOMO: Communication-efficient Learning via Atomic Sparsification
Distributed model training suffers from communication overheads due to f...
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06/10/2018
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Techniques for reducing the variance of gradient estimates used in stoch...
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05/18/2018
Blended Conditional Gradients: the unconditioning of conditional gradients
We present a blended conditional gradient approach for minimizing a smoo...
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11/06/2017
Online Learning for Changing Environments using Coin Betting
A key challenge in online learning is that classical algorithms can be s...
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10/14/2016
Improved Strongly Adaptive Online Learning using Coin Betting
This paper describes a new parameter-free online learning algorithm for ...
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04/23/2014
