2 citations · 4 across the 7 of their papers we have counts for
7 papers
Training and Evaluating Diffusion Policies with Long Context Lengths
Abhinav Agarwal, Adam Wei, Taylan Kargin +6
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only…
Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Adam Wei, Nicholas Pfaff, Thomas Cohn +4
We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specific robot data is expensive and…
LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
Anand Shah, Kehang Zhu, Yanchen Jiang +4
Training on vast amounts of human-generated data has motivated growing interest in using large language models (LLMs) to simulate human behavior. We ask which features of human beh…
Gradient dynamics for low-rank fine-tuning beyond kernels
Arif Kerem Dayi, Sitan Chen
LoRA has emerged as one of the de facto methods for fine-tuning foundation models with low computational cost and memory footprint. The idea is to only train a low-rank perturbatio…
Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust
Arif Kerem Dayı, Orhan Eren Akgün, Stephanie Gil +2
In this work, we introduce the Resilient Projected Push-Pull (RP3) algorithm designed for distributed optimization in multi-agent cyber-physical systems with directed communication…
Projected Push-Pull For Distributed Constrained Optimization Over Time-Varying Directed Graphs (extended version)
Orhan Eren Akgün, Arif Kerem Dayı, Stephanie Gil +1
We introduce the Projected Push-Pull algorithm that enables multiple agents to solve a distributed constrained optimization problem with private cost functions and global constrain…
Learning Trust Over Directed Graphs in Multiagent Systems (extended version)
Orhan Eren Akgün, Arif Kerem Dayı, Stephanie Gil +1
We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results fo…