Padhraic Smyth

ORCID

Publication Activity (10 Years)

Years Active: 1988-2024
Publications (10 Years): 65

Top Venues

Publications

Mark Steyvers, Heliodoro Tejeda Lemus, Aakriti Kumar, Catarina Belém, Sheer Karny, Xinyue Hu, Lukas Mayer, Padhraic Smyth
The Calibration Gap between Model and Human Confidence in Large Language Models. CoRR (2024)
Samuel Showalter, Alex J. Boyd, Padhraic Smyth, Mark Steyvers
Bayesian Online Learning for Consensus Prediction. AISTATS (2024)
Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Anomaly Detection of Tabular Data Using LLMs. CoRR (2024)
Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert
JANET: Joint Adaptive predictioN-region Estimation for Time-series. CoRR (2024)
Yuxin Chang, Alex J. Boyd, Padhraic Smyth
Probabilistic Modeling for Sequences of Sets in Continuous-Time. AISTATS (2024)
Catarina G. Belém, Markelle Kelly, Mark Steyvers, Sameer Singh, Padhraic Smyth
Perceptions of Linguistic Uncertainty by Language Models and Humans. CoRR (2024)
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
Dynamic Conditional Optimal Transport through Simulation-Free Flows. CoRR (2024)
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
Functional Flow Matching. AISTATS (2024)
Markelle Kelly, Padhraic Smyth
Variable-Based Calibration for Machine Learning Classifiers. AAAI (2023)
Yuxin Chang, Alex Boyd, Padhraic Smyth
Probabilistic Modeling for Sequences of Sets in Continuous-Time. CoRR (2023)
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
Functional Flow Matching. CoRR (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection without Foundation Models. CoRR (2023)
Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. CoRR (2023)
Edgar E. Robles, Ye Jin, Padhraic Smyth, Richard H. Scheuermann, Jack D. Bui, Huan-You Wang, Jean Oak, Yu Qian
A cell-level discriminative neural network model for diagnosis of blood cancers. Bioinform. 39 (10) (2023)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Inference for mark-censored temporal point processes. UAI (2023)
Samuel Showalter, Alex Boyd, Padhraic Smyth, Mark Steyvers
Bayesian Online Learning for Consensus Prediction. CoRR (2023)
Hyungrok Do, Yuxin Chang, Yoon-Sang Cho, Padhraic Smyth, Judy Zhong
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. MLHC (2023)
Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. CoRR (2023)
Gavin Kerrigan, Justin Ley, Padhraic Smyth
Diffusion Generative Models in Infinite Dimensions. AISTATS (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. ICML (2023)
Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. FAccT (2023)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Probabilistic Querying of Continuous-Time Event Sequences. AISTATS (2023)
Gavin Kerrigan, Justin Ley, Padhraic Smyth
Diffusion Generative Models in Infinite Dimensions. CoRR (2022)
Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong
Fair Generalized Linear Models with a Convex Penalty. ICML (2022)
Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
Automating data science. Commun. ACM 65 (3) (2022)
Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong
Fair Generalized Linear Models with a Convex Penalty. CoRR (2022)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Probabilistic Querying of Continuous-Time Event Sequences. CoRR (2022)
Markelle Kelly, Padhraic Smyth
Variable-Based Calibration for Machine Learning Classifiers. CoRR (2022)
Alex Boyd, Sam Showalter, Stephan Mandt, Padhraic Smyth
Predictive Querying for Autoregressive Neural Sequence Models. CoRR (2022)
Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth
Predictive Querying for Autoregressive Neural Sequence Models. NeurIPS (2022)
Gavin Kerrigan, Padhraic Smyth, Mark Steyvers
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. NeurIPS (2021)
Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
Automating Data Science: Prospects and Challenges. CoRR (2021)
Gavin Kerrigan, Padhraic Smyth, Mark Steyvers
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. CoRR (2021)
Preston Putzel, Padhraic Smyth, Jaehong Yu, Hua Zhong
Dynamic Survival Analysis with Individualized Truncated Parametric Distributions. SPACA (2021)
Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. NeurIPS (2021)
Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers
Active Bayesian Assessment of Black-Box Classifiers. AAAI (2021)
Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, Padhraic Smyth
Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions. MLHC (2021)
Disi Ji, Padhraic Smyth, Mark Steyvers
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. NeurIPS (2020)
Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS (2020)
Casey A. Graff, Shane R. Coffield, Yang Chen, Efi Foufoula-Georgiou, James T. Randerson, Padhraic Smyth
Forecasting Daily Wildfire Activity Using Poisson Regression. IEEE Trans. Geosci. Remote. Sens. 58 (7) (2020)
Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt
Variational Beam Search for Online Learning with Distribution Shifts. CoRR (2020)
Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
User-Dependent Neural Sequence Models for Continuous-Time Event Data. CoRR (2020)
Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers
Active Bayesian Assessment for Black-Box Classifiers. CoRR (2020)
Christopher Galbraith, Padhraic Smyth, Hal S. Stern
Statistical Methods for the Forensic Analysis of Geolocated Event Data. Digit. Investig. (2020)
Disi Ji, Padhraic Smyth, Mark Steyvers
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. CoRR (2020)
Jihyun Park, Dimitrios Kotzias, Patty Kuo, Robert L. Logan IV, Kritzia Merced, Sameer Singh, Michael Tanana, Efi Karra Taniskidou, Jennifer Elston-Lafata, David C. Atkins, Ming Tai-Seale, Zac E. Imel, Padhraic Smyth
Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions. J. Am. Medical Informatics Assoc. 26 (12) (2019)
Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
Dropout as a Structured Shrinkage Prior. ICML (2019)
Dimitrios Kotzias, Moshe Lichman, Padhraic Smyth
Predicting Consumption Patterns with Repeated and Novel Events. IEEE Trans. Knowl. Data Eng. 31 (2) (2019)
Eric T. Nalisnick, Padhraic Smyth
Unifying the Dropout Family Through Structured Shrinkage Priors. CoRR (2018)
Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth
Bayesian Trees for Automated Cytometry Data Analysis. MLHC (2018)
Moshe Lichman, Padhraic Smyth
Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression. WWW (2018)
Eric T. Nalisnick, Padhraic Smyth
Learning Priors for Invariance. AISTATS (2018)
Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8 (9) (2018)
Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel Baker, Padhraic Smyth, Mark Warschauer
Understanding Student Procrastination via Mixture Models. EDM (2018)
David M. Blei, Padhraic Smyth
Science and data science. Proc. Natl. Acad. Sci. USA 114 (33) (2017)
Eric T. Nalisnick, Padhraic Smyth
Variational Reference Priors. ICLR (Workshop) (2017)
Christopher Galbraith, Padhraic Smyth
Analyzing user-event data using score-based likelihood ratios with marked point processes. Digit. Investig. (2017)
Eric T. Nalisnick, Padhraic Smyth
Stick-Breaking Variational Autoencoders. ICLR (Poster) (2017)
Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, Mark Warschauer
Detecting changes in student behavior from clickstream data. LAK (2017)
Eric T. Nalisnick, Padhraic Smyth
Learning Approximately Objective Priors. UAI (2017)
Garren Gaut, Mark Steyvers, Zac E. Imel, David C. Atkins, Padhraic Smyth
Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. IEEE J. Biomed. Health Informatics 21 (2) (2017)
Petter Arnesen, Tracy Holsclaw, Padhraic Smyth
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences. Technometrics 58 (2) (2016)
Moshe Lichman, Dimitrios Kotzias, Padhraic Smyth
Personalized location models with adaptive mixtures. SIGSPATIAL/GIS (2016)
Jihyun Park, Margaret Blume-Kohout, Ralf Krestel, Eric T. Nalisnick, Padhraic Smyth
Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. AAAI Workshop: Scholarly Big Data (2016)
Michael Tanana, Kevin Hallgren, Zac E. Imel, David C. Atkins, Padhraic Smyth, Vivek Srikumar
Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing. CLPsych@HLT-NAACL (2015)
Dimitrios Kotzias, Misha Denil, Nando de Freitas, Padhraic Smyth
From Group to Individual Labels Using Deep Features. KDD (2015)
Nicholas Martin Navaroli, Padhraic Smyth
Modeling Response Time in Digital Human Communication. ICWSM (2015)
Kevin Bache, Dennis DeCoste, Padhraic Smyth
Hot Swapping for Online Adaptation of Optimization Hyperparameters. ICLR (Workshop) (2015)
James R. Foulds, Padhraic Smyth
Annealing Paths for the Evaluation of Topic Models. UAI (2014)
Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth
Approximate Slice Sampling for Bayesian Posterior Inference. AISTATS (2014)
Andrew J. Frank, Padhraic Smyth, Alexander T. Ihler
Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker. IEEE Trans. Signal Process. 62 (9) (2014)
Moshe Lichman, Padhraic Smyth
Modeling human location data with mixtures of kernel densities. KDD (2014)
Ralf Krestel, Padhraic Smyth
Recommending patents based on latent topics. RecSys (2013)
James R. Foulds, Padhraic Smyth
Modeling Scientific Impact with Topical Influence Regression. EMNLP (2013)
Dmitry Pavlov, Heikki Mannila, Padhraic Smyth
Probabilistic Models for Query Approximation with Large Sparse Binary Datasets CoRR (2013)
Christopher DuBois, Carter T. Butts, Padhraic Smyth
Stochastic blockmodeling of relational event dynamics. AISTATS (2013)
Michael J. Bannister, Christopher DuBois, David Eppstein, Padhraic Smyth
Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges. SODA (2013)
James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. KDD (2013)
Nicholas Navaroli, Christopher DuBois, Padhraic Smyth
Modeling individual email patterns over time with latent variable models. Mach. Learn. 92 (2-3) (2013)
Kevin Bache, David Newman, Padhraic Smyth
Text-based measures of document diversity. KDD (2013)
James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation CoRR (2013)
Ann E. Nicholson, Padhraic Smyth
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013). CoRR (2013)
Sergey Kirshner, Padhraic Smyth, Andrew Robertson
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series CoRR (2012)
Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. CoRR (2012)
Andrew J. Frank, Padhraic Smyth, Alexander T. Ihler
A graphical model representation of the track-oriented multiple hypothesis tracker. SSP (2012)
Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers
Statistical topic models for multi-label document classification. Mach. Learn. 88 (1-2) (2012)
Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
On Smoothing and Inference for Topic Models CoRR (2012)
Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana
Special issue on best of SIGKDD 2011. ACM Trans. Knowl. Discov. Data 6 (4) (2012)
Brynjar Gretarsson, John O'Donovan, Svetlin Bostandjiev, Tobias Höllerer, Arthur U. Asuncion, David Newman, Padhraic Smyth
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling. ACM Trans. Intell. Syst. Technol. 3 (2) (2012)
Padhraic Smyth
Analyzing Text and Social Network Data with Probabilistic Models. ECML/PKDD (1) (2012)
Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth
The Author-Topic Model for Authors and Documents CoRR (2012)
Nicholas Navaroli, Christopher DuBois, Padhraic Smyth
Statistical Models for Exploring Individual Email Communication Behavior. ACML (2012)
Seyoung Kim, Padhraic Smyth, Stefan Luther
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models CoRR (2012)
Michael J. Bannister, Christopher DuBois, David Eppstein, Padhraic Smyth
Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges CoRR (2012)
Jasmine Ion Titapiccolo, Manuela Ferrario, Carlo Barbieri, Daniele Marcelli, Flavio Mari, Emanuele Gatti, Sergio Cerutti, Padhraic Smyth, Maria G. Signorini
Predictive modeling of cardiovascular complications in incident hemodialysis patients. EMBC (2012)
James R. Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander T. Ihler
Revisiting MAP Estimation, Message Passing and Perfect Graphs. AISTATS (2011)
Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers
Statistical Topic Models for Multi-Label Document Classification CoRR (2011)
James R. Foulds, Padhraic Smyth
Multi-Instance Mixture Models. SDM (2011)
James R. Foulds, Christopher DuBois, Arthur U. Asuncion, Carter T. Butts, Padhraic Smyth
A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks. AISTATS (2011)

Padhraic Smyth.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Padhraic Smyth