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ORCIDPadhraic Smyth
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- affiliation: University of California, Irvine, USA
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2020 – today
- 2022
[j58]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)- [c128]Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. ICML 2022: 5286-5308 - [i21]Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. CoRR abs/2206.09076 (2022) - 2021
- [c127]Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers:
Active Bayesian Assessment of Black-Box Classifiers. AAAI 2021: 7935-7944 - [c126]Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, Padhraic Smyth:
Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions. MLHC 2021: 648-673 - [c125]Gavin Kerrigan, Padhraic Smyth, Mark Steyvers:
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. NeurIPS 2021: 4421-4434 - [c124]Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt:
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning. NeurIPS 2021: 6816-6828 - [c123]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
- [j57]Christopher Galbraith, Padhraic Smyth, Hal S. Stern:
Statistical Methods for the Forensic Analysis of Geolocated Event Data. Digit. Investig. 33 Supplement: 301009 (2020) - [j56]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) - [c122]Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth:
User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS 2020 - [c121]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
- [j55]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) - [j54]Dimitrios Kotzias, Moshe Lichman, Padhraic Smyth:
Predicting Consumption Patterns with Repeated and Novel Events. IEEE Trans. Knowl. Data Eng. 31(2): 371-384 (2019) - [c120]Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth:
Dropout as a Structured Shrinkage Prior. ICML 2019: 4712-4722 - 2018
- [c119]Eric T. Nalisnick, Padhraic Smyth:
Learning Priors for Invariance. AISTATS 2018: 366-375 - [c118]Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel Baker, Padhraic Smyth, Mark Warschauer:
Understanding Student Procrastination via Mixture Models. EDM 2018 - [c117]Disi Ji, Eric T. Nalisnick, Yu Qian, Richard H. Scheuermann, Padhraic Smyth:
Bayesian Trees for Automated Cytometry Data Analysis. MLHC 2018: 465-483 - [c116]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
- [j53]Christopher Galbraith, Padhraic Smyth:
Analyzing user-event data using score-based likelihood ratios with marked point processes. Digit. Investig. 22 Supplement: S106-S114 (2017) - [j52]David M. Blei, Padhraic Smyth:
Science and data science. Proc. Natl. Acad. Sci. USA 114(33): 8689-8692 (2017) - [j51]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) - [c115]Eric T. Nalisnick, Padhraic Smyth:
Stick-Breaking Variational Autoencoders. ICLR (Poster) 2017 - [c114]Eric T. Nalisnick, Padhraic Smyth:
Variational Reference Priors. ICLR (Workshop) 2017 - [c113]Jihyun Park, Kameryn Denaro, Fernando Rodriguez, Padhraic Smyth, Mark Warschauer:
Detecting changes in student behavior from clickstream data. LAK 2017: 21-30 - [c112]Eric T. Nalisnick, Padhraic Smyth:
Learning Approximately Objective Priors. UAI 2017 - 2016
- [j50]Petter Arnesen, Tracy Holsclaw, Padhraic Smyth:
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences. Technometrics 58(2): 205-213 (2016) - [c111]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 - [c110]Moshe Lichman, Dimitrios Kotzias, Padhraic Smyth:
Personalized location models with adaptive mixtures. SIGSPATIAL/GIS 2016: 67:1-67:4 - 2015
- [c109]Nicholas Martin Navaroli, Padhraic Smyth:
Modeling Response Time in Digital Human Communication. ICWSM 2015: 278-287 - [c108]Dimitrios Kotzias, Misha Denil, Nando de Freitas, Padhraic Smyth:
From Group to Individual Labels Using Deep Features. KDD 2015: 597-606 - [c107]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 - [c106]Kevin Bache, Dennis DeCoste, Padhraic Smyth:
Hot Swapping for Online Adaptation of Optimization Hyperparameters. ICLR (Workshop) 2015 - 2014
- [j49]Andrew J. Frank, Padhraic Smyth, Alexander T. Ihler:
Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker. IEEE Trans. Signal Process. 62(9): 2413-2423 (2014) - [c105]Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth:
Approximate Slice Sampling for Bayesian Posterior Inference. AISTATS 2014: 185-193 - [c104]Moshe Lichman, Padhraic Smyth:
Modeling human location data with mixtures of kernel densities. KDD 2014: 35-44 - [c103]James R. Foulds, Padhraic Smyth:
Annealing Paths for the Evaluation of Topic Models. UAI 2014: 220-229 - 2013
- [j48]Nicholas Navaroli, Christopher DuBois, Padhraic Smyth:
Modeling individual email patterns over time with latent variable models. Mach. Learn. 92(2-3): 431-455 (2013) - [c102]Christopher DuBois, Carter T. Butts, Padhraic Smyth:
Stochastic blockmodeling of relational event dynamics. AISTATS 2013: 238-246 - [c101]James R. Foulds, Padhraic Smyth:
Modeling Scientific Impact with Topical Influence Regression. EMNLP 2013: 113-123 - [c100]Kevin Bache, David Newman, Padhraic Smyth:
Text-based measures of document diversity. KDD 2013: 23-31 - [c99]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. KDD 2013: 446-454 - [c98]Ralf Krestel, Padhraic Smyth:
Recommending patents based on latent topics. RecSys 2013: 395-398 - [c97]Michael J. Bannister, Christopher DuBois, David Eppstein, Padhraic Smyth:
Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges. SODA 2013: 856-864 - [e3]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
- [j47]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) - [j46]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) - [j45]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) - [c96]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 - [c95]Padhraic Smyth:
Analyzing Text and Social Network Data with Probabilistic Models. ECML/PKDD (1) 2012: 7-8 - [c94]Andrew J. Frank, Padhraic Smyth, Alexander T. Ihler:
A graphical model representation of the track-oriented multiple hypothesis tracker. SSP 2012: 768-771 - [c93]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
- [j44]Mark Steyvers, Padhraic Smyth, Chaitanya Chemudugunta:
Combining Background Knowledge and Learned Topics. Top. Cogn. Sci. 3(1): 18-47 (2011) - [c92]Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Dynamic Egocentric Models for Citation Networks. ICML 2011: 857-864 - [c91]Christopher DuBois, James R. Foulds, Padhraic Smyth:
Latent Set Models for Two-Mode Network Data. ICWSM 2011 - [c90]Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Continuous-Time Regression Models for Longitudinal Networks. NIPS 2011: 2492-2500 - [c89]James R. Foulds, Padhraic Smyth:
Multi-Instance Mixture Models. SDM 2011: 606-617 - [c88]James R. Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander T. Ihler:
Revisiting MAP Estimation, Message Passing and Perfect Graphs. AISTATS 2011: 278-286 - [c87]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 - [e2]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
- [j43]Qiang Liu, Kevin K. Lin, Bogi Andersen, Padhraic Smyth, Alexander T. Ihler:
Estimating replicate time shifts using Gaussian process regression. Bioinform. 26(6): 770-776 (2010) - [j42]Padhraic Smyth, Charles Elkan:
Technical perspective - Creativity helps influence prediction precision. Commun. ACM 53(4): 88 (2010) - [j41]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) - [j40]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) - [c86]Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. ICML 2010: 47-54 - [c85]Christopher DuBois, Padhraic Smyth:
Modeling relational events via latent classes. KDD 2010: 803-812 - [c84]America Chambers, Padhraic Smyth, Mark Steyvers:
Learning concept graphs from text with stick-breaking priors. NIPS 2010: 334-342 - [c83]Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
Learning with Blocks: Composite Likelihood and Contrastive Divergence. AISTATS 2010: 33-40
2000 – 2009
- 2009
- [j39]Darya Chudova, Alexander T. 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) - [j38]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Algorithms for Topic Models. J. Mach. Learn. Res. 10: 1801-1828 (2009) - [c82]Alexander T. Ihler, Andrew J. Frank, Padhraic Smyth:
Particle-based Variational Inference for Continuous Systems. NIPS 2009: 826-834 - [c81]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. UAI 2009: 27-34 - 2008
- [c80]Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Combining concept hierarchies and statistical topic models. CIKM 2008: 1469-1470 - [c79]Jon Hutchins, Alexander T. 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 - [c78]Ian Porteous, David Newman, Alexander T. Ihler, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Fast collapsed gibbs sampling for latent dirichlet allocation. KDD 2008: 569-577 - [c77]Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Asynchronous Distributed Learning of Topic Models. NIPS 2008: 81-88 - [c76]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
- [j37]James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, Domonkos Tikk:
KDD Cup and workshop 2007. SIGKDD Explor. 9(2): 51-52 (2007) - [j36]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) - [c75]Sergey Kirshner, Padhraic Smyth:
Infinite mixtures of trees. ICML 2007: 417-423 - [c74]David Newman, Kat Hagedorn, Chaitanya Chemudugunta, Padhraic Smyth:
Subject metadata enrichment using statistical topic models. JCDL 2007: 366-375 - [c73]David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Inference for Latent Dirichlet Allocation. NIPS 2007: 1081-1088 - 2006
- [j35]Seyoung Kim, Padhraic Smyth:
Segmental Hidden Markov Models with Random Effects for Waveform Modeling. J. Mach. Learn. Res. 7: 945-969 (2006) - [j34]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) - [c72]Padhraic Smyth:
Data-Driven Discovery Using Probabilistic Hidden Variable Models. ALT 2006: 28 - [c71]Padhraic Smyth:
Data-Driven Discovery Using Probabilistic Hidden Variable Models. Discovery Science 2006: 13 - [c70]David Newman, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Analyzing Entities and Topics in News Articles Using Statistical Topic Models. ISI 2006: 93-104 - [c69]Alexander T. Ihler, Jon Hutchins, Padhraic Smyth:
Adaptive event detection with time-varying poisson processes. KDD 2006: 207-216 - [c68]David Newman, Chaitanya Chemudugunta, Padhraic Smyth:
Statistical entity-topic models. KDD 2006: 680-686 - [c67]Seyoung Kim, Padhraic Smyth, Hal S. Stern:
A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data. MICCAI (2) 2006: 217-224 - [c66]Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. NIPS 2006: 241-248 - [c65]Alexander T. Ihler, Padhraic Smyth:
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. NIPS 2006: 625-632 - [c64]Seyoung Kim, Padhraic Smyth:
Hierarchical Dirichlet Processes with Random Effects. NIPS 2006: 697-704 - [c63]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. UAI 2006 - 2005
- [j33]Joshua O'Madadhain, Jon Hutchins, Padhraic Smyth:
Prediction and ranking algorithms for event-based network data. SIGKDD Explor. 7(2): 23-30 (2005) - [c62]Joshua O'Madadhain, Padhraic Smyth:
EventRank: a framework for ranking time-varying networks. LinkKDD 2005: 9-16 - [c61]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 - [c60]Scott White, Padhraic Smyth:
A Spectral Clustering Approach To Finding Communities in Graph. SDM 2005: 274-285 - 2004
- [j32]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) - [c59]Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths:
Probabilistic author-topic models for information discovery. KDD 2004: 306-315 - [c58]Scott Gaffney, Padhraic Smyth:
Joint Probabilistic Curve Clustering and Alignment. NIPS 2004: 473-480 - [c57]Seyoung Kim, Padhraic Smyth, Stefan Luther:
Modeling Waveform Shapes with Random E ects Segmental Hidden Markov Models. UAI 2004: 309-316 - [c56]Sergey Kirshner, Padhraic Smyth, Andrew Robertson:
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series. UAI 2004: 317-314 - [c55]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 - [j31]Darya Chudova, Padhraic Smyth:
Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework. Data Min. Knowl. Discov. 7(3): 273-299 (2003) - [j30]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) - [j29]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) - [c54]