26 citations · 94 across the 25 of their papers we have counts for
8 papers · 1 filter
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
Rohan Sinha, James Harrison, Spencer M. Richards +1
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally…
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Tobias Enders, James Harrison, Marco Pavone +1
We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on dema…
General-Purpose In-Context Learning by Meta-Learning Transformers
Louis Kirsch, James Harrison, Jascha Sohl-Dickstein +1
Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, i…
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
James Harrison, Luke Metz, Jascha Sohl-Dickstein
Learned optimizers -- neural networks that are trained to act as optimizers -- have the potential to dramatically accelerate training of machine learning models. However, even when…
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Boris Ivanovic, James Harrison, Marco Pavone
Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e.g., in fleets of self-driving vehicles, which are beginning to commer…