research
∙
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...
share
research
∙
05/31/2023
Representation Reliability and Its Impact on Downstream Tasks
Self-supervised pre-trained models extract general-purpose representatio...
research
∙
05/25/2023
SketchOGD: Memory-Efficient Continual Learning
When machine learning models are trained continually on a sequence of ta...
research
∙
04/20/2023
On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers
We consider the fundamental problem of solving a large-scale system of l...
research
∙
03/21/2023
Online Learning for Equilibrium Pricing in Markets under Incomplete Information
The study of market equilibria is central to economic theory, particular...
research
∙
03/06/2023
Data-Driven Control with Inherent Lyapunov Stability
Recent advances in learning-based control leverage deep function approxi...
research
∙
02/06/2023
Learning Control-Oriented Dynamical Structure from Data
Even for known nonlinear dynamical systems, feedback controller synthesi...
research
∙
10/04/2022
Uncertainty-Aware Meta-Learning for Multimodal Task Distributions
Meta-learning or learning to learn is a popular approach for learning ne...
research
∙
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...
research
∙
07/19/2022
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance
The superior performance of some of today's state-of-the-art deep learni...
research
∙
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...
research
∙
04/14/2022
Control-oriented meta-learning
Real-time adaptation is imperative to the control of robots operating in...
research
∙
03/31/2022
Online Learning for Traffic Routing under Unknown Preferences
In transportation networks, users typically choose routes in a decentral...
research
∙
03/06/2022
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
Verifying that input-output relationships of a neural network conform to...
research
∙
02/22/2022
Explicit Regularization via Regularizer Mirror Descent
Despite perfectly interpolating the training data, deep neural networks ...
research
∙
03/07/2021
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Real-time adaptation is imperative to the control of robots operating in...
research
∙
02/24/2021
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
In order to safely deploy Deep Neural Networks (DNNs) within the percept...
research
∙
10/15/2019
Orthogonal Gradient Descent for Continual Learning
Neural networks are achieving state of the art and sometimes super-human...
research
∙
06/10/2019
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
Most modern learning problems are highly overparameterized, meaning that...
research
∙
04/03/2019
A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality
Stochastic mirror descent (SMD) is a fairly new family of algorithms tha...
research
∙
06/04/2018