


Остановите войну!
for scientists:


default search action
Padhraic Smyth
Person information

- affiliation: University of California, Irvine, Department of Computer Science, CA, USA
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
- [c135]Markelle Kelly, Padhraic Smyth:
Variable-Based Calibration for Machine Learning Classifiers. AAAI 2023: 8211-8219 - [c134]Gavin Kerrigan, Justin Ley, Padhraic Smyth:
Diffusion Generative Models in Infinite Dimensions. AISTATS 2023: 9538-9563 - [c133]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Probabilistic Querying of Continuous-Time Event Sequences. AISTATS 2023: 10235-10251 - [c132]Markelle Kelly
, Aakriti Kumar
, Padhraic Smyth
, Mark Steyvers
:
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. FAccT 2023: 1723-1734 - [c131]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. ICML 2023: 19882-19910 - [c130]Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Inference for mark-censored temporal point processes. UAI 2023: 226-236 - [i29]Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. CoRR abs/2302.07832 (2023) - [i28]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection without Foundation Models. CoRR abs/2302.07849 (2023) - [i27]Markelle Kelly, Aakriti Kumar, Padhraic Smyth, Mark Steyvers:
Capturing Humans' Mental Models of AI: An Item Response Theory Approach. CoRR abs/2305.09064 (2023) - [i26]Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth:
Functional Flow Matching. CoRR abs/2305.17209 (2023) - 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) - [c129]Hyungrok Do, Preston Putzel, Axel S. Martin, Padhraic Smyth, Judy Zhong:
Fair Generalized Linear Models with a Convex Penalty. ICML 2022: 5286-5308 - [c128]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
- [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 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 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 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 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 Ihler, Padhraic Smyth:
Learning with Blocks: Composite Likelihood and Contrastive Divergence. AISTATS 2010: 33-40
2000 – 2009
- 2009
- [j39]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) - [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 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 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 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 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,