14 citations · 22 across the 11 of their papers we have counts for
14 papers
Reinforcement Learning for Flow-Matching Policies
Samuel Pfrommer, Yixiao Huang, Somayeh Sojoudi
Flow-matching policies have emerged as a powerful paradigm for generalist robotics. These models are trained to imitate an action chunk, conditioned on sensor observations and text…
Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication
Samuel Pfrommer, George Ma, Yixiao Huang +1
This work shows that normalization layers can facilitate a surprising degree of communication across the spatial dimensions of an input tensor. We study a toy localization task wit…
Approximately Gaussian Replicator Flows: Nonconvex Optimization as a Nash-Convergent Evolutionary Game
Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
This work leverages tools from evolutionary game theory to solve unconstrained nonconvex optimization problems. Specifically, we lift such a problem to an optimization over probabi…
Ranking Manipulation for Conversational Search Engines
Samuel Pfrommer, Yatong Bai, Tanmay Gautam +1
Major search engine providers are rapidly incorporating Large Language Model (LLM)-generated content in response to user queries. These conversational search engines operate by loa…
Transport of Algebraic Structure to Latent Embeddings
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Machine learning often aims to produce latent embeddings of inputs which lie in a larger, abstract mathematical space. For example, in the field of 3D modeling, subsets of Euclidea…
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks
Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack set…
Projected Randomized Smoothing for Certified Adversarial Robustness
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers. While randomized smoothing typically yields robust -ball certificates…
Initial State Interventions for Deconfounded Imitation Learning
Samuel Pfrommer, Yatong Bai, Hyunin Lee +1
Imitation learning suffers from causal confusion. This phenomenon occurs when learned policies attend to features that do not causally influence the expert actions but are instead…
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization
Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi
Conventional optimization methods in machine learning and controls rely heavily on first-order update rules. Selecting the right method and hyperparameters for a particular task of…
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Samuel Pfrommer, Brendon G. Anderson, Julien Piet +1
Recent works have introduced input-convex neural networks (ICNNs) as learning models with advantageous training, inference, and generalization properties linked to their convex str…
LQR Control with Sparse Adversarial Disturbances
Samuel Pfrommer, Somayeh Sojoudi
Recent developments in cyber-physical systems and event-triggered control have led to an increased interest in the impact of sparse disturbances on dynamical processes. We study Li…
Safe Reinforcement Learning with Chance-constrained Model Predictive Control
Samuel Pfrommer, Tanmay Gautam, Alec Zhou +1
Real-world reinforcement learning (RL) problems often demand that agents behave safely by obeying a set of designed constraints. We address the challenge of safe RL by coupling a s…
ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations
Samuel Pfrommer, Mathew Halm, Michael Posa
Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In…
Discriminability of Single-Layer Graph Neural Networks
Samuel Pfrommer, Fernando Gama, Alejandro Ribeiro
Network data can be conveniently modeled as a graph signal, where data values are assigned to the nodes of a graph describing the underlying network topology. Successful learning f…