default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDZoubin Ghahramani
Person information
- affiliation: Google Brain
- affiliation (former): University College London, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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]