Lyle H. Ungar

David M. Pennock, C. Lee Giles, Lyle H. Ungar, 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

Generative Models for Cold-Start Recommendations

David M. Pennock, Lyle H. Ungar, Alexandrin Popescul, 2001 .

Expert multivariable control. 2. Application of EMC to two-product distillation columns

Christos Georgakis, William L. Luyben, Lyle H. Ungar, 1990 .

Modelling Valence and Arousal in Facebook posts

Lyle H. Ungar, H. Andrew Schwartz, Daniel Preotiuc-Pietro, 2016, WASSA@NAACL-HLT.

Model‐based approach to automated hazard identification of chemical plants

Lyle H. Ungar, Catherine A. Catino, L. Ungar, 1995 .

New Subsampling Algorithms for Fast Least Squares Regression

Dean P. Foster, Lyle H. Ungar, Yichao Lu, 2013, NIPS.

Fast Network Simulation Through Approximation or: How Blind Men Can Describe Elephants

Lyle H. Ungar, Vincent Liu, João Sedoc, 2018, HotNets.

Enabling Deep Learning of Emotion With First-Person Seed Expressions

Muhammad Abdul-Mageed, Lyle H. Ungar, Hassan Alhuzali, 2018, PEOPLES@NAACL-HTL.

Towards Structural Logistic Regression: Combining Relational and Statistical Learning

David M. Pennock, Lyle H. Ungar, Steve Lawrence, 2002 .

The Marketcast Method for Aggregating Prediction Market Forecasts

Lyle H. Ungar, Philip Tetlock, Pavel Atanasov, 2013, SBP.

Clustering and identifying temporal trends in document databases

C. Lee Giles, Lyle H. Ungar, Gary William Flake, 2000, Proceedings IEEE Advances in Digital Libraries 2000.

Hybrid neural network models for environmental process control (The 1998 Hunter Lecture)

Richard D. De Veaux, Lyle H. Ungar, Rod Bain, 1999 .

Towards an Expert Multivariable Controller

Christos Georgakis, Lyle H. Ungar, Vassilios Tzouanas, 1987 .

New Insights from Coarse Word Sense Disambiguation in the Crowd

Dean P. Foster, Lyle H. Ungar, Hansen Andrew Schwartz, 2012, COLING.

Feature Selection using Multiple Streams

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2010, AISTATS.

Integrated Annotation for Biomedical Information Extraction

Seth Kulick, Ann Bies, Martha Palmer, 2004, HLT-NAACL 2004.

Subject-specific functional parcellation via Prior Based Eigenanatomy

Brian B. Avants, James C. Gee, Lyle H. Ungar, 2014, NeuroImage.

Using policy gradient reinforcement learning on autonomous robot controllers

Vijay Kumar, Gregory Z. Grudic, Lyle H. Ungar, 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

A system for de-identifying medical message board text

Lyle H. Ungar, John H. Holmes, Adrian Benton, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Efficient clustering of high-dimensional data sets with application to reference matching

Andrew McCallum, Lyle H. Ungar, Kamal Nigam, 2000, KDD '00.

Experiments with Spectral Learning of Latent-Variable PCFGs

Karl Stratos, Shay B. Cohen, Michael Collins, 2013, HLT-NAACL.

Unsupervised Morphology Learning with Statistical Paradigms

Charles Yang, Lyle H. Ungar, Hongzhi Xu, 2018, COLING.

Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach

Eduardo Blanco, Lyle H. Ungar, Martin E. P. Seligman, 2013, *SEMEVAL.

Towards Assessing Changes in Degree of Depression through Facebook

Maarten Sap, Lyle H. Ungar, H. Andrew Schwartz, 2014, CLPsych@ACL.

CROC: A New Evaluation Criterion for Recommender Systems

David M. Pennock, Lyle H. Ungar, Alexandrin Popescul, 2005, Electron. Commer. Res..

Control of the physical world by intelligent agents : papers from the 1994 AAAI Fall Symposium, November 4-6, New Orleans, Louisiana

Benjamin Kuipers, Lyle H. Ungar, 1995 .

Automatic analysis of Monte-Carlo simulations of dynamic chemical plants

Lyle H. Ungar, Warren D. Seider, E. Gazi, 1996 .

Is Online Product Information Availability Driven by Quality or Differentiation?

Lyle H. Ungar, Ravi Aron, Panos M. Markopoulos, 2005, ICIS.

Statistical Relational Learning at U Penn

Lyle H. Ungar, Alexandrin Popescul, 2003 .

The CRASSS plug-in for integrating annotation data with hierarchical clustering results

Lyle H. Ungar, Jeffrey R. Sachs, Eugen C. Buehler, 2004, Bioinform..

Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees pdf

Timothy D. Solberg, Eric Eaton, Lyle H. Ungar, 2017, ArXiv.

Active Learning for Vision-Based Robot Grasping

Ruzena Bajcsy, Marcos Salganicoff, Lyle H. Ungar, 1996, Machine Learning.

Partial Information Framework: Aggregating Estimates from Diverse Information Sources

Lyle H. Ungar, Robin Pemantle, Shane T. Jensen, 2015 .

Modeling Probability Forecasts via Information Diversity

Lyle H. Ungar, Robin Pemantle, Ville A. Satopaa, 2014 .

Anatomically-Constrained PCA for Image Parcellation

Brian B. Avants, James C. Gee, Lyle H. Ungar, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions

Lyle H. Ungar, H. Andrew Schwartz, Salvatore Giorgi, 2018, EMNLP.

Controller Verification Under Non-Parametric Uncertainty

Lyle H. Ungar, E. Gazi, Warren D. Seider, 1994 .

A model of market power and efficiency in private electronic exchanges

Lyle H. Ungar, Ravi Aron, Annapurna Valluri, 2008, Eur. J. Oper. Res..

Transfer Learning, Feature Selection and Word Sense Disambiguation

Lyle H. Ungar, Paramveer S. Dhillon, 2009, ACL/IJCNLP.

Partial sparse canonical correlation analysis (PSCCA) for population studies in medical imaging

Brian B. Avants, James C. Gee, Lyle H. Ungar, 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

Clustering Methods for Collaborative Filtering

Dean P. Foster, Lyle H. Ungar, Dean Phillips Foster, 1998, AAAI 1998.

Fault Detection and Diagnosis using Qualitative Modelling and Interpretation

Lyle H. Ungar, J. M. Vinson, L. Ungar, 1992 .

Localizing Search in Reinforcement Learning

Gregory Z. Grudic, Lyle H. Ungar, L. Ungar, 2000, AAAI/IAAI.

Machine Learning manuscript No. (will be inserted by the editor) Active Learning for Logistic Regression:

Lyle H. Ungar, Andrew I. Schein, L. Ungar, 2007 .

Expert multivariable control

William L. Luyben, Lyle H. Ungar, Christos Georgakis, 1988 .

Neural network forecasting of short, noisy time series

Lyle H. Ungar, Fred Collopy, Fred L. Collopy, 1992 .

Proper Proxy Scoring Rules

Andreas Krause, Lyle H. Ungar, Jens Witkowski, 2017, AAAI.

Finding cohesive clusters for analyzing knowledge communities

Vasileios Kandylas, Lyle H. Ungar, S. Phineas Upham, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Studying Personality through the Content of Posted and Liked Images on Twitter

Weisi Lin, Lyle H. Ungar, Sharath Chandra Guntuku, 2017, WebSci.

Maximum entropy methods for biological sequence modeling

Lyle H. Ungar, Eugen C. Buehler, L. Ungar, 2001, BIOKDD.

Discovering User Attribute Stylistic Differences via Paraphrasing

Lyle H. Ungar, Wei Xu, Daniel Preotiuc-Pietro, 2016, AAAI.

A proposal for learning by ontological leaps

Dean P. Foster, Lyle H. Ungar, Lyle H. Ungar, 2002 .

Inventing theoretical terms in inductive learning

Lyle H. Ungar, Xiaofeng Charles Ling, L. Ungar, 1989 .

Two Step CCA: A new spectral method for estimating vector models of words

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2012, ICML 2012.

Two Reasons to Make Aggregated Probability Forecasts More Extreme

Jonathan Baron, Lyle H. Ungar, Philip E. Tetlock, 2014, Decis. Anal..

Structural Logistic Regression for Link Analysis

Lyle H. Ungar, Alexandrin Popescul, L. Ungar, 2003 .

Exploring Stylistic Variation with Age and Income on Twitter

Lyle H. Ungar, Lucie Flekova, Daniel Preotiuc-Pietro, 2016, ACL.

Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 9-14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA

Karl Stratos, Shay B. Cohen, Lyle H. Ungar, 2013 .

Eigenwords: spectral word embeddings

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2015, J. Mach. Learn. Res..

Pricing interprocess streams using slack auctions

Lyle H. Ungar, Rinaldo A. Jose, L. Ungar, 2000 .

Latent human traits in the language of social media: An open-vocabulary approach

Steven Skiena, Lyle H. Ungar, H. Andrew Schwartz, 2017, PloS one.

Innovating knowledge communities

Lyle H. Ungar, Lori Rosenkopf, S. Phineas Upham, 2010, Scientometrics.

An analysis of group collaboration and competition in science and technology

Lyle H. Ungar, Lori Rosenkopf, S. P. Upham, 2010 .

Efficient Feature Selection in the Presence of Multiple Feature Classes

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Modeling Empathy and Distress in Reaction to News Stories pdf

Lyle H. Ungar, Sven Buechel, João Sedoc, 2018, EMNLP.

Characterizing the generalization performance of model selection strategies

Dale Schuurmans, Dean P. Foster, Lyle H. Ungar, 1997, ICML.

Using prior knowledge to improve genetic network reconstruction from microarray data

Lyle H. Ungar, Amit Bahl, Phillip P. Le, 2004, Silico Biol..

The AAAI-18 Workshop on Affective Content Analysis

Lyle H. Ungar, Kokil Jaidka, Niyati Chhaya, 2018, AAAI Workshops.

SVD-NET: an algorithm that automatically selects network structure

Lyle H. Ungar, D. C. Psichogios, Dimitris C. Psichogios, 1994, IEEE Trans. Neural Networks.

Studying Cultural Differences in Emoji Usage across the East and the West pdf

Lyle H. Ungar, Sharath Chandra Guntuku, Mingyang Li, 2019, ICWSM.

Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness

Lyle H. Ungar, Gary E. Weissman, Scott D. Halpern, 2019, J. Biomed. Informatics.

Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task

Maarten Sap, Lyle H. Ungar, H. Andrew Schwartz, 2015, CLPsych@HLT-NAACL.

A comparison of two nonparametric estimation schemes: MARS and neural networks

Lyle H. Ungar, D. C. Psichogios, R. D. De Veaux, 1993 .

CONTROLLER VERIFICATION FOR POLYMERIZATION REACTORS

Lyle H. Ungar, Warren D. Seider, E. Gazi, 1996 .

Spectral learning of latent-variable PCFGs: algorithms and sample complexity

Karl Stratos, Shay B. Cohen, Michael Collins, 2014, J. Mach. Learn. Res..

Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment

David C. Parkes, Lyle H. Ungar, D. Parkes, 2000, AAAI/IAAI.

Iterative Combinatorial Auctions with Bidder-Determined Combinations

Lyle H. Ungar, G. Anandalingam, R. H. Kwon, 2005, Manag. Sci..

Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012: Preface

Xiaohua Hu, Cathy H. Wu, Lyle H. Ungar, 2012, BIBM 2012.

Efficient Clustering of Web-Derived Data Sets

Eugénio C. Oliveira, Lyle H. Ungar, Luís Sarmento, 2009, MLDM.

Active Learning for Multi-Class Logistic Regression

Lyle H. Ungar, Andrew I. Schein, 2005 .

Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2009, ECML/PKDD.

Unsupervised Post-processing of Word Vectors via Conceptor Negation

Tianlin Liu, Lyle H. Ungar, João Sedoc, 2018, AAAI.

Extracting Product Comparisons from Discussion Boards

Jacob Goldenberg, Lyle H. Ungar, Ronen Feldman, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

A bioreactor benchmark for adaptive network-based process control

Lyle H. Ungar, Lyle H. Ungar, L. Ungar, 1990 .

Qmimic: Model-based monitoring and diagnosis

Lyle H. Ungar, J. M. Vinson, Jonathan M. Vinson, 1993, 1993 American Control Conference.

Using sequence classification for filtering web pages

Lyle H. Ungar, Ronen Feldman, Binyamin Rosenfeld, 2008, CIKM '08.

PennAspect: A Two-Way Aspect Model Implementation

Lyle H. Ungar, Alexandrin Popescul, Andrew I. Schein, 2001 .

A system for de-identifying medical message board text

Lyle H. Ungar, John H. Holmes, Adrian Benton, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Neural Tree Transducers for Tree to Tree Learning

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2018 .

Penn: Using Word Similarities to better Estimate Sentence Similarity

Lyle H. Ungar, Hansen Andrew Schwartz, Sneha Jha, 2012, SemEval@NAACL-HLT.

A hybrid neural network‐first principles approach to process modeling

Lyle H. Ungar, D. C. Psichogios, Dimitris C. Psichogios, 1992 .

Understanding and Measuring Psychological Stress using Social Media pdf

Lyle H. Ungar, Sharath Chandra Guntuku, Kokil Jaidka, 2018, ICWSM.

Multiway Clustering for Creating Biomedical Term Sets

Vasileios Kandylas, Lyle H. Ungar, Ted Sandler, 2008, 2008 IEEE International Conference on Bioinformatics and Biomedicine.

Statistical relational learning for document mining

David M. Pennock, Lyle H. Ungar, Steve Lawrence, 2003, Third IEEE International Conference on Data Mining.

Identifying potential adverse effects using the web: A new approach to medical hypothesis generation

Lyle H. Ungar, Jun Mao, John H. Holmes, 2011, J. Biomed. Informatics.

Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions

Lyle H. Ungar, Kokil Jaidka, H. Andrew Schwartz, 2017, IJCNLP.

Semantic Word Clusters Using Signed Spectral Clustering

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2017, ACL.

Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering

Lyle H. Ungar, João Sedoc, Daniel Preotiuc-Pietro, 2017, EACL.

Medpie: an Information Extraction Package for Medical Message Board Posts

Lyle H. Ungar, John H. Holmes, Adrian Benton, 2012, Bioinform..

A Formal Statistical Approach to Collaborative Filtering

Dean P. Foster, Lyle H. Ungar, Dean Phillips Foster, 1998 .

Spectral Learning of Latent-Variable PCFGs

Karl Stratos, Shay B. Cohen, Michael Collins, 2012, ACL.

DASHR: database of small human noncoding RNAs

Sampath Kannan, Lyle H. Ungar, Yuk Yee Leung, 2015, Nucleic Acids Res..

Crowdsourcing for NLP

Chris Callison-Burch, Lyle H. Ungar, Ellie Pavlick, 2015, NAACL.

Process control

Lyle H. Ungar, 1998 .

Confidence Calibration in a Multiyear Geopolitical Forecasting Competition

Don A. Moore, Lyle H. Ungar, Samuel A. Swift, 2017, Manag. Sci..

AUCTION-DRIVEN COORDINATION FOR PLANTWIDE OPTIMIZATION

Lyle H. Ungar, R. A. Jose, L. Ungar, 1998 .

Analyzing knowledge communities using foreground and background clusters

Vasileios Kandylas, Lyle H. Ungar, S. Phineas Upham, 2010, TKDD.

Adaptive networks for fault diagnosis and process control

Lyle H. Ungar, B. A. Powell, S. N. Kamens, 1990 .

Analyzing Biases in Human Perception of User Age and Gender from Text

Lyle H. Ungar, Salvatore Giorgi, Lucie Flekova, 2016, ACL.

Developing Age and Gender Predictive Lexica over Social Media

Maarten Sap, Lyle H. Ungar, H. Andrew Schwartz, 2014, EMNLP.

A Generalized Linear Model for Principal Component Analysis of Binary Data

Lawrence K. Saul, Lyle H. Ungar, Andrew I. Schein, 2003, AISTATS.

Multi-View Learning of Word Embeddings via CCA

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2011, NIPS.

Automatic rebuilding of qualitative models for diagnosis

Lyle H. Ungar, J. M. Vinson, Jonathan M. Vinson, 1992, IEEE Expert.

Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task

Lyle H. Ungar, Sharath Chandra Guntuku, Salvatore Giorgi, 2018, CLPsych@NAACL-HTL.

The Good Judgment Project: A Large Scale Test of Different Methods of Combining Expert Predictions

Lyle H. Ungar, Jonathan Baron, Ville Satopaa, 2012, AAAI Fall Symposium: Machine Aggregation of Human Judgment.

String Edit Analysis for Merging Databases

Lyle H. Ungar, J. Joanne Zhu, L. Ungar, 2000, KDD 2000.

Characterizing the emergence of two nanotechnology topics using a contemporaneous global micro-model of science

Kevin W. Boyack, Lyle H. Ungar, Richard Klavans, 2014 .

Pricing price information in e-commerce

Lyle H. Ungar, Panos M. Markopoulos, L. Ungar, 2001, EC '01.

Extracting templates from radiology reports using sequence alignment

Lyle H. Ungar, Curtis P. Langlotz, Paras Lakhani, 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop.

A risk comparison of ordinary least squares vs ridge regression

Sham M. Kakade, Dean P. Foster, Lyle H. Ungar, 2011, J. Mach. Learn. Res..

Controlling Human Perception of Basic User Traits

Lyle H. Ungar, Sharath Chandra Guntuku, Daniel Preotiuc-Pietro, 2017, EMNLP.

Protein-Protein Interaction Network Alignment by Quantitative Simulation

Lyle H. Ungar, Ted Sandler, Perry Evans, 2008, 2008 IEEE International Conference on Bioinformatics and Biomedicine.

Classification of individual articles from all of science by research level

Kevin W. Boyack, Lyle H. Ungar, Richard Klavans, 2014, J. Informetrics.

Automatic Labeling of Document Clusters

Lyle H. Ungar, Alexandrin Popescul, L. Ungar, 2000, KDD 2000.

Statistical learning from relational databases

Lyle H. Ungar, Alexandrin Popescul, 2004 .

Modeling and Visualizing Locus of Control with Facebook Language

Lyle H. Ungar, Kokil Jaidka, Masoud Rouhizadeh, 2018, ICWSM.

Using radial basis functions to approximate a function and its error bounds

Lyle H. Ungar, Mark A. Kramer, J. A. Leonard, 1992, IEEE Trans. Neural Networks.

Multiscale Hidden Markov Models For Covariance Prediction

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2018 .

A predictive model for identifying mini-regulatory modules in the mouse genome

Lyle H. Ungar, Ted Sandler, Mahesh Yaragatti, 2009, Bioinform..

Answering Reading Comprehension Tests: N-gram and Multi-view Regression

Lyle H. Ungar, Andrew Pak, Daniel Seunghyun Kim, 2010 .

Toward Personality Insights from Language Exploration in Social Media

Eduardo Blanco, Lyle H. Ungar, Martin E. P. Seligman, 2013, AAAI Spring Symposium: Analyzing Microtext.

Learning Neural Emotion Analysis from 100 Observations: The Surprising Effectiveness of Pre-Trained Word Representations pdf

Lyle H. Ungar, Sven Buechel, H. Andrew Schwartz, 2018, ArXiv.

Categories and Subject Descriptors

David M. Pennock, Lyle H. Ungar, John R. Douceur, 2001 .

CONTROLLER VERIFICATION USING QUALITATIVE REASONING

Lyle H. Ungar, E. Gazi, Warren D. Seider, 1994 .

Expert-augmented machine learning

Timothy D. Solberg, Eric Eaton, Lyle H. Ungar, 2019, Proceedings of the National Academy of Sciences.

Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning

Gregory Z. Grudic, Lyle H. Ungar, 2001, IJCAI.

Studying the Dark Triad of Personality through Twitter Behavior

Lyle H. Ungar, Salvatore Giorgi, Daniel Preotiuc-Pietro, 2016, CIKM.

Matching Neural Models to Experiment

Lyle H. Ungar, John J. Hopfield, James S. Schwaber, 1993 .

Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference

Lyle H. Ungar, H. Andrew Schwartz, Masoud Rouhizadeh, 2016, EMNLP.

Positioning knowledge: schools of thought and new knowledge creation

Lyle H. Ungar, Lori Rosenkopf, S. Phineas Upham, 2010, Scientometrics.

Diachronic degradation of language models: Insights from social media

Lyle H. Ungar, Kokil Jaidka, Niyati Chhaya, 2018, ACL.

DLATK: Differential Language Analysis ToolKit

Maarten Sap, Lyle H. Ungar, Patrick Crutchley, 2017, EMNLP.

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Dimitrios Gunopulos, Tina Eliassi-Rad, Lyle H. Ungar, 2006, KDD 2006.

COMPARATIVE ANALYSIS OF QUALITATIVE MODELS WHEN THE MODEL CHANGES

Lyle H. Ungar, S. D. Grantham, L. Ungar, 1991 .

Extracting templates from radiology reports using sequence alignment

Lyle H. Ungar, Curtis P. Langlotz, Paras Lakhani, 2009, BIBM 2009.

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Lyle H. Ungar, Sharath Chandra Guntuku, Daniel Preotiuc-Pietro, 2019, ICWSM.

Bayesian Example Selection Using BaBiES

Lyle H. Ungar, Andrew I. Schein, S. Ted Sandler, 2004 .

Enterprise to Computer: Star Trek chatbot pdf

Lyle H. Ungar, João Sedoc, Abheek Basu, 2017, ArXiv.

Non-Parametric System Identification: A Comparison of MARS and Neural Networks

Lyle H. Ungar, Dimitris C. Psichogios, Richard D. De Veaux, 1992, 1992 American Control Conference.

Learning and Adaption in Multi-Agent Systems

David C. Parkes, Lyle H. Ungar, D. Parkes, 2006, Lecture Notes in Computer Science.

The role of baroreceptor resetting and habituating control in blood pressure regulation

Lyle H. Ungar, James S. Schwaber, S. R. Carden, 1994, Proceedings of 1994 American Control Conference - ACC '94.

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006

Dimitrios Gunopulos, Tina Eliassi-Rad, Lyle H. Ungar, 2006, KDD.

Accounting for Cognitive Costs in On-Line Auction Design

David C. Parkes, Dean P. Foster, Lyle H. Ungar, 1998, AMET.

Dynamic process monitoring and fault diagnosis with qualitative models

Lyle H. Ungar, J. M. Vinson, Jonathan M. Vinson, 1995, IEEE Trans. Syst. Man Cybern..

An Empirical Exploration of Moral Foundations Theory in Partisan News Sources

Lyle H. Ungar, Daniel Preotiuc-Pietro, Jordan Carpenter, 2016, LREC.

The role of personality, age, and gender in tweeting about mental illness

Maarten Sap, Lyle H. Ungar, H. Andrew Schwartz, 2015, CLPsych@HLT-NAACL.

Facebook versus Twitter: Differences in Self-Disclosure and Trait Prediction

Lyle H. Ungar, Sharath Chandra Guntuku, Kokil Jaidka, 2018, ICWSM.

A non-parametric Monte Carlo technique for controller verification

Lyle H. Ungar, Warren D. Seider, Evangelia Gazi, 1997, Autom..

Information markets for product attributes: A game theoretic, dual pricing mechanism

Lyle H. Ungar, Ravi Aron, Panos M. Markopoulos, 2010, Decis. Support Syst..

Mixtures of Conditional Maximum Entropy Models

David M. Pennock, Lyle H. Ungar, Alexandrin Popescul, 2003, ICML.

Domain Aware Neural Dialog System pdf

Lyle H. Ungar, João Sedoc, Prerna Srivastava, 2017, ArXiv.

Stability of neural net based model predictive control

John W. Eaton, Lyle H. Ungar, James B. Rawlings, 1994, Proceedings of 1994 American Control Conference - ACC '94.

Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices

Marcos Salganicoff, Lyle H. Ungar, L. Ungar, 1995, ICML.

On the Distribution of Lexical Features at Multiple Levels of Analysis

Fatemeh Almodaresi, Lyle H. Ungar, H. Andrew Schwartz, 2017, ACL.

A fuzzy logic controller for pH control of a chemical stirred tank

Lyle H. Ungar, Jelenka B. Savković-Stevanović, Varren D. Seider, 2000 .

Streaming Feature Selection using IIC

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2005, AISTATS.

Partial Information Framework: Model-Based Aggregation of Estimates from Diverse Information Sources

Lyle H. Ungar, Robin Pemantle, Shane T. Jensen, 2015, 1505.06472.

Analyzing Personality through Social Media Profile Picture Choice

Lyle H. Ungar, Zahra Riahi Samani, Mohsen Ebrahimi Moghaddam, 2016, ICWSM.

Automatic generation of qualitative models of chemical process units

Lyle H. Ungar, S. D. Grantham, C. A. Catino, 1991 .

Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information

Lyle H. Ungar, Gary E. Weissman, Zachary N. Flamholz, 2019, J. Biomed. Informatics.

Spectral Dependency Parsing with Latent Variables

Michael Collins, Dean P. Foster, Jordan Rodu, 2012, EMNLP-CoNLL.

Probability aggregation in time-series: Dynamic hierarchical modeling of sparse expert beliefs

Lyle H. Ungar, Shane T. Jensen, Barbara A. Mellers, 2014, 1408.0087.

Finding Cohesive Clusters for Analyzing Knowledge Communities

Vasileios Kandylas, Lyle H. Ungar, S. Phineas Upham, 2007, ICDM.

Extracting Human Temporal Orientation from Facebook Language

Maarten Sap, Lyle H. Ungar, Martin E. P. Seligman, 2015, NAACL.

Personality Driven Differences in Paraphrase Preference

Lyle H. Ungar, Daniel Preotiuc-Pietro, Jordan Carpenter, 2017, NLP+CSS@ACL.

Characterizing Geographic Variation in Well-Being Using Tweets

Megha Agrawal, Lyle H. Ungar, Martin E. P. Seligman, 2013, ICWSM.

Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors pdf

Tianlin Liu, Lyle H. Ungar, João Sedoc, 2018, ArXiv.

A first principles approach to automated troubleshooting of chemical plants

Lyle H. Ungar, S. D. Grantham, L. Ungar, 1990 .

Minimum Description Length Penalization for Group and Multi-Task Sparse Learning

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2011, J. Mach. Learn. Res..

Transfer Learning Using Feature Selection pdf

Dean P. Foster, Lyle H. Ungar, Paramveer S. Dhillon, 2009, ArXiv.

Dual Pricing in Electronic Markets

Lyle H. Ungar, Ravi Aron, Panos M. Markopoulos, 2003, ICIS.

Predicting Individual Well-Being Through the Language of Social Media

Megha Agrawal, Maarten Sap, Eduardo Blanco, 2016, PSB.

Recognizing Counterfactual Thinking in Social Media Texts

Lyle H. Ungar, H. Andrew Schwartz, Youngseo Son, 2017, ACL.

User-Level Race and Ethnicity Predictors from Twitter Text

Lyle H. Ungar, Daniel Preotiuc-Pietro, L. Ungar, 2018, COLING.

Multicollinearity: A tale of two nonparametric regressions

Richard D. De Veaux, Lyle H. Ungar, R. D. Veaux, 1994 .

Analyzing Product Comparisons on Discussion Boards

Jacob Goldenberg, Lyle H. Ungar, Ronen Feldman, 2014, Language, Culture, Computation.

Improving Supervised Sense Disambiguation with Web-Scale Selectors

Fernando Gomez, Lyle H. Ungar, Hansen Andrew Schwartz, 2012, COLING.

Spectral Learning Algorithms for Natural Language Processing

Karl Stratos, Shay B. Cohen, Michael Collins, 2013, HLT-NAACL.

Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning

Gregory Z. Grudic, Lyle H. Ungar, L. Ungar, 2001, NIPS.

A New Approach to Lexical Disambiguation of Arabic Text

Mark Liberman, Dean P. Foster, Lyle H. Ungar, 2010, EMNLP.

Does ‘well-being’ translate on Twitter?

Muhammad Abdul-Mageed, Lyle H. Ungar, H. Andrew Schwartz, 2016, EMNLP.

Beyond Binary Labels: Political Ideology Prediction of Twitter Users

Lyle H. Ungar, Ye Liu, Daniel Preotiuc-Pietro, 2017, ACL.

Process Modeling using Structured Neural Networks

Lyle H. Ungar, D. C. Psichogios, Dimitris C. Psichogios, 1992, 1992 American Control Conference.

Product Information Websites: Are They Good for Consumers?

Lyle H. Ungar, Ravi Aron, Panos M. Markopoulos, 2016, J. Manag. Inf. Syst..

A -Optimality for Active Learning of Logistic Regression Classifiers

Lyle H. Ungar, Andrew I. Schein, L. Ungar, 2004 .

Deriving Verb Predicates By Clustering Verbs with Arguments pdf

Derry Wijaya, Lyle H. Ungar, Andy Schwartz, 2017, ArXiv.

Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis

Brian B. Avants, James C. Gee, Lyle H. Ungar, 2010, NeuroImage.

Automatic term list generation for entity tagging

Lyle H. Ungar, Ted Sandler, Andrew I. Schein, 2006, Bioinform..

Recognizing Pathogenic Empathy in Social Media

Muhammad Abdul-Mageed, Lyle H. Ungar, Hao Peng, 2017, ICWSM.

Active learning for vision-based robot grasping

Ruzena Bajcsy, Marcos Salganicoff, Lyle H. Ungar, 1996, Machine Learning.

An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation

Martha Palmer, Lyle H. Ungar, Jinying Chen, 2006, NAACL.

EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

Muhammad Abdul-Mageed, Lyle H. Ungar, L. Ungar, 2017, ACL.

Iterative combinatorial auctions: achieving economic and computational efficiency

David C. Parkes, Lyle H. Ungar, D. Parkes, 2001 .

An Approach to Web-Scale Named-Entity Disambiguation

Eugénio C. Oliveira, Lyle H. Ungar, Luís Sarmento, 2009, MLDM.

How do Infomediaries affect firms' information strategies, and how do they impact buyer and social welfare?

Lyle H. Ungar, Ravi Aron, Panos M. Markopoulos, 2013, ICIS.

Assessing Objective Recommendation Quality through Political Forecasting

Lyle H. Ungar, H. Andrew Schwartz, Masoud Rouhizadeh, 2017, EMNLP.

Streamwise Feature Selection

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2006, J. Mach. Learn. Res..

Identifying Locus of Control in Social Media Language

Lyle H. Ungar, Kokil Jaidka, H. Andrew Schwartz, 2018, EMNLP.

An Ascending-Price Generalized Vickrey Auction

David C. Parkes, Lyle H. Ungar, D. Parkes, 2002 .

Streaming feature selection using alpha-investing

Jing Zhou, Dean P. Foster, Lyle H. Ungar, 2005, KDD '05.

Using CCA to improve CCA: A new spectral method for estimating vector models of words

Dean P. Foster, Jordan Rodu, Lyle H. Ungar, 2012, ICML.

Unsupervised Distance Metric Learning Using Predictability

Dean P. Foster, Lyle H. Ungar, Abhishek A. Gupta, 2008 .

Semantic Word Clusters Using Signed Normalized Graph Cuts pdf

Dean P. Foster, Lyle H. Ungar, João Sedoc, 2016, ArXiv.

Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

David M. Pennock, Lyle H. Ungar, Steve Lawrence, 2001, UAI.

Iterative Combinatorial Auctions: Theory and Practice

David C. Parkes, Lyle H. Ungar, D. Parkes, 2000, AAAI/IAAI.

Radial Basis Functions for Process Control

Lyle H. Ungar, Thomas Richard, D. De Veaux, 1994 .

An auction-based method for decentralized train scheduling

David C. Parkes, Lyle H. Ungar, 2001, AGENTS '01.

Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes

Lyle H. Ungar, Gary E. Weissman, Rebecca A. Hubbard, 2021, J. Am. Medical Informatics Assoc..

Quantifying causality in data science with quasi-experiments

Lyle H. Ungar, Konrad P. Kording, Konrad Paul Kording, 2021, Nature Computational Science.