23 citations · 53 across the 12 of their papers we have counts for
12 papers
Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
Aaron Havens, Brian Karrer, Neta Shaul
Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead of data samples. Bec…
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan +10
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…
Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra
Darioush Kevian, Usman Syed, Xingang Guo +5
In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra in solving undergraduate-level contro…
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Patricia Pauli, Aaron Havens, Alexandre Araujo +4
Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab…
A Unified Algebraic Perspective on Lipschitz Neural Networks
Alexandre Araujo, Aaron Havens, Blaise Delattre +2
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant. The goal is to increase and sometimes guarantee the robu…
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control
Aaron Havens, Darioush Keivan, Peter Seiler +2
Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback systems with large-scale neural network (NN) policies and/or high-dimensional sensing…
Model-Free Synthesis via Adversarial Reinforcement Learning
Darioush Keivan, Aaron Havens, Peter Seiler +2
Motivated by the recent empirical success of policy-based reinforcement learning (RL), there has been a research trend studying the performance of policy-based RL methods on standa…
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
Aaron Havens, Girish Chowdhary
As deep learning becomes more prevalent for prediction and control of real physical systems, it is important that these overparameterized models are consistent with physically plau…
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions
Aaron Havens, Bin Hu
When applying imitation learning techniques to fit a policy from expert demonstrations, one can take advantage of prior stability/robustness assumptions on the expert's policy and…
Learning Latent State Spaces for Planning through Reward Prediction
Aaron Havens, Yi Ouyang, Prabhat Nagarajan +1
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing…
Learning to Cope with Adversarial Attacks
Xian Yeow Lee, Aaron Havens, Girish Chowdhary +1
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-ph…
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron J. Havens, Zhanhong Jiang, Soumik Sarkar
The growing prospect of deep reinforcement learning (DRL) being used in cyber-physical systems has raised concerns around safety and robustness of autonomous agents. Recent work on…