Padhraic Smyth

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

Achievements

+15 more ↓

🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (33) 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (8) 🐣 Hot Topic Early Bird

🧭 Keyword Pioneer 🏃 Academic Marathon (19) 🗺️ Taxonomy Completionist (33) 🌟 Keyword Trendsetter Combo (4) 🔬 Deep Specialist (12) 🌱 Topic Pioneer 🏆 Grand Slam 🏆 Keyword Champion 🗃️ Keyword Collector (118) ⚡ Prolific Year (6) 🚀 Conference Pioneer 📈 Trend Setter ❓ The Questioner 💎 Century Club (43) 🔥 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) bayesian nonparametrics (4) importance sampling (3) active learning (3) markov chain monte carlo (3) semi-supervised learning (3) topic modeling (3) generative model (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