10 citations · 17 across the 10 of their papers we have counts for
10 papers
Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs
Dom Huh, Prasant Mohapatra
Language is a ubiquitous tool that is foundational to reasoning and collaboration, ranging from everyday interactions to sophisticated problem-solving tasks. The establishment of a…
Multi-agent Auto-Bidding with Latent Graph Diffusion Models
Dom Huh, Prasant Mohapatra
This paper proposes a diffusion-based auto-bidding framework that leverages graph representations to model large-scale auction environments. In such settings, agents must dynamical…
Maximize Your Diffusion: A Study into Reward Maximization and Alignment for Diffusion-based Control
Dom Huh, Prasant Mohapatra
Diffusion-based planning, learning, and control methods present a promising branch of powerful and expressive decision-making solutions. Given the growing interest, such methods ha…
Identity-Focused Inference and Extraction Attacks on Diffusion Models
Jayneel Vora, Aditya Krishnan, Nader Bouacida +2
The increasing reliance on diffusion models for generating synthetic images has amplified concerns about the unauthorized use of personal data, particularly facial images, in model…
PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models
Jayneel Vora, Aditya Krishnan, Nader Bouacida +2
Denoising diffusion models have emerged as state-of-the-art in generative tasks across image, audio, and video domains, producing high-quality, diverse, and contextually relevant d…
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision
Aditya Krishnan, Jayneel Vora, Prasant Mohapatra
Semantic segmentation has emerged as a pivotal area of study in computer vision, offering profound implications for scene understanding and elevating human-machine interactions acr…
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models
Jayneel Vora, Nader Bouacida, Aditya Krishnan +1
We introduce FedDM, a novel training framework designed for the federated training of diffusion models. Our theoretical analysis establishes the convergence of diffusion models whe…
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
Dom Huh, Prasant Mohapatra
Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary…
Multi-agent Reinforcement Learning: A Comprehensive Survey
Dom Huh, Prasant Mohapatra
Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-world applications, where multiple agents must make decisions to achieve their objectives in…
Incentivizing Data Contribution in Cross-Silo Federated Learning
Chao Huang, Shuqi Ke, Charles Kamhoua +2
In cross-silo federated learning, clients (e.g., organizations) train a shared global model using local data. However, due to privacy concerns, the clients may not contribute enoug…