Ungar, Lyle H.

26 publications

AAAI 2025 The Illusion of Empathy: How AI Chatbots Shape Conversation Perception Tingting Liu, Salvatore Giorgi, Ankit Aich, Allison Lahnala, Brenda Curtis, Lyle H. Ungar, João Sedoc

AAAI 2019 Unsupervised Post-Processing of Word Vectors via Conceptor Negation Tianlin Liu, Lyle H. Ungar, João Sedoc

AAAI 2017 Proper Proxy Scoring Rules Jens Witkowski, Pavel Atanasov, Lyle H. Ungar, Andreas Krause

AAAI 2016 Discovering User Attribute Stylistic Differences via Paraphrasing Daniel Preotiuc-Pietro, Wei Xu, Lyle H. Ungar

JMLR 2015 Eigenwords: Spectral Word Embeddings Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar

JMLR 2013 A Risk Comparison of Ordinary Least Squares vs Ridge Regression Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar

ICML 2012 Using CCA to Improve CCA: A New Spectral Method for Estimating Vector Models of Words Paramveer S. Dhillon, Jordan Rodu, Dean P. Foster, Lyle H. Ungar

JMLR 2011 Minimum Description Length Penalization for Group and Multi-Task Sparse Learning Paramveer S. Dhillon, Dean Foster, Lyle H. Ungar

NeurIPS 2011 Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar

ECML-PKDD 2009 Multi-Task Feature Selection Using the Multiple Inclusion Criterion (MIC) Paramveer S. Dhillon, Brian Tomasik, Dean P. Foster, Lyle H. Ungar

NeurIPS 2008 Regularized Learning with Networks of Features Ted Sandler, John Blitzer, Partha P. Talukdar, Lyle H. Ungar

MLJ 2007 Active Learning for Logistic Regression: An Evaluation Andrew I. Schein, Lyle H. Ungar

JMLR 2006 Streamwise Feature Selection Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar

AISTATS 2005 Streaming Feature Selection Using IIC Lyle H. Ungar, Jing Zhou, Dean P. Foster, Bob A. Stine

AISTATS 2003 A Generalized Linear Model for Principal Component Analysis of Binary Data Andrew I. Schein, Lawrence K. Saul, Lyle H. Ungar

ICML 2003 Mixtures of Conditional Maximum Entropy Models Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar

IJCAI 2001 Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning Gregory Z. Grudic, Lyle H. Ungar

UAI 2001 Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments Alexandrin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence

NeurIPS 2001 Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning Gregory Z. Grudic, Lyle H. Ungar

AAAI 2000 Iterative Combinatorial Auctions: Theory and Practice David C. Parkes, Lyle H. Ungar

ICML 2000 Localizing Policy Gradient Estimates to Action Transition Gregory Z. Grudic, Lyle H. Ungar

AAAI 2000 Localizing Search in Reinforcement Learning Gregory Z. Grudic, Lyle H. Ungar

AAAI 2000 Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment David C. Parkes, Lyle H. Ungar

ICML 1997 Characterizing the Generalization Performance of Model Selection Strategies Dale Schuurmans, Lyle H. Ungar, Dean P. Foster

MLJ 1996 Active Learning for Vision-Based Robot Grasping Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy

ICML 1995 Active Exploration and Learning in Real-Valued Spaces Using Multi-Armed Bandit Allocation Indices Marcos Salganicoff, Lyle H. Ungar