27 citations · 34 across the 10 of their papers we have counts for
10 papers
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
Soumyendu Sarkar, Avisek Naug, Antonio Guillen +7
Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimiz…
SustainDC: Benchmarking for Sustainable Data Center Control
Avisek Naug, Antonio Guillen, Ricardo Luna +8
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate ch…
Structured Reinforcement Learning for Media Streaming at the Wireless Edge
Archana Bura, Sarat Chandra Bobbili, Shreyas Rameshkumar +3
Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein…
N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan +5
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs thro…
Federated Ensemble-Directed Offline Reinforcement Learning
Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil +1
We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control polic…
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Desik Rengarajan, Sapana Chaudhary, Jaewon Kim +2
Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy. The meta-policy, when adapted ov…
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh +2
A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indica…
Reinforcement Learning for Mean Field Games with Strategic Complementarities
Kiyeob Lee, Desik Rengarajan, Dileep Kalathil +1
Mean Field Games (MFG) are the class of games with a very large number of agents and the standard equilibrium concept is a Mean Field Equilibrium (MFE). Algorithms for learning MFE…
Learning to Cache and Caching to Learn: Regret Analysis of Caching Algorithms
Archana Bura, Desik Rengarajan, Dileep Kalathil +2
Crucial performance metrics of a caching algorithm include its ability to quickly and accurately learn a popularity distribution of requests. However, a majority of work on analyti…
QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge
Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan +6
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. Howev…