Smyth, Padhraic

94 publications

ICML 2025 Bayesian Inference for Correlated Human Experts and Classifiers Markelle Kelly, Alex James Boyd, Sam Showalter, Mark Steyvers, Padhraic Smyth

NeurIPS 2025 Deep Continuous-Time State-Space Models for Marked Event Sequences Yuxin Chang, Alex James Boyd, Cao Xiao, Taha Kass-Hout, Parminder Bhatia, Padhraic Smyth, Andrew Warrington

ICLR 2025 ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning, Eric Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck

MLJ 2025 JANET: Joint Adaptive predictioN-Region Estimation for Time-Series Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert

AISTATS 2024 Bayesian Online Learning for Consensus Prediction Samuel Showalter, Alex J Boyd, Padhraic Smyth, Mark Steyvers

NeurIPS 2024 Benchmark Data Repositories for Better Benchmarking Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth

NeurIPS 2024 Dynamic Conditional Optimal Transport Through Simulation-Free Flows Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth

AISTATS 2024 Functional Flow Matching Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth

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

NeurIPSW 2024 Variational Inference for Interacting Particle Systems with Discrete Latent States Giosue Migliorini, Padhraic Smyth

ICML 2023 Deep Anomaly Detection Under Labeling Budget Constraints Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph

AISTATS 2023 Diffusion Generative Models in Infinite Dimensions Gavin Kerrigan, Justin Ley, Padhraic Smyth

MLHC 2023 Fair Survival Time Prediction via Mutual Information Minimization Hyungrok Do, Yuxin Chang, Yoon Sang Cho, Padhraic Smyth, Judy Zhong

UAI 2023 Inference for Mark-Censored Temporal Point Processes Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth

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

AAAI 2023 Variable-Based Calibration for Machine Learning Classifiers Markelle Kelly, Padhraic Smyth

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

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

NeurIPS 2022 Predictive Querying for Autoregressive Neural Sequence Models Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth

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

NeurIPS 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

MLHC 2021 Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, 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 User-Dependent Neural Sequence Models for Continuous-Time Event Data Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

ICML 2019 Dropout as a Structured Shrinkage Prior Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth

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

AISTATS 2018 Learning Priors for Invariance Eric T. Nalisnick, Padhraic Smyth

UAI 2017 Learning Approximately Objective Priors Eric T. Nalisnick, Padhraic Smyth

ICLR 2017 Stick-Breaking Variational Autoencoders Eric T. Nalisnick, Padhraic Smyth

ICLR 2017 Variational Reference Priors Eric T. Nalisnick, Padhraic Smyth

ICLR 2015 Hot Swapping for Online Adaptation of Optimization Hyperparameters Kevin Bache, Dennis DeCoste, Padhraic Smyth

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

AISTATS 2014 Approximate Slice Sampling for Bayesian Posterior Inference Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth

MLJ 2013 Modeling Individual Email Patterns over Time with Latent Variable Models Nicholas Navaroli, Christopher DuBois, Padhraic Smyth

UAI 2013 Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013 Ann E. Nicholson, Padhraic Smyth

AISTATS 2013 Stochastic Blockmodeling of Relational Event Dynamics Christopher DuBois, Carter T. Butts, Padhraic Smyth

ECML-PKDD 2012 Analyzing Text and Social Network Data with Probabilistic Models Padhraic Smyth

ACML 2012 Statistical Models for Exploring Individual Email Communication Behavior Nicholas Navaroli, Christopher DuBois, Padhraic Smyth

MLJ 2012 Statistical Topic Models for Multi-Label Document Classification Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers

AISTATS 2011 A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks James Foulds, Christopher DuBois, Arthur Asuncion, Carter Butts, Padhraic Smyth

NeurIPS 2011 Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion

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

AISTATS 2011 Revisiting MAP Estimation, Message Passing and Perfect Graphs James Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander Ihler

NeurIPS 2010 Learning Concept Graphs from Text with Stick-Breaking Priors America Chambers, Padhraic Smyth, Mark Steyvers

AISTATS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth

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

JMLR 2009 Distributed Algorithms for Topic Models David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling

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

NeurIPS 2009 Particle-Based Variational Inference for Continuous Systems Andrew Frank, Padhraic Smyth, Alexander T. Ihler

NeurIPS 2008 Asynchronous Distributed Learning of Topic Models Padhraic Smyth, Max Welling, Arthur U. Asuncion

NeurIPS 2007 Distributed Inference for Latent Dirichlet Allocation David Newman, Padhraic Smyth, Max Welling, Arthur U. Asuncion

ICML 2007 Infinite Mixtures of Trees Sergey Kirshner, Padhraic Smyth

ALT 2006 Data-Driven Discovery Using Probabilistic Hidden Variable Models Padhraic Smyth

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

NeurIPS 2006 Hierarchical Dirichlet Processes with Random Effects Seyoung Kim, Padhraic Smyth

NeurIPS 2006 Learning Time-Intensity Profiles of Human Activity Using Non-Parametric Bayesian Models Alexander T. Ihler, Padhraic Smyth

NeurIPS 2006 Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers

JMLR 2006 Segmental Hidden Markov Models with Random Effects for Waveform Modeling Seyoung Kim, Padhraic Smyth

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

NeurIPS 2004 Joint Probabilistic Curve Clustering and Alignment Scott J. Gaffney, Padhraic Smyth

UAI 2004 Modeling Waveform Shapes with Random E Ects Segmental Hidden Markov Models Seyoung Kim, Padhraic Smyth, Stefan Luther

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

AISTATS 2003 Clustering Markov States into Equivalence Classes Using SVD and Heuristic Search Algorithms Xianping Ge, Sridevi Parise, Padhraic Smyth

AISTATS 2003 Curve Clustering with Random Effects Regression Mixtures Scott Gaffney, Padhraic Smyth

NeurIPS 2003 Gene Expression Clustering with Functional Mixture Models Darya Chudova, Christopher Hart, Eric Mjolsness, Padhraic Smyth

UAI 2003 Probabilistic Models for Joint Clustering and Time-Warping of Multidimensional Curves Darya Chudova, Scott Gaffney, Padhraic Smyth

ICML 2003 Unsupervised Learning with Permuted Data Sergey Kirshner, Sridevi Parise, Padhraic Smyth

NeurIPS 2002 Learning to Classify Galaxy Shapes Using the EM Algorithm Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath

ECML-PKDD 2002 Learning with Mixture Models: Concepts and Applications Padhraic Smyth

MLJ 2002 Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren

NeurIPS 2001 Bayesian Predictive Profiles with Applications to Retail Transaction Data Igor V. Cadez, Padhraic Smyth

NeurIPS 2000 Model Complexity, Goodness of Fit and Diminishing Returns Igor V. Cadez, Padhraic Smyth

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

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

MLJ 1999 Linearly Combining Density Estimators via Stacking Padhraic Smyth, David H. Wolpert

MLJ 1998 Learning to Recognize Volcanoes on Venus Michael C. Burl, Lars Asker, Padhraic Smyth, Usama M. Fayyad, Pietro Perona, Larry Crumpler, Jayne Aubele

AISTATS 1997 Cross-Validated Likelihood for Model Selection in Unsupervised Learning Padhraic Smyth

MLJ 1997 Learning with Probabilistic Representations Pat Langley, Gregory M. Provan, Padhraic Smyth

AISTATS 1997 Preface David Madigan, Padhraic Smyth

NeCo 1997 Probabilistic Independence Networks for Hidden Markov Probability Models Padhraic Smyth, David Heckerman, Michael I. Jordan

NeurIPS 1997 Stacked Density Estimation Padhraic Smyth, David Wolpert

NeurIPS 1996 Clustering Sequences with Hidden Markov Models Padhraic Smyth

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

CVPR 1994 Automating the Hunt for Volcanoes on Venus Michael C. Burl, Usama M. Fayyad, Pietro Perona, Padhraic Smyth

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

NeCo 1993 Learning Finite State Machines with Self-Clustering Recurrent Networks Zheng Zeng, Rodney M. Goodman, Padhraic Smyth

NeurIPS 1993 Probabilistic Anomaly Detection in Dynamic Systems Padhraic Smyth

ICML 1992 Detecting Novel Classes with Applications to Fault Diagnosis Padhraic Smyth, Jeff Mellstrom

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

NeurIPS 1991 Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models Padhraic Smyth, Jeff Mellstrom

NeurIPS 1990 On Stochastic Complexity and Admissible Models for Neural Network Classifiers Padhraic Smyth

ICML 1989 The Induction of Probabilistic Rule Sets - The Itrule Algorithm Rodney M. Goodman, Padhraic Smyth

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