78 citations · 196 across the 72 of their papers we have counts for
14 papers · 1 filter
Chameleon: Dynamic Format Adapter for Efficient Diffusion
Arnab Sanyal, Sandeep Chinchali
Post-training quantization (PTQ) is the standard way to run modern diffusion models on memory-constrained accelerators, yet every existing diffusion PTQ scheme fixes the $\mathit{n…
A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta +3
While generative modeling has achieved remarkable success on tasks like natural language-conditioned image generation, enabling model adaptation from example data points remains a…
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
Yifeng Zhang, Harsh Goel, Peizhuo Li +3
Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforc…
Fair Resource Allocation for Fleet Intelligence
Oguzhan Baser, Kaan Kale, Po-han Li +1
Resource allocation is crucial for the performance optimization of cloud-assisted multi-agent intelligence. Traditional methods often overlook agents' diverse computational capabil…
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
Arnab Sanyal, Gourav Datta, Prithwish Mukherjee +2
Large Language Models (LLMs) achieve strong performance across tasks, but face storage and compute challenges on edge devices. We propose EntroLLM, a compression framework combinin…
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
Shubhankar Agarwal, Hamzah I. Khan, Sandeep P. Chinchali +1
Saddle point optimization is a critical problem employed in numerous real-world applications, including portfolio optimization, generative adversarial networks, and robotics. It ha…
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Cevahir Koprulu, Po-han Li, Tianyu Qiu +5
Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to…
Constrained Posterior Sampling: Time Series Generation with Hard Constraints
Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout +2
Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications,…
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features
Po-han Li, Sandeep P. Chinchali, Ufuk Topcu
Multimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data. We propose canonical s…
Time Weaver: A Conditional Time Series Generation Model
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin +2
Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winte…
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics
Christos Verginis, Cevahir Koprulu, Sandeep Chinchali +1
We study the problem of reinforcement learning for a task encoded by a reward machine. The task is defined over a set of properties in the environment, called atomic propositions,…
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving
Manabu Nakanoya, Junha Im, Hang Qiu +3
Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas. Ideally, fleets of AVs should share trajectory data to continually re…
Data Sharing and Compression for Cooperative Networked Control
Jiangnan Cheng, Marco Pavone, Sachin Katti +2
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power g…
Multi-agent Reinforcement Learning for Networked System Control
Tianshu Chu, Sandeep Chinchali, Sachin Katti
This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observatio…