Information-Theoretic Rule Induction. - Rodney M. Goodman; Padhraic Smyth (ECAI, 1988)

An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. - Rodney M. Goodman; John W. Miller; Padhraic Smyth (NIPS, 1988)

Decision tree design from a communication theory standpoint. - Rodney M. Goodman; Padhraic Smyth (IEEE Trans. Inf. Theory, 1988)

The Induction of Probabilistic Rule Sets - The Itrule Algorithm. - Rodney M. Goodman; Padhraic Smyth (ML, 1989)

A Hybrid Rule-Based/Bayesian Classifier. - Padhraic Smyth; Rodney M. Goodman; Charles M. Higgins (ECAI, 1990)

On Stochastic Complexity and Admissible Models for Neural Network Classifiers. - Padhraic Smyth (NIPS, 1990)

Decision tree design using information theory. - Rodney M. Goodman; Padhraic Smyth (Knowl. Acquis., 1990)

Rule Induction Using Information Theory. - Padhraic Smyth; Rodney M. Goodman (Knowledge Discovery in Databases, 1991)

Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. - Padhraic Smyth; Jeff Mellstrom (NIPS, 1991)

Detecting Novel Classes with Applications to Fault Diagnosis. - Padhraic Smyth; Jeff Mellstrom (ML, 1992)

Rule-Based Neural Networks for Classification and Probability Estimation. - Rodney M. Goodman; Charles M. Higgins; John W. Miller; Padhraic Smyth (Neural Comput., 1992)

An Information Theoretic Approach to Rule Induction from Databases. - Padhraic Smyth; Rodney M. Goodman (IEEE Trans. Knowl. Data Eng., 1992)

Self-clustering recurrent networks. - Zheng Zeng 006; Rodney M. Goodman; Padhraic Smyth (ICNN, 1993)

Probabilistic Anomaly Detection in Dynamic Systems. - Padhraic Smyth (NIPS, 1993)

Learning Finite State Machines With Self-Clustering Recurrent Networks. - Zheng Zeng 006; Rodney M. Goodman; Padhraic Smyth (Neural Comput., 1993)

On loss functions which minimize to conditional expected values and posterior proba- bilities. - John W. Miller; Rodney M. Goodman; Padhraic Smyth (IEEE Trans. Inf. Theory, 1993)

Automating the hunt for volcanoes on Venus. - Michael C. Burl; Usama M. Fayyad; Pietro Perona; Padhraic Smyth (CVPR, 1994)

The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach. - Usama M. Fayyad; Padhraic Smyth (DL, 1994)

Automated Analysis of Radar Imagery of Venus: Handling Lack of Ground Truth. - Michael C. Burl; Usama M. Fayyad; Pietro Perona; Padhraic Smyth (ICIP (3), 1994)

Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth. - Padhraic Smyth; Michael C. Burl; Usama M. Fayyad; Pietro Perona (KDD Workshop, 1994)

Inferring Ground Truth from Subjective Labelling of Venus Images. - Padhraic Smyth; Usama M. Fayyad; Michael C. Burl; Pietro Perona; Pierre Baldi (NIPS, 1994)

KDD-93: Progress and Challenges in Knowledge Discovery in Databases. - Gregory Piatetsky-Shapiro; Christopher J. Matheus; Padhraic Smyth; Ramasamy Uthurusamy (AI Mag., 1994)

Markov monitoring with unknown states. - Padhraic Smyth (IEEE J. Sel. Areas Commun., 1994)

Hidden Markov models for fault detection in dynamic system. - Padhraic Smyth (Pattern Recognit., 1994)

Discrete recurrent neural networks for grammatical inference. - Zheng Zeng 006; Rodney M. Goodman; Padhraic Smyth (IEEE Trans. Neural Networks, 1994)

Retrofitting Decision Tree Classifiers Using Kernel Density Estimation. - Padhraic Smyth; Alexander G. Gray; Usama M. Fayyad (ICML, 1995)

Automated Analysis and Exploration of Image Databases: Results, Progress, and Challenges. - Usama M. Fayyad; Padhraic Smyth; Nicholas Weir; S. George Djorgovski (J. Intell. Inf. Syst., 1995)

From Data Mining to Knowledge Discovery: An Overview. - Usama M. Fayyad; Gregory Piatetsky-Shapiro; Padhraic Smyth (Advances in Knowledge Discovery and Data Mining, 1996)

Modeling Subjective Uncertainty in Image Annotation. - Padhraic Smyth; Usama M. Fayyad; Michael C. Burl; Pietro Perona (Advances in Knowledge Discovery and Data Mining, 1996)

Knowledge Discovery and Data Mining: Towards a Unifying Framework. - Usama M. Fayyad; Gregory Piatetsky-Shapiro; Padhraic Smyth (KDD, 1996)

Clustering Using Monte Carlo Cross-Validation. - Padhraic Smyth (KDD, 1996)

Clustering Sequences with Hidden Markov Models. - Padhraic Smyth (NIPS, 1996)

From Data Mining to Knowledge Discovery in Databases. - Usama M. Fayyad; Gregory Piatetsky-Shapiro; Padhraic Smyth (AI Mag., 1996)

The KDD Process for Extracting Useful Knowledge from Volumes of Data. - Usama M. Fayyad; Gregory Piatetsky-Shapiro; Padhraic Smyth (Commun. ACM, 1996)

Statistical Inference and Data Mining. - Clark Glymour; David Madigan; Daryl Pregibon; Padhraic Smyth (Commun. ACM, 1996)

Bounds on the mean classification error rate of multiple experts. - Padhraic Smyth (Pattern Recognit. Lett., 1996)

Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. - William Rodman Shankle; Subramani Mani; Michael J. Pazzani; Padhraic Smyth (AIME, 1997)

Preface. - David Madigan; Padhraic Smyth (AISTATS, 1997)

Cross-validated Likelihood for Model Selection in Unsupervised Learning. - Padhraic Smyth (AISTATS, 1997)

Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach. - Subramani Mani; William Rodman Shankle; Michael J. Pazzani; Padhraic Smyth; Malcolm B. Dick (AMIA, 1997)

A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. - Eamonn J. Keogh; Padhraic Smyth (KDD, 1997)

Detecting Atmospheric Regimes Using Cross-Validated Clustering. - Padhraic Smyth; Michael Ghil; Kayo Ide; Joseph Roden; Andrew Fraser (KDD, 1997)

Anytime Exploratory Data Analysis for Massive Data Sets. - Padhraic Smyth; David H. Wolpert (KDD, 1997)

Stacked Density Estimation. - Padhraic Smyth; David H. Wolpert (NIPS, 1997)

Statistical Themes and Lessons for Data Mining. - Clark Glymour; David Madigan; Daryl Pregibon; Padhraic Smyth (Data Min. Knowl. Discov., 1997)

Learning with Probabilistic Representations. - Pat Langley; Gregory M. Provan; Padhraic Smyth (Mach. Learn., 1997)

Probabilistic Independence Networks for Hidden Markov Probability Models. - Padhraic Smyth; David Heckerman; Michael I. Jordan (Neural Comput., 1997)

Belief networks, hidden Markov models, and Markov random fields: A unifying view. - Padhraic Smyth (Pattern Recognit. Lett., 1997)

Applying classification algorithms in practice. - Carla E. Brodley; Padhraic Smyth (Stat. Comput., 1997)

Rule Discovery from Time Series. - Gautam Das 001; King-Ip Lin; Heikki Mannila; Gopal Renganathan; Padhraic Smyth (KDD, 1998)

Learning to Recognize Volcanoes on Venus. - Michael C. Burl; Lars Asker; Padhraic Smyth; Usama M. Fayyad; Pietro Perona; Larry Crumpler; Jayne Aubele (Mach. Learn., 1998)

Joint probabilistic clustering of multivariate and sequential data. - Padhraic Smyth (AISTATS, 1999)

Hierarchical Models for Screening of Iron Deficiency Anemia. - Igor V. Cadez; Christine E. McLaren; Padhraic Smyth; Geoffrey J. McLachlan (ICML, 1999)

Trajectory Clustering with Mixtures of Regression Models. - Scott Gaffney; Padhraic Smyth (KDD, 1999)

Prediction with Local Patterns using Cross-Entropy. - Heikki Mannila; Dmitry Pavlov; Padhraic Smyth (KDD, 1999)

Discovering Chinese Words from Unsegmented Text (poster abstract). - Xianping Ge; Wanda Pratt; Padhraic Smyth (SIGIR, 1999)

The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding - Xianping Ge; David Eppstein; Padhraic Smyth (CoRR, 1999)

Linearly Combining Density Estimators via Stacking. - Padhraic Smyth; David H. Wolpert (Mach. Learn., 1999)

Discussion on the paper by Friedman and Fisher. - Padhraic Smyth (Stat. Comput., 1999)

Approximate Query Answering with Frequent Sets and Maximum Entropy. - Heikki Mannila; Padhraic Smyth (ICDE, 2000)

A general probabilistic framework for clustering individuals and objects. - Igor V. Cadez; Scott Gaffney; Padhraic Smyth (KDD, 2000)

Visualization of navigation patterns on a Web site using model-based clustering. - Igor V. Cadez; David Heckerman; Christopher Meek; Padhraic Smyth; Steven White (KDD, 2000)

Deformable Markov model templates for time-series pattern matching. - Xianping Ge; Padhraic Smyth (KDD, 2000)

Towards scalable support vector machines using squashing. - Dmitry Pavlov; Darya Chudova; Padhraic Smyth (KDD, 2000)

Model Complexity, Goodness of Fit and Diminishing Returns. - Igor V. Cadez; Padhraic Smyth (NIPS, 2000)

Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. - Dmitry Pavlov; Heikki Mannila; Padhraic Smyth (UAI, 2000)

Model selection for probabilistic clustering using cross-validated likelihood. - Padhraic Smyth (Stat. Comput., 2000)

The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. - Stephen D. Bay; Dennis F. Kibler; Michael J. Pazzani; Padhraic Smyth (SIGKDD Explor., 2000)

Principles of Data Mining - David J. Hand; Heikki Mannila; Padhraic Smyth (MIT Press, 2001)

Breaking out of the Black-Box: Research Challenges in Data Mining. - Padhraic Smyth (DMKD, 2001)

Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. - Igor V. Cadez; Padhraic Smyth; Heikki Mannila (KDD, 2001)

Probabilistic query models for transaction data. - Dmitry Pavlov; Padhraic Smyth (KDD, 2001)

Bayesian Predictive Profiles With Applications to Retail Transaction Data. - Igor V. Cadez; Padhraic Smyth (NIPS, 2001)

The distribution of loop lengths in graphical models for turbo decoding. - Xianping Ge; David Eppstein; Padhraic Smyth (IEEE Trans. Inf. Theory, 2001)

Probabilistic Model-Based Detection of Bent-Double Radio Galaxies. - Sergey Kirshner; Igor V. Cadez; Padhraic Smyth; Chandrika Kamath 001; Erick Cantú-Paz (ICPR (2), 2002)

Pattern discovery in sequences under a Markov assumption. - Darya Chudova; Padhraic Smyth (KDD, 2002)

Learning to Classify Galaxy Shapes Using the EM Algorithm. - Sergey Kirshner; Igor V. Cadez; Padhraic Smyth; Chandrika Kamath 001 (NIPS, 2002)

Learning with Mixture Models: Concepts and Applications. - Padhraic Smyth (PKDD, 2002)

Business applications of data mining. - Chidanand Apté; Bing Liu 001; Edwin P. D. Pednault; Padhraic Smyth (Commun. ACM, 2002)

Data-driven evolution of data mining algorithms. - Padhraic Smyth; Daryl Pregibon; Christos Faloutsos (Commun. ACM, 2002)

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. - Igor V. Cadez; Padhraic Smyth; Geoffrey J. McLachlan; Christine E. McLaren (Mach. Learn., 2002)

Modeling the Internet and the Web: Probabilistic Method and Algorithms - Pierre Baldi; Paolo Frasconi; Padhraic Smyth (John Wiley, 2003)

Curve Clustering with Random Effects Regression Mixtures. - Scott Gaffney; Padhraic Smyth (AISTATS, 2003)

Clustering Markov States into Equivalence Classes using SVD and Heuristic Search Algorithms. - Xianping Ge; Sridevi Parise; Padhraic Smyth (AISTATS, 2003)

Unsupervised Learning with Permuted Data. - Sergey Kirshner; Sridevi Parise; Padhraic Smyth (ICML, 2003)

Translation-invariant mixture models for curve clustering. - Darya Chudova; Scott Gaffney; Eric Mjolsness; Padhraic Smyth (KDD, 2003)

Algorithms for estimating relative importance in networks. - Scott White; Padhraic Smyth (KDD, 2003)

Gene Expression Clustering with Functional Mixture Models. - Darya Chudova; Christopher E. Hart; Eric Mjolsness; Padhraic Smyth (NIPS, 2003)

Approximate Query Answering by Model Averaging. - Dmitry Pavlov; Padhraic Smyth (SDM, 2003)

Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves. - Darya Chudova; Scott Gaffney; Padhraic Smyth (UAI, 2003)

Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. - Igor V. Cadez; David Heckerman; Christopher Meek; Padhraic Smyth; Steven White (Data Min. Knowl. Discov., 2003)

Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. - Darya Chudova; Padhraic Smyth (Data Min. Knowl. Discov., 2003)

Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. - Dmitry Pavlov; Heikki Mannila; Padhraic Smyth (IEEE Trans. Knowl. Data Eng., 2003)

Probabilistic author-topic models for information discovery. - Mark Steyvers; Padhraic Smyth; Michal Rosen-Zvi; Thomas Griffiths 001 (KDD, 2004)

Joint Probabilistic Curve Clustering and Alignment. - Scott Gaffney; Padhraic Smyth (NIPS, 2004)

Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models. - Seyoung Kim; Padhraic Smyth; Stefan Luther (UAI, 2004)

Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. - Sergey Kirshner; Padhraic Smyth; Andrew Robertson (UAI, 2004)

The Author-Topic Model for Authors and Documents. - Michal Rosen-Zvi; Thomas L. Griffiths 001; Mark Steyvers; Padhraic Smyth (UAI, 2004)

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance. - Kevin K. Lin; Darya Chudova; G. Wesley Hatfield; Padhraic Smyth; Bogi Andersen (Proc. Natl. Acad. Sci. USA, 2004)

EventRank: a framework for ranking time-varying networks. - Joshua O'Madadhain; Padhraic Smyth (LinkKDD, 2005)

Parametric Response Surface Models for Analysis of Multi-site fMRI Data. - Seyoung Kim; Padhraic Smyth; Hal S. Stern; Jessica A. Turner (MICCAI, 2005)

A Spectral Clustering Approach To Finding Communities in Graph. - Scott White; Padhraic Smyth (SDM, 2005)

Prediction and ranking algorithms for event-based network data. - Joshua O'Madadhain; Jon Hutchins; Padhraic Smyth (SIGKDD Explor., 2005)

Data-Driven Discovery Using Probabilistic Hidden Variable Models. - Padhraic Smyth (ALT, 2006)

Analyzing Entities and Topics in News Articles Using Statistical Topic Models. - David Newman 001; Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (ISI, 2006)

Adaptive event detection with time-varying poisson processes. - Alexander Ihler; Jon Hutchins; Padhraic Smyth (KDD, 2006)

Statistical entity-topic models. - David Newman 001; Chaitanya Chemudugunta; Padhraic Smyth (KDD, 2006)

A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data. - Seyoung Kim; Padhraic Smyth; Hal S. Stern (MICCAI (2), 2006)

Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (NIPS, 2006)

Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. - Alexander T. Ihler; Padhraic Smyth (NIPS, 2006)

Hierarchical Dirichlet Processes with Random Effects. - Seyoung Kim; Padhraic Smyth (NIPS, 2006)

Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. - Ian Porteous; Alexander T. Ihler; Padhraic Smyth; Max Welling (UAI, 2006)

Segmental Hidden Markov Models with Random Effects for Waveform Modeling. - Seyoung Kim; Padhraic Smyth (J. Mach. Learn. Res., 2006)

Imaging phenotypes and genotypes in schizophrenia. - Jessica A. Turner; Padhraic Smyth; Fabio Macciardi; James H. Fallon; James L. Kennedy; Steven G. Potkin (Neuroinformatics, 2006)

Infinite mixtures of trees. - Sergey Kirshner; Padhraic Smyth (ICML, 2007)

Subject metadata enrichment using statistical topic models. - David Newman 001; Kat Hagedorn; Chaitanya Chemudugunta; Padhraic Smyth (JCDL, 2007)

Distributed Inference for Latent Dirichlet Allocation. - David Newman 001; Arthur U. Asuncion; Padhraic Smyth; Max Welling (NIPS, 2007)

KDD Cup and workshop 2007. - James Bennett; Charles Elkan; Bing Liu 001; Padhraic Smyth; Domonkos Tikk (SIGKDD Explor., 2007)

Learning to detect events with Markov-modulated poisson processes. - Alexander T. Ihler; Jon Hutchins; Padhraic Smyth (ACM Trans. Knowl. Discov. Data, 2007)

Combining concept hierarchies and statistical topic models. - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (CIKM, 2008)

Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set. - Jon Hutchins; Alexander Ihler; Padhraic Smyth (KDD Workshop on Knowledge Discovery from Sensor Data, 2008)

Fast collapsed gibbs sampling for latent dirichlet allocation. - Ian Porteous; David Newman 001; Alexander Ihler; Arthur U. Asuncion; Padhraic Smyth; Max Welling (KDD, 2008)

Asynchronous Distributed Learning of Topic Models. - Arthur U. Asuncion; Padhraic Smyth; Max Welling (NIPS, 2008)

Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. - Chaitanya Chemudugunta; America Holloway; Padhraic Smyth; Mark Steyvers (ISWC, 2008)

Text Modeling using Unsupervised Topic Models and Concept Hierarchies - Chaitanya Chemudugunta; Padhraic Smyth; Mark Steyvers (CoRR, 2008)

Particle-based Variational Inference for Continuous Systems. - Alexander Ihler; Andrew J. Frank; Padhraic Smyth (NIPS, 2009)

On Smoothing and Inference for Topic Models. - Arthur U. Asuncion; Max Welling; Padhraic Smyth; Yee Whye Teh (UAI, 2009)

Bayesian detection of non-sinusoidal periodic patterns in circadian expression data. - Darya Chudova; Alexander Ihler; Kevin K. Lin; Bogi Andersen; Padhraic Smyth (Bioinform., 2009)

Distributed Algorithms for Topic Models. - David Newman 001; Arthur U. Asuncion; Padhraic Smyth; Max Welling (J. Mach. Learn. Res., 2009)

Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. - Arthur U. Asuncion; Qiang Liu 001; Alexander T. Ihler; Padhraic Smyth (ICML, 2010)

Modeling relational events via latent classes. - Christopher DuBois; Padhraic Smyth (KDD, 2010)

Learning concept graphs from text with stick-breaking priors. - America Chambers; Padhraic Smyth; Mark Steyvers (NIPS, 2010)

Estimating replicate time shifts using Gaussian process regression. - Qiang Liu 001; Kevin K. Lin; Bogi Andersen; Padhraic Smyth; Alexander Ihler (Bioinform., 2010)

Technical perspective - Creativity helps influence prediction precision. - Padhraic Smyth; Charles Elkan (Commun. ACM, 2010)

Learning with Blocks: Composite Likelihood and Contrastive Divergence. - Arthur U. Asuncion; Qiang Liu 001; Alexander Ihler; Padhraic Smyth (AISTATS, 2010)

A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data. - Seyoung Kim; Padhraic Smyth; Hal S. Stern (IEEE Trans. Medical Imaging, 2010)

Learning author-topic models from text corpora. - Michal Rosen-Zvi; Chaitanya Chemudugunta; Thomas L. Griffiths 001; Padhraic Smyth; Mark Steyvers (ACM Trans. Inf. Syst., 2010)

Dynamic Egocentric Models for Citation Networks. - Duy Quang Vu; Arthur U. Asuncion; David R. Hunter; Padhraic Smyth (ICML, 2011)

Latent Set Models for Two-Mode Network Data. - Christopher DuBois; James R. Foulds; Padhraic Smyth (ICWSM, 2011)

Continuous-Time Regression Models for Longitudinal Networks. - Duy Quang Vu; Arthur U. Asuncion; David R. Hunter; Padhraic Smyth (NIPS, 2011)

Multi-Instance Mixture Models. - James R. Foulds; Padhraic Smyth (SDM, 2011)

A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks. - James R. Foulds; Christopher DuBois; Arthur U. Asuncion; Carter T. Butts; Padhraic Smyth (AISTATS, 2011)

Revisiting MAP Estimation, Message Passing and Perfect Graphs. - James R. Foulds; Nicholas Navaroli; Padhraic Smyth; Alexander Ihler (AISTATS, 2011)

Combining Background Knowledge and Learned Topics. - Mark Steyvers; Padhraic Smyth; Chaitanya Chemudugunta (Top. Cogn. Sci., 2011)

Predictive modeling of cardiovascular complications in incident hemodialysis patients. - Jasmine Ion Titapiccolo; Manuela Ferrario; Carlo Barbieri; Daniele Marcelli; Flavio Mari; Emanuele Gatti; Sergio Cerutti; Padhraic Smyth; Maria G. Signorini (EMBC, 2012)

Analyzing Text and Social Network Data with Probabilistic Models. - Padhraic Smyth (ECML/PKDD (1), 2012)

A graphical model representation of the track-oriented multiple hypothesis tracker. - Andrew J. Frank; Padhraic Smyth; Alexander Ihler (SSP, 2012)

Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models - Seyoung Kim; Padhraic Smyth; Stefan Luther (CoRR, 2012)

Statistical Models for Exploring Individual Email Communication Behavior. - Nicholas Navaroli; Christopher DuBois; Padhraic Smyth (ACML, 2012)

Statistical topic models for multi-label document classification. - Timothy N. Rubin; America Chambers; Padhraic Smyth; Mark Steyvers (Mach. Learn., 2012)

TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling. - Brynjar Gretarsson; John O'Donovan; Svetlin Bostandjiev; Tobias Höllerer; Arthur U. Asuncion; David Newman 001; Padhraic Smyth (ACM Trans. Intell. Syst. Technol., 2012)

Special issue on best of SIGKDD 2011. - Joydeep Ghosh; Padhraic Smyth; Andrew Tomkins; Rich Caruana (ACM Trans. Knowl. Discov. Data, 2012)

Stochastic blockmodeling of relational event dynamics. - Christopher DuBois; Carter T. Butts; Padhraic Smyth (AISTATS, 2013)

Modeling Scientific Impact with Topical Influence Regression. - James R. Foulds; Padhraic Smyth (EMNLP, 2013)

Text-based measures of document diversity. - Kevin Bache; David Newman 001; Padhraic Smyth (KDD, 2013)

Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. - James R. Foulds; Levi Boyles; Christopher DuBois; Padhraic Smyth; Max Welling (KDD, 2013)

Recommending patents based on latent topics. - Ralf Krestel; Padhraic Smyth (RecSys, 2013)

Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges. - Michael J. Bannister; Christopher DuBois; David Eppstein; Padhraic Smyth (SODA, 2013)

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013). - Ann E. Nicholson; Padhraic Smyth (CoRR, 2013)

Probabilistic Models for Query Approximation with Large Sparse Binary Datasets - Dmitry Pavlov; Heikki Mannila; Padhraic Smyth (CoRR, 2013)

Modeling individual email patterns over time with latent variable models. - Nicholas Navaroli; Christopher DuBois; Padhraic Smyth (Mach. Learn., 2013)

Approximate Slice Sampling for Bayesian Posterior Inference. - Christopher DuBois; Anoop Korattikara Balan; Max Welling; Padhraic Smyth (AISTATS, 2014)

Modeling human location data with mixtures of kernel densities. - Moshe Lichman; Padhraic Smyth (KDD, 2014)

Annealing Paths for the Evaluation of Topic Models. - James R. Foulds; Padhraic Smyth (UAI, 2014)

Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker. - Andrew J. Frank; Padhraic Smyth; Alexander Ihler (IEEE Trans. Signal Process., 2014)

Modeling Response Time in Digital Human Communication. - Nicholas Martin Navaroli; Padhraic Smyth (ICWSM, 2015)

From Group to Individual Labels Using Deep Features. - Dimitrios Kotzias; Misha Denil; Nando de Freitas; Padhraic Smyth (KDD, 2015)

Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing. - Michael Tanana; Kevin Hallgren; Zac E. Imel; David C. Atkins; Padhraic Smyth; Vivek Srikumar (CLPsych@HLT-NAACL, 2015)

Hot Swapping for Online Adaptation of Optimization Hyperparameters. - Kevin Bache; Dennis DeCoste; Padhraic Smyth (ICLR (Workshop), 2015)

Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. - Jihyun Park; Margaret Blume-Kohout; Ralf Krestel; Eric T. Nalisnick; Padhraic Smyth (AAAI Workshop: Scholarly Big Data, 2016)

Personalized location models with adaptive mixtures. - Moshe Lichman; Dimitrios Kotzias; Padhraic Smyth (SIGSPATIAL/GIS, 2016)

Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences. - Petter Arnesen; Tracy Holsclaw; Padhraic Smyth (Technometrics, 2016)

Stick-Breaking Variational Autoencoders. - Eric T. Nalisnick; Padhraic Smyth (ICLR (Poster), 2017)

Variational Reference Priors. - Eric T. Nalisnick; Padhraic Smyth (ICLR (Workshop), 2017)

Detecting changes in student behavior from clickstream data. - Jihyun Park; Kameryn Denaro; Fernando Rodriguez; Padhraic Smyth; Mark Warschauer (LAK, 2017)

Learning Approximately Objective Priors. - Eric T. Nalisnick; Padhraic Smyth (UAI, 2017)

Analyzing user-event data using score-based likelihood ratios with marked point processes. - Christopher Galbraith; Padhraic Smyth (Digit. Investig., 2017)

Science and data science. - David M. Blei; Padhraic Smyth (Proc. Natl. Acad. Sci. USA, 2017)

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. - Garren Gaut; Mark Steyvers; Zac E. Imel; David C. Atkins; Padhraic Smyth (IEEE J. Biomed. Health Informatics, 2017)

Learning Priors for Invariance. - Eric T. Nalisnick; Padhraic Smyth (AISTATS, 2018)

Understanding Student Procrastination via Mixture Models. - Jihyun Park; Renzhe Yu; Fernando Rodriguez; Rachel B. Baker; Padhraic Smyth; Mark Warschauer (EDM, 2018)

Bayesian Trees for Automated Cytometry Data Analysis. - Disi Ji; Eric T. Nalisnick; Yu Qian; Richard H. Scheuermann; Padhraic Smyth (MLHC, 2018)

Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression. - Moshe Lichman; Padhraic Smyth (WWW, 2018)

Unifying the Dropout Family Through Structured Shrinkage Priors. - Eric T. Nalisnick; Padhraic Smyth (CoRR, 2018)

Automating Data Science (Dagstuhl Seminar 18401). - Tijl De Bie; Luc De Raedt; Holger H. Hoos; Padhraic Smyth (Dagstuhl Reports, 2018)

Dropout as a Structured Shrinkage Prior. - Eric T. Nalisnick; José Miguel Hernández-Lobato; Padhraic Smyth (ICML, 2019)

Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions. - Jihyun Park; Dimitrios Kotzias; Patty Kuo; Robert L. Logan IV; Kritzia Merced; Sameer Singh 001; Michael Tanana; Efi Karra Taniskidou; Jennifer Elston-Lafata; David C. Atkins; Ming Tai-Seale; Zac E. Imel; et al. (J. Am. Medical Informatics Assoc., 2019)

Predicting Consumption Patterns with Repeated and Novel Events. - Dimitrios Kotzias; Moshe Lichman; Padhraic Smyth (IEEE Trans. Knowl. Data Eng., 2019)

User-Dependent Neural Sequence Models for Continuous-Time Event Data. - Alex Boyd; Robert Bamler; Stephan Mandt; Padhraic Smyth (NeurIPS, 2020)

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. - Disi Ji; Padhraic Smyth; Mark Steyvers (NeurIPS, 2020)

Variational Beam Search for Online Learning with Distribution Shifts. - Aodong Li; Alex Boyd; Padhraic Smyth; Stephan Mandt (CoRR, 2020)

Statistical Methods for the Forensic Analysis of Geolocated Event Data. - Christopher Galbraith; Padhraic Smyth; Hal S. Stern (Digit. Investig., 2020)

Forecasting Daily Wildfire Activity Using Poisson Regression. - Casey A. Graff; Shane R. Coffield; Yang Chen 058; Efi Foufoula-Georgiou; James T. Randerson; Padhraic Smyth (IEEE Trans. Geosci. Remote. Sens., 2020)

Active Bayesian Assessment of Black-Box Classifiers. - Disi Ji; Robert L. Logan IV; Padhraic Smyth; Mark Steyvers (AAAI, 2021)

Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions. - Preston Putzel; Hyungrok Do; Alex Boyd; Hua Zhong; Padhraic Smyth (MLHC, 2021)

Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. - Gavin Kerrigan; Padhraic Smyth; Mark Steyvers (NeurIPS, 2021)

Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. - Aodong Li; Alex Boyd; Padhraic Smyth; Stephan Mandt (NeurIPS, 2021)

Dynamic Survival Analysis with Individualized Truncated Parametric Distributions. - Preston Putzel; Padhraic Smyth; Jaehong Yu; Hua Zhong (SPACA, 2021)

Automating Data Science: Prospects and Challenges. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (CoRR, 2021)

Fair Generalized Linear Models with a Convex Penalty. - Hyungrok Do; Preston Putzel; Axel S. Martin; Padhraic Smyth; Judy Zhong (ICML, 2022)

Predictive Querying for Autoregressive Neural Sequence Models. - Alex Boyd; Samuel Showalter; Stephan Mandt; Padhraic Smyth (NeurIPS, 2022)

Automating data science. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (Commun. ACM, 2022)

Variable-Based Calibration for Machine Learning Classifiers. - Markelle Kelly; Padhraic Smyth (AAAI, 2023)

Probabilistic Querying of Continuous-Time Event Sequences. - Alex Boyd; Yuxin Chang; Stephan Mandt; Padhraic Smyth (AISTATS, 2023)

Diffusion Generative Models in Infinite Dimensions. - Gavin Kerrigan; Justin Ley; Padhraic Smyth (AISTATS, 2023)

Capturing Humans' Mental Models of AI: An Item Response Theory Approach. - Markelle Kelly; Aakriti Kumar; Padhraic Smyth; Mark Steyvers (FAccT, 2023)

Deep Anomaly Detection under Labeling Budget Constraints. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Stephan Mandt; Maja Rudolph (ICML, 2023)

When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. - Hyungrok Do; Yuxin Chang; Yoon-Sang Cho; Padhraic Smyth; Judy Zhong (MLHC, 2023)

Zero-Shot Anomaly Detection via Batch Normalization. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (NeurIPS, 2023)

Inference for mark-censored temporal point processes. - Alex Boyd; Yuxin Chang; Stephan Mandt; Padhraic Smyth (UAI, 2023)

Zero-Shot Anomaly Detection without Foundation Models. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2023)

A cell-level discriminative neural network model for diagnosis of blood cancers. - Edgar E. Robles; Ye Jin; Padhraic Smyth; Richard H. Scheuermann; Jack D. Bui; Huan-You Wang; Jean Oak; Yu Qian (Bioinform., 2023)

Probabilistic Modeling for Sequences of Sets in Continuous-Time. - Yuxin Chang; Alex J. Boyd; Padhraic Smyth (AISTATS, 2024)

Functional Flow Matching. - Gavin Kerrigan; Giosue Migliorini; Padhraic Smyth (AISTATS, 2024)

Bayesian Online Learning for Consensus Prediction. - Samuel Showalter; Alex J. Boyd; Padhraic Smyth; Mark Steyvers (AISTATS, 2024)

Perceptions of Linguistic Uncertainty by Language Models and Humans. - Catarina G. Belém; Markelle Kelly; Mark Steyvers; Sameer Singh 001; Padhraic Smyth (EMNLP, 2024)

Dynamic Conditional Optimal Transport through Simulation-Free Flows. - Gavin Kerrigan; Giosue Migliorini; Padhraic Smyth (NeurIPS, 2024)

Benchmark Data Repositories for Better Benchmarking. - Rachel Longjohn; Markelle Kelly; Sameer Singh 001; Padhraic Smyth (NeurIPS, 2024)

The Calibration Gap between Model and Human Confidence in Large Language Models. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (CoRR, 2024)

Anomaly Detection of Tabular Data Using LLMs. - Aodong Li; Yunhan Zhao; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2024)

EventFlow: Forecasting Continuous-Time Event Data with Flow Matching. - Gavin Kerrigan; Kai Nelson; Padhraic Smyth (CoRR, 2024)

Deep Linear Hawkes Processes. - Yuxin Chang; Alex Boyd; Cao Xiao; Taha A. Kass-Hout; Parminder Bhatia; Padhraic Smyth; Andrew Warrington (CoRR, 2024)

Understanding Gender Bias in AI-Generated Product Descriptions. - Markelle Kelly; Mohammad Tahaei; Padhraic Smyth; Lauren Wilcox (FAccT, 2025)

ELBOing Stein: Variational Bayes with Stein Mixture Inference. - Ola Rønning; Eric T. Nalisnick; Christophe Ley; Padhraic Smyth; Thomas Hamelryck (ICLR, 2025)

What large language models know and what people think they know. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (Nat. Mac. Intell., 2025)

A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations. - Clément Guilloteau; Gavin Kerrigan; Kai Nelson; Giosue Migliorini; Padhraic Smyth; Runze Li; Efi Foufoula-Georgiou (IEEE Trans. Geosci. Remote. Sens., 2025)

Semantic Probabilistic Control of Language Models. - Kareem Ahmed; Catarina G. Belém; Padhraic Smyth; Sameer Singh 001 (CoRR, 2025)

Bayesian Inference for Correlated Human Experts and Classifiers. - Markelle Kelly; Alex Boyd; Samuel Showalter; Mark Steyvers; Padhraic Smyth (CoRR, 2025)

JANET: Joint Adaptive predictioN-region Estimation for Time-series. - Eshant English; Eliot Wong-Toi; Matteo Fontana; Stephan Mandt; Padhraic Smyth; Christoph Lippert (Mach. Learn., 2025)