William W Cohen

About

William W. Cohen is a Professor at Carnegie Mellon University in the Machine Learning Department, with a joint appointment in the Language Technology Institute. He holds a 20%-time appointment as a Principal Scientist at Google, where he worked full-time between May 2018 and March 2024. Cohen received his bachelor's degree in Computer Science from Duke University in 1984 and his PhD in Computer Science from Rutgers University in 1990. His professional background includes work at AT&T Bell Labs and AT&T Labs-Research from 1990 to 2000, and at Whizbang Labs from 2000 to 2002, focusing on extracting information from the web. From 2002 to 2018, he was part of Carnegie Mellon University’s Machine Learning Department, contributing significantly to the field. Cohen has served as a past president of the International Machine Learning Society and has held roles as an action editor for various prominent journals and book series related to AI and machine learning. He has been involved in organizing major conferences, including serving as General Chair for the 2008 International Machine Learning Conference and co-chairing other significant events. Recognized as an AAAI Fellow, Cohen has received multiple awards for influential papers, including the SIGMOD 'Test of Time' Award, the SIGIR 'Test of Time' Award, and the Semantic Web Science Association's Ten-Year Award. His research interests encompass question answering, machine learning for NLP tasks, neuro-symbolic reasoning, and…

Research topics

• Artificial Intelligence
• Information Retrieval
• Computer Science
• Machine Learning
• Natural Language Processing
• Data Mining
• World Wide Web

Selected publications

• Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 125 citations

  Senior authorCorresponding

  In this work, we address the twin problems of unsupervised topic discovery and estimation of topic specific influence of blogs. We propose a new model that can be used to provide a user with highly influential blog postings on the topic of the user's interest. We adopt the framework of an unsupervised model called Latent Dirichlet Allocation, known for its effectiveness in topic discovery. An extension of this model, which we call Link-LDA, defines a generative model for hyperlinks and thereby m…

  DOI

• Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation

  arXiv (Cornell University) · 2024-06-06 · 2 citations

  preprintOpen accessSenior author

  Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. PPI achieves this by combining small amounts of human-labeled data with larger amounts of data labeled by a reasonably accurate -- but potentially biased -- automatic system, in a way that results in tighter confidence intervals for certain parameters of interest (e.g., the mean performance of a language model). In this paper, we propose a method called Stratified Prediction-Po…

  PublisherOA PDFDOI

• VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought

  2024-01-01 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Instruct-Imagen: Image Generation with Multi-modal Instruction

  arXiv (Cornell University) · 2024-01-03 · 1 citations

  preprintOpen access

  This paper presents instruct-imagen, a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision. It uses natural language to amalgamate disparate modalities (e.g., text, edge, style, subject, etc.), such that abundant generation intents can be standardized in a uniform format. We then build instruct-imagen by fine-tuning…

  PublisherOA PDFDOI

• Multiple-Prediction-Powered Inference

  arXiv (Cornell University) · 2026-03-28

  articleOpen access

  Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Inference (MultiPPI): a general framework for constructing statistically efficient estimates by optimally allocating resources across these diverse data sources. This work provides theoretical guarantees about the minimax optimality, finite-sample performance, and asymptotic normality of the MultiPPI estimator. Through exper…

  PublisherOA PDF

Recent grants

• NIH Grant K25DA017357

  NIH · $458k · 2007

• SHF: Large: Collaborative Research: Exploiting the Naturalness of Software

  NSF · $667k · 2014–2018

• Building Cognitive Tutors with Programming by Demonstration: When Simulated Students help Cognitive Modeling and Educational Studies

  NSF · $499k · 2005–2009

Frequent coauthors

• Bhuwan Dhingra

  56 shared

• Ruslan Salakhutdinov

  44 shared

• Zhilin Yang

  34 shared

• Kenneth R. Koedinger

  Carnegie Mellon University

  34 shared

• Haitian Sun

  Nanjing University of Chinese Medicine

  32 shared

• Kathryn Mazaitis

  23 shared

• Einat Minkov

  22 shared

• Noboru Matsuda

  21 shared

Education

• B.S.

  Duke University

  1984

• Ph.D.

  Rutgers University

  1990

Awards & honors

• AAAI Fellow

Similar researchers at Carnegie Mellon University

• Christos Nick Faloutsosh-137
• Robert E Kassh-71
• Barnabas Poczosh-58