activity
20192026
most citedMaximum Likelihood Estimation for Learning Populations of Parameters

12 citations · 20 across the 17 of their papers we have counts for

collaborators

20 papers

cs.MA2026

GRASP: Graph Agentic Search over Propositions for Multi-hop Question Answering

Stockton Jenkins, Ramya Korlakai Vinayak, Junjie Hu

Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these systems with knowledge graphs…

cs.CL2026

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

Daiwei Chen, Zhoutong Fu, Chengming Jiang +12

Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in generative recommendation. The standar…

cs.AI2026

Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights

Yi Chen, Daiwei Chen, Sukrut Madhav Chikodikar +2

Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications. Retrieval-augmented generation (RAG) and conformal factuality h…

cs.LG2026

Almost Asymptotically Optimal Active Clustering Through Pairwise Observations

Rachel S. Y. Teo, P. N. Karthik, Ramya Korlakai Vinayak +1

We propose a new analysis framework for clustering items into an unknown number of distinct groups using noisy and actively collected responses. At each time step, an agent…

cs.CV2026

Agentic Very Long Video Understanding

Aniket Rege, Arka Sadhu, Yuliang Li +5

The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond sho…

cs.LG2025

Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure

Zhi Wang, Chicheng Zhang, Ramya Korlakai Vinayak

In lifelong learning, a learner faces a sequence of tasks with shared structure and aims to identify and leverage it to accelerate learning. We study the setting where such structu…