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ORCIDZoubin Ghahramani
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- affiliation: Google Brain
- affiliation (former): University College London, UK
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2020 – today
- 2024
[j62]Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. J. Mach. Learn. Res. 25: 50:1-50:51 (2024)- [j61]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. 25: 212:1-212:83 (2024) - [i68]Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas:
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR abs/2404.07839 (2024) - [i67]Irina Jurenka, Markus Kunesch, Kevin R. McKee, Daniel Gillick, Shaojian Zhu, Sara Wiltberger, Shubham Milind Phal, Katherine L. Hermann, Daniel Kasenberg, Avishkar Bhoopchand, Ankit Anand, Miruna Pîslar, Stephanie Chan, Lisa Wang, Jennifer She, Parsa Mahmoudieh, Aliya Rysbek, Wei-Jen Ko, Andrea Huber, Brett Wiltshire, Gal Elidan, Roni Rabin, Jasmin Rubinovitz, Amit Pitaru, Mac McAllister, Julia Wilkowski, David Choi, Roee Engelberg, Lidan Hackmon, Adva Levin, Rachel Griffin, Michael Sears, Filip Bar, Mia Mesar, Mana Jabbour, Arslan Chaudhry, James Cohan, Sridhar Thiagarajan, Nir Levine, Ben Brown, Dilan Görür, Svetlana Grant, Rachel Hashimshoni, Laura Weidinger, Jieru Hu, Dawn Chen, Kuba Dolecki, Canfer Akbulut, Maxwell L. Bileschi, Laura Culp, Wen-Xin Dong, Nahema Marchal, Kelsie Van Deman, Hema Bajaj Misra, Michael Duah, Moran Ambar, Avi Caciularu, Sandra Lefdal, Christopher Summerfield, James An, Pierre-Alexandre Kamienny, Abhinit Mohdi, Theofilos Strinopoulous, Annie Hale, Wayne Anderson, Luis C. Cobo, Niv Efron, Muktha Ananda, Shakir Mohamed, Maureen Heymans, Zoubin Ghahramani, Yossi Matias, Ben Gomes, Lila Ibrahim:
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach. CoRR abs/2407.12687 (2024) - 2023
- [c185]Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. ICML 2023: 8990-9012 - 2022
- [i66]Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson:
Neural Diffusion Processes. CoRR abs/2206.03992 (2022) - [i65]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-training helps Bayesian optimization too. CoRR abs/2207.03084 (2022) - [i64]Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - 2021
- [c184]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS 2021: 9443-9455 - [i63]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR abs/2105.04504 (2021) - [i62]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR abs/2109.08215 (2021) - 2020
- [j60]Isabel Valera
, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani:
General Latent Feature Models for Heterogeneous Datasets. J. Mach. Learn. Res. 21: 100:1-100:49 (2020) - [j59]Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera:
Handling incomplete heterogeneous data using VAEs. Pattern Recognit. 107: 107501 (2020) - [c183]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML 2020: 7563-7574 - [i61]Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani:
Resource-Efficient Neural Networks for Embedded Systems. CoRR abs/2001.03048 (2020) - [i60]Mohamed Tarek, Kai Xu, Martin Trapp, Hong Ge, Zoubin Ghahramani:
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models. CoRR abs/2002.02702 (2020) - [i59]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR abs/2004.06231 (2020) - [i58]Will Y. Zou, Smitha Shyam, Michael Mui, Mingshi Wang, Jan O. Pedersen, Zoubin Ghahramani:
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects. CoRR abs/2004.09703 (2020)
2010 – 2019
- 2019
- [j58]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29(5): 1127-1147 (2019) - [c182]Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller:
One-Network Adversarial Fairness. AAAI 2019: 2412-2420 - [c181]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. AAAI 2019: 5207-5215 - [c180]Kai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp, Zoubin Ghahramani:
AdvancedHMC.jl: A robust, modular and e cient implementation of advanced HMC algorithms. AABI 2019: 1-10 - [c179]Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. NeurIPS 2019: 6344-6355 - [c178]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting, Zoubin Ghahramani:
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. UAI 2019: 334-344 - [p5]Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, Zoubin Ghahramani:
The Automatic Statistician. Automated Machine Learning 2019: 161-173 - [i57]Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. CoRR abs/1905.10884 (2019) - 2018
- [j57]Christopher A. Penfold, Anastasiya Sybirna, John E. Reid, Yun Huang, Lorenz Wernisch, Zoubin Ghahramani, Murray Grant, M. Azim Surani:
Branch-recombinant Gaussian processes for analysis of perturbations in biological time series. Bioinform. 34(17): i1005-i1013 (2018) - [j56]Adam Scibior, Ohad Kammar, Zoubin Ghahramani:
Functional programming for modular Bayesian inference. Proc. ACM Program. Lang. 2(ICFP): 83:1-83:29 (2018) - [j55]Adam Scibior, Ohad Kammar, Matthijs Vákár, Sam Staton, Hongseok Yang, Yufei Cai, Klaus Ostermann, Sean K. Moss, Chris Heunen, Zoubin Ghahramani:
Denotational validation of higher-order Bayesian inference. Proc. ACM Program. Lang. 2(POPL): 60:1-60:29 (2018) - [c177]Yusuke Mukuta, Akisato Kimura, David B. Adrian, Zoubin Ghahramani:
Weakly Supervised Collective Feature Learning From Curated Media. AAAI 2018: 7260-7267 - [c176]Hong Ge, Kai Xu, Zoubin Ghahramani:
Turing: Composable inference for probabilistic programming. AISTATS 2018: 1682-1690 - [c175]Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda:
Few-shot learning of neural networks from scratch by pseudo example optimization. BMVC 2018: 105 - [c174]Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. ICLR (Poster) 2018 - [c173]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) 2018 - [c172]Tameem Adel, Zoubin Ghahramani, Adrian Weller:
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. ICML 2018: 50-59 - [c171]Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani:
Variational Bayesian dropout: pitfalls and fixes. ICML 2018: 2024-2033 - [c170]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML 2018: 5022-5031 - [c169]Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song:
MetaGAN: An Adversarial Approach to Few-Shot Learning. NeurIPS 2018: 2371-2380 - [i56]Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda:
Imitation networks: Few-shot learning of neural networks from scratch. CoRR abs/1802.03039 (2018) - [i55]Yusuke Mukuta, Akisato Kimura, David B. Adrian, Zoubin Ghahramani:
Weakly supervised collective feature learning from curated media. CoRR abs/1802.04668 (2018) - [i54]George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR abs/1802.10031 (2018) - [i53]Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani:
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR abs/1804.11271 (2018) - [i52]Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Variational Measure Preserving Flows. CoRR abs/1805.10377 (2018) - [i51]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani:
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR abs/1806.01910 (2018) - [i50]Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani:
Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR abs/1807.00400 (2018) - [i49]Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani:
Variational Bayesian dropout: pitfalls and fixes. CoRR abs/1807.01969 (2018) - [i48]Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera:
Handling Incomplete Heterogeneous Data using VAEs. CoRR abs/1807.03653 (2018) - [i47]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. CoRR abs/1807.09306 (2018) - [i46]Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani:
Probabilistic Meta-Representations Of Neural Networks. CoRR abs/1810.00555 (2018) - [i45]Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani:
Efficient and Robust Machine Learning for Real-World Systems. CoRR abs/1812.02240 (2018) - 2017
- [j54]Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman:
GPflow: A Gaussian Process Library using TensorFlow. J. Mach. Learn. Res. 18: 40:1-40:6 (2017) - [c168]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR 2017 - [c167]Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller:
Lost Relatives of the Gumbel Trick. ICML 2017: 371-379 - [c166]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. ICML 2017: 1183-1192 - [c165]Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi:
Bayesian inference on random simple graphs with power law degree distributions. ICML 2017: 2004-2013 - [c164]Konstantina Palla, David A. Knowles, Zoubin Ghahramani:
A Birth-Death Process for Feature Allocation. ICML 2017: 2751-2759 - [c163]Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner:
Magnetic Hamiltonian Monte Carlo. ICML 2017: 3453-3461 - [c162]Isabel Valera, Zoubin Ghahramani:
Automatic Discovery of the Statistical Types of Variables in a Dataset. ICML 2017: 3521-3529 - [c161]Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS 2017: 3846-3855 - [i44]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. CoRR abs/1703.02910 (2017) - [i43]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR abs/1706.00387 (2017) - [i42]Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller:
Lost Relatives of the Gumbel Trick. CoRR abs/1706.04161 (2017) - [i41]Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani:
One-Shot Learning in Discriminative Neural Networks. CoRR abs/1707.05562 (2017) - [i40]Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani:
General Latent Feature Modeling for Data Exploration Tasks. CoRR abs/1707.08352 (2017) - [i39]Adam Scibior, Ohad Kammar, Matthijs Vákár, Sam Staton, Hongseok Yang, Yufei Cai, Klaus Ostermann, Sean K. Moss, Chris Heunen, Zoubin Ghahramani:
Denotational validation of higher-order Bayesian inference. CoRR abs/1711.03219 (2017) - 2016
- [j53]José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani:
A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. 17: 160:1-160:53 (2016) - [j52]Tomoharu Iwata, James Robert Lloyd, Zoubin Ghahramani:
Unsupervised Many-to-Many Object Matching for Relational Data. IEEE Trans. Pattern Anal. Mach. Intell. 38(3): 607-617 (2016) - [j51]Alfredo Nazábal, Pablo Garcia-Moreno, Antonio Artés-Rodríguez, Zoubin Ghahramani:
Human Activity Recognition by Combining a Small Number of Classifiers. IEEE J. Biomed. Health Informatics 20(5): 1342-1351 (2016) - [c160]Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani:
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes. AISTATS 2016: 231-239 - [c159]Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg:
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS 2016: 408-416 - [c158]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML 2016: 1050-1059 - [c157]Amar Shah, Zoubin Ghahramani:
Pareto Frontier Learning with Expensive Correlated Objectives. ICML 2016: 1919-1927 - [c156]Yutian Chen, Zoubin Ghahramani:
Scalable Discrete Sampling as a Multi-Armed Bandit Problem. ICML 2016: 2492-2501 - [c155]Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani:
Distributed Flexible Nonlinear Tensor Factorization. NIPS 2016: 920-928 - [c154]Yarin Gal, Zoubin Ghahramani:
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS 2016: 1019-1027 - [c153]Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh:
The Mondrian Kernel. UAI 2016 - [c152]Amar Shah, Zoubin Ghahramani:
Markov Beta Processes for Time Evolving Dictionary Learning. UAI 2016 - [i38]Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy:
A study of the effect of JPG compression on adversarial images. CoRR abs/1608.00853 (2016) - [i37]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR abs/1611.02247 (2016) - 2015
- [j50]John P. Cunningham, Zoubin Ghahramani:
Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16: 2859-2900 (2015) - [j49]Zoubin Ghahramani:
Probabilistic machine learning and artificial intelligence. Nat. 521(7553): 452-459 (2015) - [j48]David A. Knowles, Zoubin Ghahramani:
Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 271-289 (2015) - [j47]Konstantina Palla, David A. Knowles, Zoubin Ghahramani:
Relational Learning and Network Modelling Using Infinite Latent Attribute Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 462-474 (2015) - [j46]Novi Quadrianto, Zoubin Ghahramani:
A Very Simple Safe-Bayesian Random Forest. IEEE Trans. Pattern Anal. Mach. Intell. 37(6): 1297-1303 (2015) - [j45]Sébastien Bratières, Novi Quadrianto, Zoubin Ghahramani:
GPstruct: Bayesian Structured Prediction Using Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1514-1520 (2015) - [j44]Konstantinos Bousmalis, Stefanos Zafeiriou, Louis-Philippe Morency, Maja Pantic, Zoubin Ghahramani:
Variational Infinite Hidden Conditional Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 37(9): 1917-1929 (2015) - [c151]James Hensman, Alexander G. de G. Matthews, Zoubin Ghahramani:
Scalable Variational Gaussian Process Classification. AISTATS 2015 - [c150]Christian Steinruecken, Zoubin Ghahramani, David J. C. MacKay:
Improving PPM with Dynamic Parameter Updates. DCC 2015: 193-202 - [c149]Adam Scibior, Zoubin Ghahramani, Andrew D. Gordon:
Practical probabilistic programming with monads. Haskell 2015: 165-176 - [c148]Yarin Gal, Yutian Chen, Zoubin Ghahramani:
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. ICML 2015: 645-654 - [c147]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani:
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML 2015: 1073-1082 - [c146]Amar Shah, David A. Knowles, Zoubin Ghahramani:
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process. ICML 2015: 1594-1603 - [c145]José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani:
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML 2015: 1699-1707 - [c144]Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani:
Distributed Inference for Dirichlet Process Mixture Models. ICML 2015: 2276-2284 - [c143]James Robert Lloyd, Zoubin Ghahramani:
Statistical Model Criticism using Kernel Two Sample Tests. NIPS 2015: 829-837 - [c142]James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani:
MCMC for Variationally Sparse Gaussian Processes. NIPS 2015: 1648-1656 - [c141]Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani:
Particle Gibbs for Infinite Hidden Markov Models. NIPS 2015: 2395-2403 - [c140]Shixiang Gu, Zoubin Ghahramani, Richard E. Turner:
Neural Adaptive Sequential Monte Carlo. NIPS 2015: 2629-2637 - [c139]Amar Shah, Zoubin Ghahramani:
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions. NIPS 2015: 3330-3338 - [c138]Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani:
Training generative neural networks via Maximum Mean Discrepancy optimization. UAI 2015: 258-267 - [i36]Razvan Ranca, Zoubin Ghahramani:
Slice Sampling for Probabilistic Programming. CoRR abs/1501.04684 (2015) - [i35]Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani:
Training generative neural networks via Maximum Mean Discrepancy optimization. CoRR abs/1505.03906 (2015) - [i34]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. CoRR abs/1506.02142 (2015) - [i33]Yarin Gal, Zoubin Ghahramani:
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. CoRR abs/1506.02158 (2015) - [i32]Shixiang Gu, Zoubin Ghahramani, Richard E. Turner:
Neural Adaptive Sequential Monte Carlo. CoRR abs/1506.03338 (2015) - [i31]Amar Shah, David A. Knowles, Zoubin Ghahramani:
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process. CoRR abs/1506.08180 (2015) - [i30]Yutian Chen, Zoubin Ghahramani:
Subsampling-Based Approximate Monte Carlo for Discrete Distributions. CoRR abs/1506.09039 (2015) - [i29]Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams:
Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR abs/1511.02543 (2015) - [i28]Amar Shah, Zoubin Ghahramani:
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions. CoRR abs/1511.07130 (2015) - 2014
- [c137]James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani:
Automatic Construction and Natural-Language Description of Nonparametric Regression Models. AAAI 2014: 1242-1250 - [c136]Ava Bargi, Richard Yi Da Xu, Zoubin Ghahramani, Massimo Piccardi:
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response. AISTATS 2014: 77-85 - [c135]David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani:
Avoiding pathologies in very deep networks. AISTATS 2014: 202-210 - [c134]Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani:
Student-t Processes as Alternatives to Gaussian Processes. AISTATS 2014: 877-885 - [c133]Yarin Gal, Zoubin Ghahramani:
Pitfalls in the use of Parallel Inference for the Dirichlet Process. ICML 2014: 208-216 - [c132]Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani:
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications. ICML 2014: 334-342 - [c131]José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. ICML 2014: 379-387 - [c130]Neil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Cold-start Active Learning with Robust Ordinal Matrix Factorization. ICML 2014: 766-774 - [c129]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. ICML 2014: 1359-1367 - [c128]José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Probabilistic Matrix Factorization with Non-random Missing Data. ICML 2014: 1512-1520 - [c127]Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani:
Beta Diffusion Trees. ICML 2014: 1809-1817 - [c126]David A. Knowles, Zoubin Ghahramani, Konstantina Palla:
A reversible infinite HMM using normalised random measures. ICML 2014: 1998-2006 - [c125]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS 2014: 918-926 - [c124]Isabel Valera, Zoubin Ghahramani:
General Table Completion using a Bayesian Nonparametric Model. NIPS 2014: 981-989 - [c123]Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Volatility Model. NIPS 2014: 1044-1052 - [e5]Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 [contents] - [r1]Zoubin Ghahramani, Shakir Mohamed, Katherine A. Heller:
Partial Membership and Factor Analysis. Handbook of Mixed Membership Models and Their Applications 2014: 67-88 - [i27]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. CoRR abs/1402.0119 (2014) - [i26]Alex Davies, Zoubin Ghahramani:
The Random Forest Kernel and other kernels for big data from random partitions. CoRR abs/1402.4293 (2014) - [i25]James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani:
Automatic Construction and Natural-Language Description of Nonparametric Regression Models. CoRR abs/1402.4304 (2014) - [i24]Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani:
Student-t Processes as Alternatives to Gaussian Processes. CoRR abs/1402.4306 (2014) - [i23]David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani:
Avoiding pathologies in very deep networks. CoRR abs/1402.5836 (2014) - [i22]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. CoRR abs/1406.2541 (2014) - [i21]Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani:
Warped Mixtures for Nonparametric Cluster Shapes. CoRR abs/1408.2061 (2014) - 2013
- [j43]Frederik Eaton, Zoubin Ghahramani:
Model Reductions for Inference: Generality of Pairwise, Binary, and Planar Factor Graphs. Neural Comput. 25(5): 1213-1260 (2013) - [c122]Jacob Andreas, Zoubin Ghahramani:
A Generative Model of Vector Space Semantics. CVSM@ACL 2013: 91-99 - [c121]Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani:
Active Learning for Interactive Visualization. AISTATS 2013: 342-350 - [c120]David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) 2013: 10-18 - [c119]Creighton Heaukulani, Zoubin Ghahramani:
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks. ICML (1) 2013: 275-283 - [c118]Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Dynamic Covariance Models for Multivariate Financial Time Series. ICML (3) 2013: 558-566 - [c117]Colorado Reed, Zoubin Ghahramani:
Scaling the Indian Buffet Process via Submodular Maximization. ICML (3) 2013: 1013-1021 - [c116]David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani:
Structure Discovery in Nonparametric Regression through Compositional Kernel Search. ICML (3) 2013: 1166-1174 - [c115]Tomoharu Iwata, Amar Shah, Zoubin Ghahramani:
Discovering latent influence in online social activities via shared cascade poisson processes. KDD 2013: 266-274 - [c114]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SIGMa: simple greedy matching for aligning large knowledge bases. KDD 2013: 572-580 - [c113]Konstantinos Bousmalis, Stefanos Zafeiriou, Louis-Philippe Morency, Maja Pantic, Zoubin Ghahramani:
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures. ECML/PKDD (2) 2013: 531-547 - [c112]Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani:
Warped Mixtures for Nonparametric Cluster Shapes. UAI 2013 - [c111]Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani:
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. UAI 2013 - [c110]Amar Shah, Zoubin Ghahramani:
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering. UAI 2013 - [e4]Christopher J. C. Burges, Léon Bottou, Zoubin Ghahramani, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. 2013 [contents] - [i20]David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani:
Structure Discovery in Nonparametric Regression through Compositional Kernel Search. CoRR abs/1302.4922 (2013) - [i19]Colorado Reed, Zoubin Ghahramani:
Scaling the Indian Buffet Process via Submodular Maximization. CoRR abs/1304.3285 (2013) - [i18]Sébastien Bratières, Novi Quadrianto, Zoubin Ghahramani:
Bayesian Structured Prediction Using Gaussian Processes. CoRR abs/1307.3846 (2013) - [i17]Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani:
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. CoRR abs/1309.6858 (2013) - [i16]Amar Shah, Zoubin Ghahramani:
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering. CoRR abs/1309.6862 (2013) - 2012
- [j42]Paul D. W. Kirk, Jim E. Griffin, Richard S. Savage, Zoubin Ghahramani, David L. Wild:
Bayesian correlated clustering to integrate multiple datasets. Bioinform. 28(24): 3290-3297 (2012) - [c109]Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani:
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning . ICML 2012 - [c108]Konstantina Palla, David A. Knowles, Zoubin Ghahramani:
An Infinite Latent Attribute Model for Network Data. ICML 2012 - [c107]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. ICML 2012 - [c106]Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani:
Gaussian Process Regression Networks. ICML 2012 - [c105]Michael A. Osborne, David Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, Zoubin Ghahramani:
Active Learning of Model Evidence Using Bayesian Quadrature. NIPS 2012: 46-54 - [c104]James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy:
Random function priors for exchangeable arrays with applications to graphs and relational data. NIPS 2012: 1007-1015 - [c103]Neil Houlsby, José Miguel Hernández-Lobato, Ferenc Huszar, Zoubin Ghahramani:
Collaborative Gaussian Processes for Preference Learning. NIPS 2012: 2105-2113 - [c102]David A. Knowles, Konstantina Palla, Zoubin Ghahramani:
A nonparametric variable clustering model. NIPS 2012: 2996-3004 - [c101]Yichuan Zhang, Charles Sutton, Amos J. Storkey, Zoubin Ghahramani:
Continuous Relaxations for Discrete Hamiltonian Monte Carlo. NIPS 2012: 3203-3211 - [c100]Andrew Gordon Wilson, Zoubin Ghahramani:
Modelling Input Varying Correlations between Multiple Responses. ECML/PKDD (2) 2012: 858-861 - [c99]John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen:
Gaussian Processes for time-marked time-series data. AISTATS 2012: 255-263 - [c98]Hyun-Chul Kim, Zoubin Ghahramani:
Bayesian Classifier Combination. AISTATS 2012: 619-627 - [c97]Donglin Niu, Jennifer G. Dy, Zoubin Ghahramani:
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views. AISTATS 2012: 814-822 - [c96]Jacob Steinhardt, Zoubin Ghahramani:
Flexible Martingale Priors for Deep Hierarchies. AISTATS 2012: 1108-1116 - [i15]Finale Doshi-Velez, Zoubin Ghahramani:
Correlated Non-Parametric Latent Feature Models. CoRR abs/1205.2650 (2012) - [i14]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. CoRR abs/1206.4682 (2012) - [i13]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR abs/1206.6865 (2012) - [i12]Edward Lloyd Snelson, Zoubin Ghahramani:
Variable noise and dimensionality reduction for sparse Gaussian processes. CoRR abs/1206.6873 (2012) - [i11]Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani:
Bayesian Inference for Gaussian Mixed Graph Models. CoRR abs/1206.6874 (2012) - [i10]Iain Murray, Zoubin Ghahramani:
Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms. CoRR abs/1207.4134 (2012) - [i9]Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani:
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases. CoRR abs/1207.4525 (2012) - [i8]Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani:
On the Convergence of Bound Optimization Algorithms. CoRR abs/1212.2490 (2012) - 2011
- [j41]Thomas L. Griffiths, Zoubin Ghahramani:
The Indian Buffet Process: An Introduction and Review. J. Mach. Learn. Res. 12: 1185-1224 (2011) - [j40]Ramin Zabih, Jiri Matas, Zoubin Ghahramani:
State of the Journal. IEEE Trans. Pattern Anal. Mach. Intell. 33(1): 1-2 (2011) - [j39]Ramin Zabih, Zoubin Ghahramani, Sing Bing Kang, Jiri Matas:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 33(5): 865-866 (2011) - [j38]Ramin Zabih, Zoubin Ghahramani, Sing Bing Kang, Jiri Matas:
Editorial. IEEE Trans. Pattern Anal. Mach. Intell. 33(9): 1697-1698 (2011) - [c95]Finale Doshi-Velez, Zoubin Ghahramani:
A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains. CogSci 2011 - [c94]David A. Knowles, Jurgen Van Gael, Zoubin Ghahramani:
Message Passing Algorithms for the Dirichlet Diffusion Tree. ICML 2011: 721-728 - [c93]Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes:
The Dynamic Beamformer. MLINI 2011: 148-155 - [c92]Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths:
Testing a Bayesian Measure of Representativeness Using a Large Image Database. NIPS 2011: 2321-2329 - [c91]David A. Knowles, Zoubin Ghahramani:
Pitman-Yor Diffusion Trees. UAI 2011: 410-418 - [c90]Andrew Gordon Wilson, Zoubin Ghahramani:
Generalised Wishart Processes. UAI 2011: 736-744 - [c89]Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani:
Approximate inference for the loss-calibrated Bayesian. AISTATS 2011: 416-424 - [i7]Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani:
Bayesian and L1 Approaches to Sparse Unsupervised Learning. CoRR abs/1106.1157 (2011) - [i6]Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Máté Lengyel:
Bayesian Active Learning for Classification and Preference Learning. CoRR abs/1112.5745 (2011) - 2010
- [j37]Christoph Lippert, Zoubin Ghahramani, Karsten M. Borgwardt:
Gene function prediction from synthetic lethality networks via ranking on demand. Bioinform. 26(7): 912-918 (2010) - [j36]Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin, Bernard J. de la Cruz, David L. Wild:
Discovering transcriptional modules by Bayesian data integration. Bioinform. 26(12): 158-167 (2010) - [j35]Oliver Stegle, Katherine J. Denby, Emma J. Cooke, David L. Wild, Zoubin Ghahramani, Karsten M. Borgwardt:
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. J. Comput. Biol. 17(3): 355-367 (2010) - [j34]Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos, Zoubin Ghahramani:
Kronecker Graphs: An Approach to Modeling Networks. J. Mach. Learn. Res. 11: 985-1042 (2010) - [j33]Ramin Zabih, Jiri Matas, Zoubin Ghahramani:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 32(5): 769 (2010) - [j32]Ramin Zabih, Jiri Matas, Zoubin Ghahramani:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 32(8): 1345-1346 (2010) - [j31]Ramin Zabih, Jiri Matas, Zoubin Ghahramani:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 32(10): 1729 (2010) - [c88]Sébastien Bratières, Jurgen Van Gael, Andreas Vlachos, Zoubin Ghahramani:
Scaling the iHMM: Parallelization versus Hadoop. CIT 2010: 1235-1240 - [c87]Zoubin Ghahramani:
(Invited Talk) Bayesian Hidden Markov Models and Extensions. CoNLL 2010: 56 - [c86]Charalampos Rotsos, Jurgen Van Gael, Andrew W. Moore, Zoubin Ghahramani:
Probabilistic graphical models for semi-supervised traffic classification. IWCMC 2010: 752-757 - [c85]Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan:
Tree-Structured Stick Breaking for Hierarchical Data. NIPS 2010: 19-27 - [c84]Andrew Gordon Wilson, Zoubin Ghahramani:
Copula Processes. NIPS 2010: 2460-2468 - [c83]Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani:
Learning the Structure of Deep Sparse Graphical Models. AISTATS 2010: 1-8 - [c82]Sinead Williamson, Peter Orbanz, Zoubin Ghahramani:
Dependent Indian Buffet Processes. AISTATS 2010: 924-931 - [i5]David A. Knowles, Zoubin Ghahramani:
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling. CoRR abs/1011.6293 (2010)
2000 – 2009
- 2009
- [j30]Richard S. Savage, Katherine A. Heller, Yang Xu, Zoubin Ghahramani, William M. Truman, Murray Grant, Katherine J. Denby, David L. Wild:
R/BHC: fast Bayesian hierarchical clustering for microarray data. BMC Bioinform. 10 (2009) - [j29]Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani:
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models. J. Mach. Learn. Res. 10: 1187-1238 (2009) - [j28]Ramin Zabih, Zoubin Ghahramani, Jiri Matas:
Introduction of New Associate Editors. IEEE Trans. Pattern Anal. Mach. Intell. 31(6): 961-963 (2009) - [j27]Ramin Zabih, Jiri Matas, Zoubin Ghahramani:
Introduction of New Associate Editors. IEEE Trans. Pattern Anal. Mach. Intell. 31(8): 1345-1346 (2009) - [j26]Carl Edward Rasmussen, Bernard J. de la Cruz, Zoubin Ghahramani, David L. Wild:
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures. IEEE ACM Trans. Comput. Biol. Bioinform. 6(4): 615-628 (2009) - [c81]Jurgen Van Gael, Andreas Vlachos, Zoubin Ghahramani:
The infinite HMM for unsupervised PoS tagging. EMNLP 2009: 678-687 - [c80]Oliver Stegle, Katherine J. Denby, David L. Wild, Stuart McHattie, Andrew Meade, Zoubin Ghahramani, Karsten M. Borgwardt:
Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series. GCB 2009: 133-142 - [c79]Ryan Prescott Adams, Zoubin Ghahramani:
Archipelago: nonparametric Bayesian semi-supervised learning. ICML 2009: 1-8 - [c78]Finale Doshi-Velez, Zoubin Ghahramani:
Accelerated sampling for the Indian Buffet Process. ICML 2009: 273-280 - [c77]Finale Doshi-Velez, David A. Knowles, Shakir Mohamed, Zoubin Ghahramani:
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. NIPS 2009: 1294-1302 - [c76]Oliver Stegle, Katherine J. Denby, David L. Wild, Zoubin Ghahramani, Karsten M. Borgwardt:
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. RECOMB 2009: 201-216 - [c75]Finale Doshi-Velez, Zoubin Ghahramani:
Correlated Non-Parametric Latent Feature Models. UAI 2009: 143-150 - [c74]Wei Chu, Zoubin Ghahramani:
Probabilistic Models for Incomplete Multi-dimensional Arrays. AISTATS 2009: 89-96 - [c73]Frederik Eaton, Zoubin Ghahramani:
Choosing a Variable to Clamp. AISTATS 2009: 145-152 - [c72]Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten M. Borgwardt:
A kernel method for unsupervised structured network inference. AISTATS 2009: 368-375 - [c71]Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani:
Factorial Mixture of Gaussians and the Marginal Independence Model. AISTATS 2009: 520-527 - [c70]Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon, Tai Sing Lee:
The Block Diagonal Infinite Hidden Markov Model. AISTATS 2009: 552-559 - [c69]Yang Xu, Katherine A. Heller, Zoubin Ghahramani:
Tree-Based Inference for Dirichlet Process Mixtures. AISTATS 2009: 623-630 - [i4]Karsten M. Borgwardt, Zoubin Ghahramani:
Bayesian two-sample tests. CoRR abs/0906.4032 (2009) - [i3]Ricardo Bezerra de Andrade e Silva, Katherine A. Heller, Zoubin Ghahramani, Edoardo M. Airoldi:
Ranking Relations using Analogies in Biological and Information Networks. CoRR abs/0912.5193 (2009) - 2008
- [j25]Jian Zhang, Zoubin Ghahramani, Yiming Yang:
Flexible latent variable models for multi-task learning. Mach. Learn. 73(3): 221-242 (2008) - [j24]David J. Kriegman, David J. Fleet, Zoubin Ghahramani:
Editorial-State of the Transactions. IEEE Trans. Pattern Anal. Mach. Intell. 30(2): 193-194 (2008) - [j23]David J. Kriegman, David J. Fleet, Zoubin Ghahramani:
Introduction of New Associate Editors. IEEE Trans. Pattern Anal. Mach. Intell. 30(4): 561 (2008) - [j22]David J. Kriegman, David J. Fleet, Zoubin Ghahramani:
Introduction of New Associate Editors. IEEE Trans. Pattern Anal. Mach. Intell. 30(9): 1505-1506 (2008) - [j21]David J. Kriegman, David J. Fleet, Zoubin Ghahramani:
Introduction of New Associate Editors. IEEE Trans. Pattern Anal. Mach. Intell. 30(12): 2065-2066 (2008) - [j20]JaeMo Sung, Zoubin Ghahramani, Sung Yang Bang:
Latent-Space Variational Bayes. IEEE Trans. Pattern Anal. Mach. Intell. 30(12): 2236-2242 (2008) - [j19]JaeMo Sung, Zoubin Ghahramani, Sung Yang Bang:
Second-Order Latent-Space Variational Bayes for Approximate Bayesian Inference. IEEE Signal Process. Lett. 15: 918-921 (2008) - [c68]Zoubin Ghahramani:
Bayesian Methods for Artificial Intelligence and Machine Learning. ECAI 2008: 8 - [c67]Christian Hübler, Hans-Peter Kriegel, Karsten M. Borgwardt, Zoubin Ghahramani:
Metropolis Algorithms for Representative Subgraph Sampling. ICDM 2008: 283-292 - [c66]Katherine A. Heller, Sinead Williamson, Zoubin Ghahramani:
Statistical models for partial membership. ICML 2008: 392-399 - [c65]Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani:
Beam sampling for the infinite hidden Markov model. ICML 2008: 1088-1095 - [c64]Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani:
Bayesian Exponential Family PCA. NIPS 2008: 1089-1096 - [c63]Jurgen Van Gael, Yee Whye Teh, Zoubin Ghahramani:
The Infinite Factorial Hidden Markov Model. NIPS 2008: 1697-1704 - [c62]Hyun-Chul Kim, Zoubin Ghahramani:
Outlier Robust Gaussian Process Classification. SSPR/SPR 2008: 896-905 - [i2]Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos, Zoubin Ghahramani:
Kronecker Graphs: An Approach to Modeling Networks. CoRR abs/0812.4905 (2008) - 2007
- [c61]David A. Knowles, Zoubin Ghahramani:
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. ICA 2007: 381-388 - [c60]Ricardo Bezerra de Andrade e Silva, Wei Chu, Zoubin Ghahramani:
Hidden Common Cause Relations in Relational Learning. NIPS 2007: 1345-1352 - [c59]Katherine A. Heller, Zoubin Ghahramani:
A Nonparametric Bayesian Approach to Modeling Overlapping Clusters. AISTATS 2007: 187-194 - [c58]Ricardo Bezerra de Andrade e Silva, Katherine A. Heller, Zoubin Ghahramani:
Analogical Reasoning with Relational Bayesian Sets. AISTATS 2007: 500-507 - [c57]Edward Lloyd Snelson, Zoubin Ghahramani:
Local and global sparse Gaussian process approximations. AISTATS 2007: 524-531 - [c56]Yee Whye Teh, Dilan Görür, Zoubin Ghahramani:
Stick-breaking Construction for the Indian Buffet Process. AISTATS 2007: 556-563 - [e3]Zoubin Ghahramani:
Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007. ACM International Conference Proceeding Series 227, ACM 2007, ISBN 978-1-59593-793-3 [contents] - 2006
- [j18]Hyun-Chul Kim, Zoubin Ghahramani:
Bayesian Gaussian Process Classification with the EM-EP Algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 28(12): 1948-1959 (2006) - [j17]Hyun-Chul Kim, Daijin Kim, Zoubin Ghahramani, Sung Yang Bang:
Appearance-based gender classification with Gaussian processes. Pattern Recognit. Lett. 27(6): 618-626 (2006) - [j16]Wei Chu, Zoubin Ghahramani, Alexei A. Podtelezhnikov, David L. Wild:
Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 3(2): 98-113 (2006) - [c55]Arik Azran, Zoubin Ghahramani:
Spectral Methods for Automatic Multiscale Data Clustering. CVPR (1) 2006: 190-197 - [c54]Katherine A. Heller, Zoubin Ghahramani:
A Simple Bayesian Framework for Content-Based Image Retrieval. CVPR (2) 2006: 2110-2117 - [c53]Daisuke Kurata, Yoshihiko Nankaku, Keiichi Tokuda, Tadashi Kitamura, Zoubin Ghahramani:
Face Recognition Based on Separable Lattice HMMS. ICASSP (5) 2006: 737-740 - [c52]Arik Azran, Zoubin Ghahramani:
A new approach to data driven clustering. ICML 2006: 57-64 - [c51]Hyun-Chul Kim, Daijin Kim, Zoubin Ghahramani, Sung Yang Bang:
Gender Classification with Bayesian Kernel Methods. IJCNN 2006: 3371-3376 - [c50]Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi:
Relational Learning with Gaussian Processes. NIPS 2006: 289-296 - [c49]Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis:
Modeling Dyadic Data with Binary Latent Factors. NIPS 2006: 977-984 - [c48]Wei Chu, Zoubin Ghahramani, Roland Krause, David L. Wild:
Identifying Protein Complexes in High-Throughput Protein Interaction Screens Using an Infinite Latent Feature Model. Pacific Symposium on Biocomputing 2006: 231-242 - [c47]Iain Murray, Zoubin Ghahramani, David J. C. MacKay:
MCMC for Doubly-intractable Distributions. UAI 2006 - [c46]Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani:
Bayesian Inference for Gaussian Mixed Graph Models. UAI 2006 - [c45]Edward Lloyd Snelson, Zoubin Ghahramani:
Variable Noise and Dimensionality Reduction for Sparse Gaussian processes. UAI 2006 - [c44]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI 2006 - [p4]Xiaojin Zhu, Jaz S. Kandola, John Lafferty, Zoubin Ghahramani:
Graph Kernels by Spectral Transforms. Semi-Supervised Learning 2006: 276-291 - [p3]Wei Chu, S. Sathiya Keerthi, Chong Jin Ong, Zoubin Ghahramani:
Bayesian Support Vector Machines for Feature Ranking and Selection. Feature Extraction 2006: 403-418 - 2005
- [j15]Matthew J. Beal, Francesco Falciani, Zoubin Ghahramani, Claudia Rangel, David L. Wild:
A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinform. 21(3): 349-356 (2005) - [j14]Wei Chu, Zoubin Ghahramani, Francesco Falciani, David L. Wild:
Biomarker discovery in microarray gene expression data with Gaussian processes. Bioinform. 21(16): 3385-3393 (2005) - [j13]Wei Chu, Zoubin Ghahramani:
Gaussian Processes for Ordinal Regression. J. Mach. Learn. Res. 6: 1019-1041 (2005) - [c43]JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin Ghahramani:
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models. ECML 2005: 377-388 - [c42]Wei Chu, Zoubin Ghahramani:
Preference learning with Gaussian processes. ICML 2005: 137-144 - [c41]Katherine A. Heller, Zoubin Ghahramani:
Bayesian hierarchical clustering. ICML 2005: 297-304 - [c40]Edward Lloyd Snelson, Zoubin Ghahramani:
Compact approximations to Bayesian predictive distributions. ICML 2005: 840-847 - [c39]Zoubin Ghahramani, Katherine A. Heller:
Bayesian Sets. NIPS 2005: 435-442 - [c38]Thomas L. Griffiths, Zoubin Ghahramani:
Infinite latent feature models and the Indian buffet process. NIPS 2005: 475-482 - [c37]Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling:
Nested sampling for Potts models. NIPS 2005: 947-954 - [c36]Edward Lloyd Snelson, Zoubin Ghahramani:
Sparse Gaussian Processes using Pseudo-inputs. NIPS 2005: 1257-1264 - [c35]Jian Zhang, Zoubin Ghahramani, Yiming Yang:
Learning Multiple Related Tasks using Latent Independent Component Analysis. NIPS 2005: 1585-1592 - [e2]Robert G. Cowell, Zoubin Ghahramani:
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTATS 2005, Bridgetown, Barbados, January 6-8, 2005. Society for Artificial Intelligence and Statistics 2005 [contents] - 2004
- [j12]Claudia Rangel, John Angus, Zoubin Ghahramani, Maria Lioumi, Elizabeth Sotheran, Alessia Gaiba, David L. Wild, Francesco Falciani:
Modeling T-cell activation using gene expression profiling and state-space models. Bioinform. 20(9): 1361-1372 (2004) - [j11]Sebastian Thrun, Yufeng Liu, Daphne Koller, Andrew Y. Ng, Zoubin Ghahramani, Hugh F. Durrant-Whyte:
Simultaneous Localization and Mapping with Sparse Extended Information Filters. Int. J. Robotics Res. 23(7-8): 693-716 (2004) - [c34]Wei Chu, Zoubin Ghahramani, David L. Wild:
Protein secondary structure prediction using sigmoid belief networks to parameterize segmental semi-Markov models. ESANN 2004: 81-86 - [c33]Wei Chu, Zoubin Ghahramani, David L. Wild:
A graphical model for protein secondary structure prediction. ICML 2004 - [c32]Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani:
Predictive automatic relevance determination by expectation propagation. ICML 2004 - [c31]Jian Zhang, Zoubin Ghahramani, Yiming Yang:
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection. NIPS 2004: 1617-1624 - [c30]Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty:
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning. NIPS 2004: 1641-1648 - [c29]Philip E. Bourne, C. K. J. Allerston, Werner G. Krebs, Wilfred W. Li, Ilya N. Shindyalov, Adam Godzik, Iddo Friedberg, Tong Liu, David L. Wild, Seungwoo Hwang, Zoubin Ghahramani, Li Chen, John D. Westbrook:
The Status of Structural Genomics Defined Through the Analysis of Current Targets and Structures. Pacific Symposium on Biocomputing 2004: 375-386 - [c28]Ananya Dubey, Seungwoo Hwang, Claudia Rangel, Carl Edward Rasmussen, Zoubin Ghahramani, David L. Wild:
Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models. Pacific Symposium on Biocomputing 2004: 399-410 - [c27]Iain Murray, Zoubin Ghahramani:
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms. UAI 2004: 392-399 - 2003
- [c26]Zoubin Ghahramani:
Unsupervised Learning. Advanced Lectures on Machine Learning 2003: 72-112 - [c25]Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani:
Optimization with EM and Expectation-Conjugate-Gradient. ICML 2003: 672-679 - [c24]Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty:
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML 2003: 912-919 - [c23]Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani:
Warped Gaussian Processes. NIPS 2003: 337-344 - [c22]Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani:
On the Convergence of Bound Optimization Algorithms. UAI 2003: 509-516 - 2002
- [j10]Alpan Raval, Zoubin Ghahramani, David L. Wild:
A Bayesian network model for protein fold and remote homologue recognition. Bioinform. 18(6): 788-801 (2002) - [j9]Naonori Ueda, Zoubin Ghahramani:
Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks 15(10): 1223-1241 (2002) - [c21]Carl Edward Rasmussen, Zoubin Ghahramani:
Bayesian Monte Carlo. NIPS 2002: 489-496 - [c20]Rong Jin, Zoubin Ghahramani:
Learning with Multiple Labels. NIPS 2002: 897-904 - [c19]Sebastian Thrun, Daphne Koller, Zoubin Ghahramani, Hugh F. Durrant-Whyte, Andrew Y. Ng:
Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results. WAFR 2002: 363-380 - 2001
- [j8]Zoubin Ghahramani:
An Introduction to Hidden Markov Models and Bayesian Networks. Int. J. Pattern Recognit. Artif. Intell. 15(1): 9-42 (2001) - [c18]Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen:
The Infinite Hidden Markov Model. NIPS 2001: 577-584 - [c17]Carl Edward Rasmussen, Zoubin Ghahramani:
Infinite Mixtures of Gaussian Process Experts. NIPS 2001: 881-888 - [e1]Thomas G. Dietterich, Suzanna Becker, Zoubin Ghahramani:
Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]. MIT Press 2001 [contents] - 2000
- [j7]Zoubin Ghahramani, Geoffrey E. Hinton:
Variational Learning for Switching State-Space Models. Neural Comput. 12(4): 831-864 (2000) - [j6]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. Neural Comput. 12(9): 2109-2128 (2000) - [j5]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates. J. VLSI Signal Process. 26(1-2): 133-140 (2000) - [c16]Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani:
MFDTs: Mean Field Dynamic Trees. ICPR 2000: 3151-3154 - [c15]Carl Edward Rasmussen, Zoubin Ghahramani:
Occam's Razor. NIPS 2000: 294-300 - [c14]Zoubin Ghahramani, Matthew J. Beal:
Propagation Algorithms for Variational Bayesian Learning. NIPS 2000: 507-513
1990 – 1999
- 1999
- [j4]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Mach. Learn. 37(2): 183-233 (1999) - [j3]Sam T. Roweis, Zoubin Ghahramani:
A Unifying Review of Linear Gaussian Models. Neural Comput. 11(2): 305-345 (1999) - [c13]Zoubin Ghahramani, Matthew J. Beal:
Variational Inference for Bayesian Mixtures of Factor Analysers. NIPS 1999: 449-455 - [c12]Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh:
Learning to Parse Images. NIPS 1999: 463-469 - 1998
- [c11]Zoubin Ghahramani, Sam T. Roweis:
Learning Nonlinear Dynamical Systems Using an EM Algorithm. NIPS 1998: 431-437 - [c10]Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. NIPS 1998: 599-605 - [p2]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models 1998: 105-161 - [p1]Geoffrey E. Hinton, Brian Sallans, Zoubin Ghahramani:
A Hierarchical Community of Experts. Learning in Graphical Models 1998: 479-494 - 1997
- [j2]Zoubin Ghahramani, Michael I. Jordan:
Factorial Hidden Markov Models. Mach. Learn. 29(2-3): 245-273 (1997) - [c9]Zoubin Ghahramani, Geoffrey E. Hinton:
Hierarchical Non-linear Factor Analysis and Topographic Maps. NIPS 1997: 486-492 - [c8]Zoubin Ghahramani:
Learning Dynamic Bayesian Networks. Summer School on Neural Networks 1997: 168-197 - 1996
- [j1]David A. Cohn, Zoubin Ghahramani, Michael I. Jordan:
Active Learning with Statistical Models. J. Artif. Intell. Res. 4: 129-145 (1996) - [c7]Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul:
Hidden Markov Decision Trees. NIPS 1996: 501-507 - [i1]David A. Cohn, Zoubin Ghahramani, Michael I. Jordan:
Active Learning with Statistical Models. CoRR cs.AI/9603104 (1996) - 1995
- [c6]Zoubin Ghahramani, Michael I. Jordan:
Factorial Hidden Markov Models. NIPS 1995: 472-478 - 1994
- [c5]Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan:
Forward dynamic models in human motor control: Psychophysical evidence. NIPS 1994: 43-50 - [c4]Zoubin Ghahramani:
Factorial Learning and the EM Algorithm. NIPS 1994: 617-624 - [c3]David A. Cohn, Zoubin Ghahramani, Michael I. Jordan:
Active Learning with Statistical Models. NIPS 1994: 705-712 - [c2]Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan:
Computational Structure of coordinate transformations: A generalization study. NIPS 1994: 1125-1132 - 1993
- [c1]Zoubin Ghahramani, Michael I. Jordan:
Supervised learning from incomplete data via an EM approach. NIPS 1993: 120-127
Coauthor Index
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