activity
20092026
most citedDistributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret

242 citations · 1.7k across the 67 of their papers we have counts for

collaborators

71 papers

cs.CL2026

A Multi-Agent LLM Framework for Rating the Quality of Surgical Feedback

Rafal Kocielnik, J. Everett Knudsen, Steven Y. Cen +7

Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, assessing the quality of trainer f…

cs.LG2025

Analyzing Political Text at Scale with Online Tensor LDA

Sara Kangaslahti, Danny Ebanks, Jean Kossaifi +3

This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Di…

cs.CL2025

Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale

Rafal Kocielnik, Min Kim, Penphob +5

Detecting prosociality in text--communication intended to affirm, support, or improve others' behavior--is a novel and increasingly important challenge for trust and safety systems…

cs.CY2024

Online Moderation in Competitive Action Games: How Intervention Affects Player Behaviors

Zhuofang Li, Rafal Kocielnik, Mitchell Linegar +7

Online competitive action games have flourished as a space for entertainment and social connections, yet they face challenges from a small percentage of players engaging in disrupt…

cs.AI2023★ 8 cited

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…

cs.LG2023★ 11 cited

Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs

Jean Kossaifi, Nikola Kovachki, Kamyar Azizzadenesheli +1

Memory complexity and data scarcity have so far prohibited learning solution operators of partial differential equations (PDEs) at high resolutions. We address these limitations by…