Rajgopal Kannan

Accurate and Resource-Efficient Federated Continual Learning

Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan +2

Federated continual learning (FCL) must learn from distributed task streams under limited resources, such as communication, computation, memory, and label availability. Existing FC…

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning

Nikunj Gupta, James Zachary Hare, Jesse Milzman +2

Cooperative multi-agent reinforcement learning agents that act on partial local observations face a fundamental information bottleneck: the knowledge needed to select jointly optim…

TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks

Ta-Yang Wang, Rajgopal Kannan, Viktor Prasanna

Heterogeneous graphs are widely used to model multi-relational systems, but missing node attributes remain a major bottleneck for downstream learning. In this paper, we identify an…

Do LLM-derived graph priors improve multi-agent coordination?

Nikunj Gupta, Rajgopal Kannan, Viktor Prasanna

Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly in multi-domain operations (M…

Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learning

Nikunj Gupta, James Zachary Hare, Jesse Milzman +2

Coordinating actions is the most fundamental form of cooperation in multi-agent reinforcement learning (MARL). Successful decentralized decision-making often depends not only on go…

TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations

Nikunj Gupta, Ludwika Twardecka, James Zachary Hare +3

In this paper, we propose capturing and utilizing \textit{Temporal Information through Graph-based Embeddings and Representations} or \textbf{TIGER} to enhance multi-agent reinforc…

Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study

Gangda Deng, Yuxin Yang, Ömer Faruk Akgül +4

Graph Neural Networks (GNNs) have become essential tools for learning on relational data, yet the performance of a single GNN is often limited by the heterogeneity present in real-…

Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say

Jacob Fein-Ashley, Dhruv Parikh, Rajgopal Kannan +1

Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across mo…

RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion

Ömer Faruk Akgül, Feiyu Zhu, Yuxin Yang +2

Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. TKG completion involves forecasting missing or future links, requiring models to…

Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning

Nikunj Gupta, James Zachary Hare, Jesse Milzman +2

This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode loca…

Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours

Yuxin Yang, Hongkuan Zhou, Rajgopal Kannan +1

Temporal Graph Neural Networks (TGNNs) have emerged as powerful tools for modeling dynamic interactions across various domains. The design space of TGNNs is notably complex, given…

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation

Tian Ye, Rajgopal Kannan, Viktor Prasanna

Adversarial training has emerged as an effective approach to train robust neural network models that are resistant to adversarial attacks, even in low-label regimes where labeled d…

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna

Graphs play a crucial role in data mining and machine learning, representing real-world objects and interactions. As graph datasets grow, managing large, decentralized subgraphs be…

Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants

Gangda Deng, Hongkuan Zhou, Rajgopal Kannan +1

Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph neural networks (GNNs). Increasing the GNN depth can expand the scope (i.e., receptive fiel…

cs.LG2024★ 4 cited

PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models

Neelesh Gupta, Pengmiao Zhang, Rajgopal Kannan +1

Deep neural networks (DNNs) have proven to be effective models for accurate Memory Access Prediction (MAP), a critical task in mitigating memory latency through data prefetching. H…