6 citations · 8 across the 17 of their papers we have counts for
21 papers
Vertical Sliced Wasserstein Distances for Meta-Measures
Elaheh Akbari, Ahmadreza Moradipari, Kyungtae Han +4
Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures…
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…
OT-MeanFlow3D: Bridging Optimal Transport and Meanflow for Efficient 3D Point Cloud Generation
Elaheh Akbari, Shansita Sharma, Ping He +5
Flow-matching models have recently emerged as a powerful framework for generative modeling, including 3D point cloud synthesis. However, their deployment is limited by the need for…
On Learning Closed-Loop Probabilistic Multi-Agent Simulator
Juanwu Lu, Rohit Gupta, Ahmadreza Moradipari +3
The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Re…
NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models
Sung-Yeon Park, Can Cui, Yunsheng Ma +4
Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to comprehend driving scenes remain…
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…
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…
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
Ahmadreza Moradipari, Mohammad Pedramfar, Modjtaba Shokrian Zini +1
In this paper, we prove the first Bayesian regret bounds for Thompson Sampling in reinforcement learning in a multitude of settings. We simplify the learning problem using a discre…
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Mahyar Abbasian, Taha Rajabzadeh, Ahmadreza Moradipari +3
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over…
Predicting Parameters for Modeling Traffic Participants
Ahmadreza Moradipari, Sangjae Bae, Mahnoosh Alizadeh +2
Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based…
Collaborative Multi-agent Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh
We study a collaborative multi-agent stochastic linear bandit setting, where agents that form a network communicate locally to minimize their overall regret. In this setting, e…
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh +2
In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired…
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori +2
We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of the LB problem is in t…
Stage-wise Conservative Linear Bandits
Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh
We study stage-wise conservative linear stochastic bandits: an instance of bandit optimization, which accounts for (unknown) safety constraints that appear in applications such as…
Coagent Networks Revisited
Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer +2
Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment. Prominent examples of co…
Mobility-Aware Smart Charging of Electric Bus Fleets
Ahmadreza Moradipari, Nathaniel Tucker, Tuo Zhang +2
We study the joint route assignment and charge scheduling problem of a transit system dispatcher operating a fleet of electric buses in order to maximize solar energy integration a…
Safe Linear Thompson Sampling with Side Information
Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh +1
The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, w…
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints
Nathaniel Tucker, Ahmadreza Moradipari, Mahnoosh Alizadeh
We consider the problem of an aggregator attempting to learn customers' load flexibility models while implementing a load shaping program by means of broadcasting daily dispatch si…
Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network
Ahmadreza Moradipari, Mahnoosh Alizadeh
We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for…
Mobility-Aware Electric Vehicle Fast Charging Load Models with Geographical Price Variations
Ahmadreza Moradipari, Nathaniel Tucker, Mahnoosh Alizadeh
We study the traffic patterns as well as the charging patterns of a population of cost-minimizing EV owners traveling and charging within a transportation network equipped with fas…
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information
Ahmadreza Moradipari, Sina Shahsavari, Ashkan Esmaeili +1
Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been m…