research
∙
05/16/2023
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Optimization problems over dynamic networks have been extensively studie...
share
research
∙
04/21/2023
Noise-Reuse in Online Evolution Strategies
Online evolution strategies have become an attractive alternative to aut...
research
∙
12/14/2022
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
We consider the sequential decision-making problem of making proactive r...
research
∙
12/08/2022
General-Purpose In-Context Learning by Meta-Learning Transformers
Modern machine learning requires system designers to specify aspects of ...
research
∙
12/02/2022
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
We propose a learning-based robust predictive control algorithm that com...
research
∙
11/17/2022
VeLO: Training Versatile Learned Optimizers by Scaling Up
While deep learning models have replaced hand-designed features across m...
research
∙
09/23/2022
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Learning-based behavior prediction methods are increasingly being deploy...
research
∙
09/22/2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
Learned optimizers – neural networks that are trained to act as optimize...
research
∙
03/22/2022
Practical tradeoffs between memory, compute, and performance in learned optimizers
Optimization plays a costly and crucial role in developing machine learn...
research
∙
11/11/2021
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
We identify an issue in recent approaches to learning-based control that...
research
∙
07/29/2021
Bayesian Embeddings for Few-Shot Open World Recognition
As autonomous decision-making agents move from narrow operating environm...
research
∙
04/23/2021
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Autonomous mobility-on-demand (AMoD) systems represent a rapidly develop...
research
∙
04/16/2021
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
We propose a learning-based robust predictive control algorithm that can...
research
∙
04/06/2021
Particle MPC for Uncertain and Learning-Based Control
As robotic systems move from highly structured environments to open worl...
research
∙
11/09/2020
Sparse Longitudinal Representations of Electronic Health Record Data for the Early Detection of Chronic Kidney Disease in Diabetic Patients
Chronic kidney disease (CKD) is a gradual loss of renal function over ti...
research
∙
08/26/2020
Safe Model-Based Meta-Reinforcement Learning: A Sequential Exploration-Exploitation Framework
Safe deployment of autonomous robots in diverse environments requires ag...
research
∙
06/18/2020
Deep Reinforcement Learning amidst Lifelong Non-Stationarity
As humans, our goals and our environment are persistently changing throu...
research
∙
12/18/2019
Continuous Meta-Learning without Tasks
Meta-learning is a promising strategy for learning to efficiently learn ...
research
∙
02/15/2019
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Today's robotic systems are increasingly turning to computationally expe...
research
∙
01/09/2019
Robust and Adaptive Planning under Model Uncertainty
Planning under model uncertainty is a fundamental problem across many ap...
research
∙
07/24/2018
Meta-Learning Priors for Efficient Online Bayesian Regression
Gaussian Process (GP) regression has seen widespread use in robotics due...
research
∙
06/16/2018