9 papers
On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
Amirhossein Afsharrad, Amirhesam Abedsoltan, Ahmadreza Moradipari +1
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation prob…
Multi-Agent Stage-wise Conservative Linear Bandits
Amirhossein Afsharrad, Ahmadreza Moradipari, Sanjay Lall
In many real-world applications such as recommendation systems, multiple learning agents must balance exploration and exploitation while maintaining safety guarantees to avoid cata…
LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
Borna Khodabandeh, Amirabbas Afzali, Amirhossein Afsharrad +4
Visual encoders have become fundamental components in modern computer vision pipelines. However, ensuring robustness against adversarial perturbations remains a critical challenge.…
One Goal, Many Challenges: Robust Preference Optimization Amid Content-Aware and Multi-Source Noise
Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi +1
Large Language Models (LLMs) have made significant strides in generating human-like responses, largely due to preference alignment techniques. However, these methods often assume u…
Cooperative Multi-Agent Constrained Stochastic Linear Bandits
Amirhossein Afsharrad, Parisa Oftadeh, Ahmadreza Moradipari +1
In this study, we explore a collaborative multi-agent stochastic linear bandit setting involving a network of agents that communicate locally to minimize their collective regre…
Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers
Davide Celestini, Amirhossein Afsharrad, Daniele Gammelli +6
Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for so…
Adversarial Training of Two-Layer Polynomial and ReLU Activation Networks via Convex Optimization
Daniel Kuelbs, Sanjay Lall, Mert Pilanci
Training neural networks which are robust to adversarial attacks remains an important problem in deep learning, especially as heavily overparameterized models are adopted in safety…
Markov Decision Processes with Noisy State Observation
Amirhossein Afsharrad, Sanjay Lall
This paper addresses the challenge of a particular class of noisy state observations in Markov Decision Processes (MDPs), a common issue in various real-world applications. We focu…
Convex Methods for Constrained Linear Bandits
Amirhossein Afsharrad, Ahmadreza Moradipari, Sanjay Lall
Recently, bandit optimization has received significant attention in real-world safety-critical systems that involve repeated interactions with humans. While there exist various alg…