← Authors

Padhraic Smyth

43 papers · 2006–2025 · 10 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (10) 🌈 Renaissance Researcher (8) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer πŸƒ Academic Marathon (19) πŸ—ΊοΈ Taxonomy Completionist (33) 🌟 Keyword Trendsetter Combo (4) πŸ”¬ Deep Specialist (12) 🌱 Topic Pioneer πŸ† Grand Slam πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (118) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner πŸ’Ž Century Club (43) πŸ”₯ Unstoppable (9)

Conferences

NIPS (16) AISTATS (11) ICML (4) MLHC (3) AAAI (2) EMNLP (2) JMLR (2) ACML (1) ICLR (1) UAI (1)

Top co-authors

Mark Steyvers (8) Stephan Mandt (7) Alex Boyd (6) Christopher DuBois (4) Markelle Kelly (4) Gavin Kerrigan (4) Eric Nalisnick (4) Max Welling (4) Yuxin Chang (4) Hyungrok Do (3)

Keywords

bayesian inference (9) topic model (4) variational inference (4) probabilistic modeling (4) latent dirichlet allocation (4) importance sampling (3) active learning (3) markov chain monte carlo (3) semi-supervised learning (3) topic modeling (3) generative model (3) bayesian nonparametrics (3) hierarchical dirichlet process (3) gibbs sampling (3) survival analysis (3) network analysis (2) algorithmic fairness (2) message passing (2) distributed learning (2) conditional generation (2)

Papers

Bayesian Inference for Correlated Human Experts and Classifiers ICML 2025
ELBOing Stein: Variational Bayes with Stein Mixture Inference ICLR 2025
Bayesian Online Learning for Consensus Prediction AISTATS 2024
Perceptions of Linguistic Uncertainty by Language Models and Humans EMNLP 2024
Probabilistic Modeling for Sequences of Sets in Continuous-Time AISTATS 2024
Functional Flow Matching AISTATS 2024
Benchmark Data Repositories for Better Benchmarking NIPS 2024
Dynamic Conditional Optimal Transport through Simulation-Free Flows NIPS 2024
Zero-Shot Anomaly Detection via Batch Normalization NIPS 2023
Variable-Based Calibration for Machine Learning Classifiers AAAI 2023
Inference for mark-censored temporal point processes UAI 2023
Fair Survival Time Prediction via Mutual Information Minimization MLHC 2023
Diffusion Generative Models in Infinite Dimensions AISTATS 2023
Deep Anomaly Detection under Labeling Budget Constraints ICML 2023
Probabilistic Querying of Continuous-Time Event Sequences AISTATS 2023
Predictive Querying for Autoregressive Neural Sequence Models NIPS 2022
Fair Generalized Linear Models with a Convex Penalty ICML 2022
Active Bayesian Assessment of Black-Box Classifiers AAAI 2021
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration NIPS 2021
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning NIPS 2021
Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions MLHC 2021
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference NIPS 2020
User-Dependent Neural Sequence Models for Continuous-Time Event Data NIPS 2020
Dropout as a Structured Shrinkage Prior ICML 2019
Bayesian Trees for Automated Cytometry Data Analysis MLHC 2018
Learning Priors for Invariance AISTATS 2018
Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014
Modeling Scientific Impact with Topical Influence Regression EMNLP 2013
Stochastic blockmodeling of relational event dynamics AISTATS 2013
Statistical Models for Exploring Individual Email Communication Behavior ACML 2012
A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks AISTATS 2011
Continuous-Time Regression Models for Longitudinal Networks NIPS 2011
Revisiting MAP Estimation, Message Passing and Perfect Graphs AISTATS 2011
Learning concept graphs from text with stick-breaking priors NIPS 2010
Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010
Distributed Algorithms for Topic Models JMLR 2009
Particle-based Variational Inference for Continuous Systems NIPS 2009
Asynchronous Distributed Learning of Topic Models NIPS 2008
Distributed Inference for Latent Dirichlet Allocation NIPS 2007
Segmental Hidden Markov Models with Random Effects for Waveform Modeling JMLR 2006
Hierarchical Dirichlet Processes with Random Effects NIPS 2006
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models NIPS 2006
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model NIPS 2006