49 citations · 53 across the 3 of their papers we have counts for
5 papers
Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia, Xu Liu, Tong Yu +5
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallu…
Decentralized Personalized Online Federated Learning
Renzhi Wu, Saayan Mitra, Xiang Chen +1
Vanilla federated learning does not support learning in an online environment, learning a personalized model on each client, and learning in a decentralized setting. There are exis…
Optimal Sketching Bounds for Sparse Linear Regression
Tung Mai, Alexander Munteanu, Cameron Musco +3
We study oblivious sketching for -sparse linear regression under various loss functions such as an norm, or from a broad class of hinge-like loss functions, which inclu…
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs
Michael B. Cohen, Jonathan Kelner, John Peebles +4
In this paper we introduce a notion of spectral approximation for directed graphs. While there are many potential ways one might define approximation for directed graphs, most of t…
Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery
Peter Chin, Anup Rao, Van Vu
In this paper, we present and analyze a simple and robust spectral algorithm for the stochastic block model with blocks, for any fixed. Our algorithm works with graphs havi…