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ORCIDPadhraic Smyth
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- affiliation: University of California, Irvine, Department of Computer Science, CA, USA
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2020 – today
- 2024
[c143]Samuel Showalter, Alex J. Boyd, Padhraic Smyth, Mark Steyvers:
Bayesian Online Learning for Consensus Prediction. AISTATS 2024: 2539-2547- [c142]Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth:
Functional Flow Matching. AISTATS 2024: 3934-3942 - [c141]Yuxin Chang, Alex J. Boyd, Padhraic Smyth:
Probabilistic Modeling for Sequences of Sets in Continuous-Time. AISTATS 2024: 4357-4365 - [c140]Catarina G. Belém, Markelle Kelly, Mark Steyvers, Sameer Singh, Padhraic Smyth:
Perceptions of Linguistic Uncertainty by Language Models and Humans. EMNLP 2024: 8467-8502 - [i39]Mark Steyvers, Heliodoro Tejeda Lemus, Aakriti Kumar, Catarina G. Belém, Sheer Karny, Xinyue Hu, Lukas William Mayer, Padhraic Smyth:
The Calibration Gap between Model and Human Confidence in Large Language Models. CoRR abs/2401.13835 (2024) - [i38]Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth:
Dynamic Conditional Optimal Transport through Simulation-Free Flows. CoRR abs/2404.04240 (2024) - [i37]Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Anomaly Detection of Tabular Data Using LLMs. CoRR abs/2406.16308 (2024) - [i36]Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert:
JANET: Joint Adaptive predictioN-region Estimation for Time-series. CoRR abs/2407.06390 (2024) - [i35]Catarina G. Belém, Markelle Kelly, Mark Steyvers, Sameer Singh, Padhraic Smyth:
Perceptions of Linguistic Uncertainty by Language Models and Humans. CoRR abs/2407.15814 (2024) - [i34]Gavin Kerrigan, Kai Nelson, Padhraic Smyth:
EventFlow: Forecasting Continuous-Time Event Data with Flow Matching. CoRR abs/2410.07430 (2024) - [i33]Ola Rønning, Eric T. Nalisnick, Christophe Ley, Padhraic Smyth, Thomas Hamelryck:
ELBOing Stein: Variational Bayes with Stein Mixture Inference. CoRR abs/2410.22948 (2024) - [i32]Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth:
Benchmark Data Repositories for Better Benchmarking. CoRR abs/2410.24100 (2024) - 2023
- [j60]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) - [c139]Markelle Kelly, Padhraic Smyth:
Variable-Based Calibration for Machine Learning Classifiers. AAAI 2023: 8211-8219 - [c138]Gavin Kerrigan, Justin Ley, Padhraic Smyth:
Diffusion Generative Models in Infinite Dimensions. AISTATS 2023: 9538-9563 - [c137]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. AISTATS 2023: 10235-10251 - [c136]Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers:
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. FAccT 2023: 1723-1734 - [c135]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. ICML 2023: 19882-19910 - [c134]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: 128-149 - [c133]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS 2023 - [c132]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Inference for mark-censored temporal point processes. UAI 2023: 226-236 - [i31]Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. CoRR abs/2302.07832 (2023) - [i30]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection without Foundation Models. CoRR abs/2302.07849 (2023) - [i29]Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers:
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. CoRR abs/2305.09064 (2023) - [i28]Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth:
Functional Flow Matching. CoRR abs/2305.17209 (2023) - [i27]Samuel Showalter, Alex Boyd, Padhraic Smyth, Mark Steyvers:
Bayesian Online Learning for Consensus Prediction. CoRR abs/2312.07679 (2023) - [i26]Yuxin Chang, Alex Boyd, Padhraic Smyth:
Probabilistic Modeling for Sequences of Sets in Continuous-Time. CoRR abs/2312.15045 (2023) - 2022
- [j59]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): 76-87 (2022) - [c131]Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. ICML 2022: 5286-5308 - [c130]Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth:
Predictive Querying for Autoregressive Neural Sequence Models. NeurIPS 2022 - [i25]Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. CoRR abs/2206.09076 (2022) - [i24]Markelle Kelly, Padhraic Smyth:
Variable-Based Calibration for Machine Learning Classifiers. CoRR abs/2209.15154 (2022) - [i23]Alex Boyd, Samuel Showalter, Stephan Mandt, Padhraic Smyth:
Predictive Querying for Autoregressive Neural Sequence Models. CoRR abs/2210.06464 (2022) - [i22]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. CoRR abs/2211.08499 (2022) - [i21]Gavin Kerrigan, Justin Ley, Padhraic Smyth:
Diffusion Generative Models in Infinite Dimensions. CoRR abs/2212.00886 (2022) - 2021
- [c129]Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers:
Active Bayesian Assessment of Black-Box Classifiers. AAAI 2021: 7935-7944 - [c128]Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, Padhraic Smyth:
Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions. MLHC 2021: 648-673 - [c127]Gavin Kerrigan, Padhraic Smyth, Mark Steyvers:
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. NeurIPS 2021: 4421-4434 - [c126]Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. NeurIPS 2021: 6816-6828 - [c125]Preston Putzel, Padhraic Smyth, Jaehong Yu, Hua Zhong:
Dynamic Survival Analysis with Individualized Truncated Parametric Distributions. SPACA 2021: 159-170 - [i20]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 abs/2105.05699 (2021) - [i19]Gavin Kerrigan, Padhraic Smyth, Mark Steyvers:
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. CoRR abs/2109.14591 (2021) - 2020
- [j58]Christopher Galbraith, Padhraic Smyth, Hal S. Stern:
Statistical Methods for the Forensic Analysis of Geolocated Event Data. Digit. Investig. 33 Supplement: 301009 (2020) - [j57]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): 4837-4851 (2020) - [c124]Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS 2020 - [c123]Disi Ji, Padhraic Smyth, Mark Steyvers:
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. NeurIPS 2020 - [i18]Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers:
Active Bayesian Assessment for Black-Box Classifiers. CoRR abs/2002.06532 (2020) - [i17]Disi Ji, Padhraic Smyth, Mark Steyvers:
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference. CoRR abs/2010.09851 (2020) - [i16]Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. CoRR abs/2011.03231 (2020) - [i15]Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Variational Beam Search for Online Learning with Distribution Shifts. CoRR abs/2012.08101 (2020)
2010 – 2019
- 2019
- [j56]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): 1493-1504 (2019) - [j55]Dimitrios Kotzias, Moshe Lichman, Padhraic Smyth:
Predicting Consumption Patterns with Repeated and Novel Events. IEEE Trans. Knowl. Data Eng. 31(2): 371-384 (2019) - [c122]Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth:
Dropout as a Structured Shrinkage Prior. ICML 2019: 4712-4722 - 2018
- [c121]Eric T. Nalisnick, Padhraic Smyth:
Learning Priors for Invariance. AISTATS 2018: 366-375 - [c120]Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel B. Baker, Padhraic Smyth, Mark Warschauer:
Understanding Student Procrastination via Mixture Models. EDM 2018 - [c119]Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth:
Bayesian Trees for Automated Cytometry Data Analysis. MLHC 2018: 465-483 - [c118]Moshe Lichman, Padhraic Smyth:
Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression. WWW 2018: 719-728 - [i14]Eric T. Nalisnick, Padhraic Smyth:
Unifying the Dropout Family Through Structured Shrinkage Priors. CoRR abs/1810.04045 (2018) - [i13]Tijl De Bie, Luc De Raedt, Holger H. Hoos, Padhraic Smyth:
Automating Data Science (Dagstuhl Seminar 18401). Dagstuhl Reports 8(9): 154-181 (2018) - 2017
- [j54]Christopher Galbraith, Padhraic Smyth:
Analyzing user-event data using score-based likelihood ratios with marked point processes. Digit. Investig. 22 Supplement: S106-S114 (2017) - [j53]David M. Blei, Padhraic Smyth:
Science and data science. Proc. Natl. Acad. Sci. USA 114(33): 8689-8692 (2017) - [j52]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): 476-487 (2017) - [c117]Eric T. Nalisnick, Padhraic Smyth:
Stick-Breaking Variational Autoencoders. ICLR (Poster) 2017 - [c116]Eric T. Nalisnick, Padhraic Smyth:
Variational Reference Priors. ICLR (Workshop) 2017 - [c115]Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, Mark Warschauer:
Detecting changes in student behavior from clickstream data. LAK 2017: 21-30 - [c114]Eric T. Nalisnick, Padhraic Smyth:
Learning Approximately Objective Priors. UAI 2017 - 2016
- [j51]Petter Arnesen, Tracy Holsclaw, Padhraic Smyth:
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences. Technometrics 58(2): 205-213 (2016) - [c113]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 - [c112]Moshe Lichman, Dimitrios Kotzias, Padhraic Smyth:
Personalized location models with adaptive mixtures. SIGSPATIAL/GIS 2016: 67:1-67:4 - 2015
- [c111]Nicholas Martin Navaroli, Padhraic Smyth:
Modeling Response Time in Digital Human Communication. ICWSM 2015: 278-287 - [c110]Dimitrios Kotzias, Misha Denil, Nando de Freitas, Padhraic Smyth:
From Group to Individual Labels Using Deep Features. KDD 2015: 597-606 - [c109]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: 71-79 - [c108]Kevin Bache, Dennis DeCoste, Padhraic Smyth:
Hot Swapping for Online Adaptation of Optimization Hyperparameters. ICLR (Workshop) 2015 - 2014
- [j50]Andrew J. Frank, Padhraic Smyth, Alexander Ihler:
Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker. IEEE Trans. Signal Process. 62(9): 2413-2423 (2014) - [c107]Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth:
Approximate Slice Sampling for Bayesian Posterior Inference. AISTATS 2014: 185-193 - [c106]Moshe Lichman, Padhraic Smyth:
Modeling human location data with mixtures of kernel densities. KDD 2014: 35-44 - [c105]James R. Foulds, Padhraic Smyth:
Annealing Paths for the Evaluation of Topic Models. UAI 2014: 220-229 - 2013
- [j49]Nicholas Navaroli, Christopher DuBois, Padhraic Smyth:
Modeling individual email patterns over time with latent variable models. Mach. Learn. 92(2-3): 431-455 (2013) - [c104]Christopher DuBois, Carter T. Butts, Padhraic Smyth:
Stochastic blockmodeling of relational event dynamics. AISTATS 2013: 238-246 - [c103]James R. Foulds, Padhraic Smyth:
Modeling Scientific Impact with Topical Influence Regression. EMNLP 2013: 113-123 - [c102]Kevin Bache, David Newman, Padhraic Smyth:
Text-based measures of document diversity. KDD 2013: 23-31 - [c101]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. KDD 2013: 446-454 - [c100]Ralf Krestel, Padhraic Smyth:
Recommending patents based on latent topics. RecSys 2013: 395-398 - [c99]Michael J. Bannister, Christopher DuBois, David Eppstein, Padhraic Smyth:
Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges. SODA 2013: 856-864 - [e4]Ann E. Nicholson, Padhraic Smyth:
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013. AUAI Press 2013 [contents] - [i12]Dmitry Pavlov, Heikki Mannila, Padhraic Smyth:
Probabilistic Models for Query Approximation with Large Sparse Binary Datasets. CoRR abs/1301.3884 (2013) - [i11]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. CoRR abs/1305.2452 (2013) - [i10]Ann E. Nicholson, Padhraic Smyth:
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (2013). CoRR abs/1309.7971 (2013) - 2012
- [j48]Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers:
Statistical topic models for multi-label document classification. Mach. Learn. 88(1-2): 157-208 (2012) - [j47]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): 23:1-23:26 (2012) - [j46]Joydeep Ghosh, Padhraic Smyth, Andrew Tomkins, Rich Caruana:
Special issue on best of SIGKDD 2011. ACM Trans. Knowl. Discov. Data 6(4): 14:1-14:2 (2012) - [c98]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: 3943-3946 - [c97]Padhraic Smyth:
Analyzing Text and Social Network Data with Probabilistic Models. ECML/PKDD (1) 2012: 7-8 - [c96]Andrew J. Frank, Padhraic Smyth, Alexander Ihler:
A graphical model representation of the track-oriented multiple hypothesis tracker. SSP 2012: 768-771 - [c95]Nicholas Navaroli, Christopher DuBois, Padhraic Smyth:
Statistical Models for Exploring Individual Email Communication Behavior. ACML 2012: 317-332 - [i9]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. CoRR abs/1205.2662 (2012) - [i8]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. CoRR abs/1206.6845 (2012) - [i7]Sergey Kirshner, Padhraic Smyth, Andrew Robertson:
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. CoRR abs/1207.4142 (2012) - [i6]Seyoung Kim, Padhraic Smyth, Stefan Luther:
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models. CoRR abs/1207.4143 (2012) - [i5]Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth:
The Author-Topic Model for Authors and Documents. CoRR abs/1207.4169 (2012) - [i4]Michael J. Bannister, Christopher DuBois, David Eppstein, Padhraic Smyth:
Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges. CoRR abs/1209.5791 (2012) - 2011
- [j45]Mark Steyvers, Padhraic Smyth, Chaitanya Chemudugunta:
Combining Background Knowledge and Learned Topics. Top. Cogn. Sci. 3(1): 18-47 (2011) - [c94]Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Dynamic Egocentric Models for Citation Networks. ICML 2011: 857-864 - [c93]Christopher DuBois, James R. Foulds, Padhraic Smyth:
Latent Set Models for Two-Mode Network Data. ICWSM 2011 - [c92]Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Continuous-Time Regression Models for Longitudinal Networks. NIPS 2011: 2492-2500 - [c91]James R. Foulds, Padhraic Smyth:
Multi-Instance Mixture Models. SDM 2011: 606-617 - [c90]James R. Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander Ihler:
Revisiting MAP Estimation, Message Passing and Perfect Graphs. AISTATS 2011: 278-286 - [c89]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: 287-295 - [e3]Chid Apté, Joydeep Ghosh, Padhraic Smyth:
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24, 2011. ACM 2011, ISBN 978-1-4503-0813-7 [contents] - [i3]Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers:
Statistical Topic Models for Multi-Label Document Classification. CoRR abs/1107.2462 (2011) - 2010
- [j44]Qiang Liu, Kevin K. Lin, Bogi Andersen, Padhraic Smyth, Alexander Ihler:
Estimating replicate time shifts using Gaussian process regression. Bioinform. 26(6): 770-776 (2010) - [j43]Padhraic Smyth, Charles Elkan:
Technical perspective - Creativity helps influence prediction precision. Commun. ACM 53(4): 88 (2010) - [j42]Seyoung Kim, Padhraic Smyth, Hal S. Stern:
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data. IEEE Trans. Medical Imaging 29(6): 1260-1274 (2010) - [j41]Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L. Griffiths, Padhraic Smyth, Mark Steyvers:
Learning author-topic models from text corpora. ACM Trans. Inf. Syst. 28(1): 4:1-4:38 (2010) - [c88]Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. ICML 2010: 47-54 - [c87]Christopher DuBois, Padhraic Smyth:
Modeling relational events via latent classes. KDD 2010: 803-812 - [c86]America Chambers, Padhraic Smyth, Mark Steyvers:
Learning concept graphs from text with stick-breaking priors. NIPS 2010: 334-342 - [c85]Arthur U. Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth:
Learning with Blocks: Composite Likelihood and Contrastive Divergence. AISTATS 2010: 33-40
2000 – 2009
- 2009
- [j40]Darya Chudova, Alexander Ihler, Kevin K. Lin, Bogi Andersen, Padhraic Smyth:
Bayesian detection of non-sinusoidal periodic patterns in circadian expression data. Bioinform. 25(23): 3114-3120 (2009) - [j39]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Algorithms for Topic Models. J. Mach. Learn. Res. 10: 1801-1828 (2009) - [c84]Alexander Ihler, Andrew J. Frank, Padhraic Smyth:
Particle-based Variational Inference for Continuous Systems. NIPS 2009: 826-834 - [c83]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. UAI 2009: 27-34 - 2008
- [c82]Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Combining concept hierarchies and statistical topic models. CIKM 2008: 1469-1470 - [c81]Jon Hutchins, Alexander Ihler, Padhraic Smyth:
Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set. KDD Workshop on Knowledge Discovery from Sensor Data 2008: 94-114 - [c80]Ian Porteous, David Newman, Alexander Ihler, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Fast collapsed gibbs sampling for latent dirichlet allocation. KDD 2008: 569-577 - [c79]Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Asynchronous Distributed Learning of Topic Models. NIPS 2008: 81-88 - [c78]Chaitanya Chemudugunta, America Holloway, Padhraic Smyth, Mark Steyvers:
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. ISWC 2008: 229-244 - [i2]Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Text Modeling using Unsupervised Topic Models and Concept Hierarchies. CoRR abs/0808.0973 (2008) - 2007
- [j38]James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk:
KDD Cup and workshop 2007. SIGKDD Explor. 9(2): 51-52 (2007) - [j37]Alexander T. Ihler, Jon Hutchins, Padhraic Smyth:
Learning to detect events with Markov-modulated poisson processes. ACM Trans. Knowl. Discov. Data 1(3): 13 (2007) - [c77]Sergey Kirshner, Padhraic Smyth:
Infinite mixtures of trees. ICML 2007: 417-423 - [c76]David Newman, Kat Hagedorn, Chaitanya Chemudugunta, Padhraic Smyth:
Subject metadata enrichment using statistical topic models. JCDL 2007: 366-375 - [c75]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Inference for Latent Dirichlet Allocation. NIPS 2007: 1081-1088 - 2006
- [j36]Seyoung Kim, Padhraic Smyth:
Segmental Hidden Markov Models with Random Effects for Waveform Modeling. J. Mach. Learn. Res. 7: 945-969 (2006) - [j35]Jessica A. Turner, Padhraic Smyth, Fabio Macciardi, James H. Fallon, James L. Kennedy, Steven G. Potkin:
Imaging phenotypes and genotypes in schizophrenia. Neuroinformatics 4(1): 21-49 (2006) - [c74]Padhraic Smyth:
Data-Driven Discovery Using Probabilistic Hidden Variable Models. ALT 2006: 28 - [c73]Padhraic Smyth:
Data-Driven Discovery Using Probabilistic Hidden Variable Models. Discovery Science 2006: 13 - [c72]David Newman, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Analyzing Entities and Topics in News Articles Using Statistical Topic Models. ISI 2006: 93-104 - [c71]Alexander Ihler, Jon Hutchins, Padhraic Smyth:
Adaptive event detection with time-varying poisson processes. KDD 2006: 207-216 - [c70]David Newman, Chaitanya Chemudugunta, Padhraic Smyth:
Statistical entity-topic models. KDD 2006: 680-686 - [c69]Seyoung Kim, Padhraic Smyth, Hal S. Stern:
A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data. MICCAI (2) 2006: 217-224 - [c68]Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. NIPS 2006: 241-248 - [c67]Alexander T. Ihler, Padhraic Smyth:
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. NIPS 2006: 625-632 - [c66]Seyoung Kim, Padhraic Smyth:
Hierarchical Dirichlet Processes with Random Effects. NIPS 2006: 697-704 - [c65]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. UAI 2006 - 2005
- [j34]Joshua O'Madadhain, Jon Hutchins, Padhraic Smyth:
Prediction and ranking algorithms for event-based network data. SIGKDD Explor. 7(2): 23-30 (2005) - [c64]Joshua O'Madadhain, Padhraic Smyth:
EventRank: a framework for ranking time-varying networks. LinkKDD 2005: 9-16 - [c63]Seyoung Kim, Padhraic Smyth, Hal S. Stern, Jessica A. Turner:
Parametric Response Surface Models for Analysis of Multi-site fMRI Data. MICCAI 2005: 352-359 - [c62]Scott White, Padhraic Smyth:
A Spectral Clustering Approach To Finding Communities in Graph. SDM 2005: 274-285 - 2004
- [j33]Kevin K. Lin, Darya Chudova, G. Wesley Hatfield, Padhraic Smyth, Bogi Andersen:
Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance. Proc. Natl. Acad. Sci. USA 101(45): 15955-15960 (2004) - [c61]Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths:
Probabilistic author-topic models for information discovery. KDD 2004: 306-315 - [c60]Scott Gaffney, Padhraic Smyth:
Joint Probabilistic Curve Clustering and Alignment. NIPS 2004: 473-480 - [c59]Seyoung Kim, Padhraic Smyth, Stefan Luther:
Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models. UAI 2004: 309-316 - [c58]Sergey Kirshner, Padhraic Smyth, Andrew Robertson:
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. UAI 2004: 317-314 - [c57]Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth:
The Author-Topic Model for Authors and Documents. UAI 2004: 487-494 - 2003
- [b2]Pierre Baldi, Paolo Frasconi, Padhraic Smyth:
Modeling the Internet and the Web: Probabilistic Method and Algorithms. John Wiley 2003, ISBN 0-470-84906-1 - [j32]Darya Chudova, Padhraic Smyth:
Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. Data Min. Knowl. Discov. 7(3): 273-299 (2003) - [j31]Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White:
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. 7(4): 399-424 (2003) - [j30]Dmitry Pavlov, Heikki Mannila, Padhraic Smyth:
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. IEEE Trans. Knowl. Data Eng. 15(6): 1409-1421 (2003) - [c56]Scott Gaffney, Padhraic Smyth:
Curve Clustering with Random Effects Regression Mixtures. AISTATS 2003: 101-108 - [c55]Xianping Ge, Sridevi Parise, Padhraic Smyth:
Clustering Markov States into Equivalence Classes using SVD and Heuristic Search Algorithms. AISTATS 2003: 109-116 - [c54]Sergey Kirshner, Sridevi Parise, Padhraic Smyth:
Unsupervised Learning with Permuted Data. ICML 2003: 345-352 - [c53]Darya Chudova, Scott Gaffney, Eric Mjolsness, Padhraic Smyth:
Translation-invariant mixture models for curve clustering. KDD 2003: 79-88 - [c52]Scott White, Padhraic Smyth:
Algorithms for estimating relative importance in networks. KDD 2003: 266-275 - [c51]Darya Chudova, Christopher E. Hart, Eric Mjolsness, Padhraic Smyth:
Gene Expression Clustering with Functional Mixture Models. NIPS 2003: 683-690 - [c50]Dmitry Pavlov, Padhraic Smyth:
Approximate Query Answering by Model Averaging. SDM 2003: 142-153 - [c49]Darya Chudova, Scott Gaffney, Padhraic Smyth:
Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves. UAI 2003: 134-141 - 2002
- [j29]Padhraic Smyth, Daryl Pregibon, Christos Faloutsos:
Data-driven evolution of data mining algorithms. Commun. ACM 45(8): 33-37 (2002) - [j28]Chidanand Apté, Bing Liu, Edwin P. D. Pednault, Padhraic Smyth:
Business applications of data mining. Commun. ACM 45(8): 49-53 (2002) - [j27]Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren:
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Mach. Learn. 47(1): 7-34 (2002) - [c48]Padhraic Smyth:
Learning with Mixture Models: Concepts and Applications. ECML 2002: 529- - [c47]Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath, Erick Cantú-Paz:
Probabilistic Model-Based Detection of Bent-Double Radio Galaxies. ICPR (2) 2002: 499-502 - [c46]Darya Chudova, Padhraic Smyth:
Pattern discovery in sequences under a Markov assumption. KDD 2002: 153-162 - [c45]Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath:
Learning to Classify Galaxy Shapes Using the EM Algorithm. NIPS 2002: 1497-1504 - [c44]Padhraic Smyth:
Learning with Mixture Models: Concepts and Applications. PKDD 2002: 512 - 2001
- [b1]David J. Hand, Heikki Mannila, Padhraic Smyth:
Principles of Data Mining. MIT Press 2001, ISBN 9780262082907 - [j26]Xianping Ge, David Eppstein, Padhraic Smyth:
The distribution of loop lengths in graphical models for turbo decoding. IEEE Trans. Inf. Theory 47(6): 2549-2553 (2001) - [c43]Padhraic Smyth:
Breaking out of the Black-Box: Research Challenges in Data Mining. DMKD 2001 - [c42]Igor V. Cadez, Padhraic Smyth, Heikki Mannila:
Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. KDD 2001: 37-46 - [c41]Dmitry Pavlov, Padhraic Smyth:
Probabilistic query models for transaction data. KDD 2001: 164-173 - [c40]Igor V. Cadez, Padhraic Smyth:
Bayesian Predictive Profiles With Applications to Retail Transaction Data. NIPS 2001: 1353-1360 - 2000
- [j25]Padhraic Smyth:
Model selection for probabilistic clustering using cross-validated likelihood. Stat. Comput. 10(1): 63-72 (2000) - [j24]Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth:
The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explor. 2(2): 81-85 (2000) - [c39]Heikki Mannila, Padhraic Smyth:
Approximate Query Answering with Frequent Sets and Maximum Entropy. ICDE 2000: 309 - [c38]Xianping Ge, Padhraic Smyth:
Deformable Markov model templates for time-series pattern matching. KDD 2000: 81-90 - [c37]Igor V. Cadez, Scott Gaffney, Padhraic Smyth:
A general probabilistic framework for clustering individuals and objects. KDD 2000: 140-149 - [c36]Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White:
Visualization of navigation patterns on a Web site using model-based clustering. KDD 2000: 280-284 - [c35]Dmitry Pavlov, Darya Chudova, Padhraic Smyth:
Towards scalable support vector machines using squashing. KDD 2000: 295-299 - [c34]Igor V. Cadez, Padhraic Smyth:
Model Complexity, Goodness of Fit and Diminishing Returns. NIPS 2000: 388-394 - [c33]Dmitry Pavlov, Heikki Mannila, Padhraic Smyth:
Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. UAI 2000: 465-472
1990 – 1999
- 1999
- [j23]Padhraic Smyth, David H. Wolpert:
Linearly Combining Density Estimators via Stacking. Mach. Learn. 36(1-2): 59-83 (1999) - [j22]Padhraic Smyth:
Discussion on the paper by Friedman and Fisher. Stat. Comput. 9(2): 149-150 (1999) - [c32]Padhraic Smyth:
Joint probabilistic clustering of multivariate and sequential data. AISTATS 1999 - [c31]Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan:
Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86 - [c30]Scott Gaffney, Padhraic Smyth:
Trajectory Clustering with Mixtures of Regression Models. KDD 1999: 63-72 - [c29]Heikki Mannila, Dmitry Pavlov, Padhraic Smyth:
Prediction with Local Patterns using Cross-Entropy. KDD 1999: 357-361 - [c28]Xianping Ge, Wanda Pratt, Padhraic Smyth:
Discovering Chinese Words from Unsegmented Text (poster abstract). SIGIR 1999: 271-272 - [i1]Xianping Ge, David Eppstein, Padhraic Smyth:
The Distribution of Cycle Lengths in Graphical Models for Iterative Decoding. CoRR cs.DM/9907002 (1999) - 1998
- [j21]Michael C. Burl, Lars Asker, Padhraic Smyth, Usama M. Fayyad, Pietro Perona, Larry Crumpler, Jayne Aubele:
Learning to Recognize Volcanoes on Venus. Mach. Learn. 30(2-3): 165-194 (1998) - [c27]Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, Padhraic Smyth:
Rule Discovery from Time Series. KDD 1998: 16-22 - 1997
- [j20]Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth:
Statistical Themes and Lessons for Data Mining. Data Min. Knowl. Discov. 1(1): 11-28 (1997) - [j19]Pat Langley, Gregory M. Provan, Padhraic Smyth:
Learning with Probabilistic Representations. Mach. Learn. 29(2-3): 91-101 (1997) - [j18]Padhraic Smyth, David Heckerman, Michael I. Jordan:
Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Comput. 9(2): 227-269 (1997) - [j17]Padhraic Smyth:
Belief networks, hidden Markov models, and Markov random fields: A unifying view. Pattern Recognit. Lett. 18(11-13): 1261-1268 (1997) - [j16]Carla E. Brodley, Padhraic Smyth:
Applying classification algorithms in practice. Stat. Comput. 7(1): 45-56 (1997) - [c26]William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth:
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. AIME 1997: 73-85 - [c25]Padhraic Smyth:
Cross-validated Likelihood for Model Selection in Unsupervised Learning. AISTATS 1997: 473-480 - [c24]David Madigan, Padhraic Smyth:
Preface. AISTATS 1997: i-xiii - [c23]Subramani Mani, William Rodman Shankle, Michael J. Pazzani, Padhraic Smyth, Malcolm B. Dick:
Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach. AMIA 1997 - [c22]Eamonn J. Keogh, Padhraic Smyth:
A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997: 24-30 - [c21]Padhraic Smyth, David H. Wolpert:
Anytime Exploratory Data Analysis for Massive Data Sets. KDD 1997: 54-60 - [c20]Padhraic Smyth, Michael Ghil, Kayo Ide, Joseph Roden, Andrew Fraser:
Detecting Atmospheric Regimes Using Cross-Validated Clustering. KDD 1997: 61-66 - [c19]Padhraic Smyth, David H. Wolpert:
Stacked Density Estimation. NIPS 1997: 668-674 - [e2]David Madigan, Padhraic Smyth:
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, AISTATS 1997, Fort Lauderdale, Florida, USA, January, 4-7, 1997. MLR Press 1997 [contents] - 1996
- [j15]Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth:
From Data Mining to Knowledge Discovery in Databases. AI Mag. 17(3): 37-54 (1996) - [j14]Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth:
The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM 39(11): 27-34 (1996) - [j13]Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth:
Statistical Inference and Data Mining. Commun. ACM 39(11): 35-41 (1996) - [j12]Padhraic Smyth:
Bounds on the mean classification error rate of multiple experts. Pattern Recognit. Lett. 17(12): 1253-1257 (1996) - [c18]Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth:
Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD 1996: 82-88 - [c17]Padhraic Smyth:
Clustering Using Monte Carlo Cross-Validation. KDD 1996: 126-133 - [c16]Padhraic Smyth:
Clustering Sequences with Hidden Markov Models. NIPS 1996: 648-654 - [p3]Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth:
From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining 1996: 1-34 - [p2]Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona:
Modeling Subjective Uncertainty in Image Annotation. Advances in Knowledge Discovery and Data Mining 1996: 517-539 - [e1]Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy:
Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996, ISBN 0-262-56097-6 [contents] - 1995
- [j11]Usama M. Fayyad, Padhraic Smyth, Nicholas Weir, S. George Djorgovski:
Automated Analysis and Exploration of Image Databases: Results, Progress, and Challenges. J. Intell. Inf. Syst. 4(1): 7-25 (1995) - [c15]Padhraic Smyth, Alexander G. Gray, Usama M. Fayyad:
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation. ICML 1995: 506-514 - 1994
- [j10]Gregory Piatetsky-Shapiro, Christopher J. Matheus, Padhraic Smyth, Ramasamy Uthurusamy:
KDD-93: Progress and Challenges in Knowledge Discovery in Databases. AI Mag. 15(3): 77-82 (1994) - [j9]Padhraic Smyth:
Markov monitoring with unknown states. IEEE J. Sel. Areas Commun. 12(9): 1600-1612 (1994) - [j8]Padhraic Smyth:
Hidden Markov models for fault detection in dynamic system. Pattern Recognit. 27(1): 149-164 (1994) - [j7]Zheng Zeng, Rodney M. Goodman, Padhraic Smyth:
Discrete recurrent neural networks for grammatical inference. IEEE Trans. Neural Networks 5(2): 320-330 (1994) - [c14]Michael C. Burl, Usama M. Fayyad, Pietro Perona, Padhraic Smyth:
Automating the hunt for volcanoes on Venus. CVPR 1994: 302-309 - [c13]Usama M. Fayyad, Padhraic Smyth:
The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach. DL 1994: 225-249 - [c12]Michael C. Burl, Usama M. Fayyad, Pietro Perona, Padhraic Smyth:
Automated Analysis of Radar Imagery of Venus: Handling Lack of Ground Truth. ICIP (3) 1994: 236-240 - [c11]Padhraic Smyth, Michael C. Burl, Usama M. Fayyad, Pietro Perona:
Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth. KDD Workshop 1994: 109-120 - [c10]Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona, Pierre Baldi:
Inferring Ground Truth from Subjective Labelling of Venus Images. NIPS 1994: 1085-1092 - 1993
- [j6]Zheng Zeng, Rodney M. Goodman, Padhraic Smyth:
Learning Finite State Machines With Self-Clustering Recurrent Networks. Neural Comput. 5(6): 976-990 (1993) - [j5]John W. Miller, Rodney M. Goodman, Padhraic Smyth:
On loss functions which minimize to conditional expected values and posterior proba- bilities. IEEE Trans. Inf. Theory 39(4): 1404-1408 (1993) - [c9]Zheng Zeng, Rodney M. Goodman, Padhraic Smyth:
Self-clustering recurrent networks. ICNN 1993: 33-38 - [c8]Padhraic Smyth:
Probabilistic Anomaly Detection in Dynamic Systems. NIPS 1993: 825-832 - 1992
- [j4]Rodney M. Goodman, Charles M. Higgins, John W. Miller, Padhraic Smyth:
Rule-Based Neural Networks for Classification and Probability Estimation. Neural Comput. 4(6): 781-804 (1992) - [j3]Padhraic Smyth, Rodney M. Goodman:
An Information Theoretic Approach to Rule Induction from Databases. IEEE Trans. Knowl. Data Eng. 4(4): 301-316 (1992) - [c7]Padhraic Smyth, Jeff Mellstrom:
Detecting Novel Classes with Applications to Fault Diagnosis. ML 1992: 416-425 - 1991
- [c6]Padhraic Smyth, Jeff Mellstrom:
Fault Diagnosis of Antenna Pointing Systems Using Hybrid Neural Network and Signal Processing Models. NIPS 1991: 667-674 - [p1]Padhraic Smyth, Rodney M. Goodman:
Rule Induction Using Information Theory. Knowledge Discovery in Databases 1991: 159-176 - 1990
- [j2]Rodney M. Goodman, Padhraic Smyth:
Decision tree design using information theory. Knowl. Acquis. 2(1): 1-19 (1990) - [c5]Padhraic Smyth, Rodney M. Goodman, Charles M. Higgins:
A Hybrid Rule-Based/Bayesian Classifier. ECAI 1990: 610-615 - [c4]Padhraic Smyth:
On Stochastic Complexity and Admissible Models for Neural Network Classifiers. NIPS 1990: 818-824
1980 – 1989
- 1989
- [c3]Rodney M. Goodman, Padhraic Smyth:
The Induction of Probabilistic Rule Sets - The Itrule Algorithm. ML 1989: 129-132 - 1988
- [j1]Rodney M. Goodman, Padhraic Smyth:
Decision tree design from a communication theory standpoint. IEEE Trans. Inf. Theory 34(5): 979-994 (1988) - [c2]Rodney M. Goodman, Padhraic Smyth:
Information-Theoretic Rule Induction. ECAI 1988: 357-362 - [c1]Rodney M. Goodman, John W. Miller, Padhraic Smyth:
An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. NIPS 1988: 256-263
Coauthor Index
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