Zoubin Ghahramani

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 106

Top Venues

CoRR

ICML

NIPS

AISTATS

Publications

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 (2024)
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 (2024)
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson
Neural Diffusion Processes. ICML (2023)
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson
Neural Diffusion Processes. CoRR (2022)
Dustin Tran, Jeremiah 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 (2022)
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 (2022)
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 (2021)
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 (2021)
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)
Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani
General Latent Feature Models for Heterogeneous Datasets. J. Mach. Learn. Res. 21 (2020)
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 (2020)
Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera
Handling incomplete heterogeneous data using VAEs. Pattern Recognit. 107 (2020)
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)
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 (2020)
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 (2020)
Mohamed Tarek, Kai Xu, Martin Trapp, Hong Ge, Zoubin Ghahramani
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models. CoRR (2020)
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29 (5) (2019)
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)
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Bayesian Learning of Sum-Product Networks. NeurIPS (2019)
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)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. AAAI (2019)
Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller
One-Network Adversarial Fairness. AAAI (2019)
Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Bayesian Learning of Sum-Product Networks. CoRR (2019)
Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda
Imitation networks: Few-shot learning of neural networks from scratch. CoRR (2018)
Adam Scibior, Ohad Kammar, Zoubin Ghahramani
Functional programming for modular Bayesian inference. Proc. ACM Program. Lang. 2 (ICFP) (2018)
Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR (2018)
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) (2018)
Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani
Variational Measure Preserving Flows. CoRR (2018)
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Variational Bayesian dropout: pitfalls and fixes. CoRR (2018)
Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani
Probabilistic Meta-Representations Of Neural Networks. CoRR (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR (2018)
Yusuke Mukuta, Akisato Kimura, David B. Adrian, Zoubin Ghahramani
Weakly supervised collective feature learning from curated media. CoRR (2018)
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 (2018)
Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera
Handling Incomplete Heterogeneous Data using VAEs. CoRR (2018)
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)
Tameem Adel, Zoubin Ghahramani, Adrian Weller
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. ICML (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML (2018)
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Variational Bayesian dropout: pitfalls and fixes. ICML (2018)
Yusuke Mukuta, Akisato Kimura, David B. Adrian, Zoubin Ghahramani
Weakly Supervised Collective Feature Learning From Curated Media. AAAI (2018)
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR (2018)
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)
Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda
Few-shot learning of neural networks from scratch by pseudo example optimization. BMVC (2018)
Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Automatic Bayesian Density Analysis. CoRR (2018)
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
Gaussian Process Behaviour in Wide Deep Neural Networks. CoRR (2018)
Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
MetaGAN: An Adversarial Approach to Few-Shot Learning. NeurIPS (2018)
Hong Ge, Kai Xu, Zoubin Ghahramani
Turing: Composable inference for probabilistic programming. AISTATS (2018)
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) (2018)
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
Lost Relatives of the Gumbel Trick. CoRR (2017)
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 (2017)
Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani
One-Shot Learning in Discriminative Neural Networks. CoRR (2017)
Konstantina Palla, David A. Knowles, Zoubin Ghahramani
A Birth-Death Process for Feature Allocation. ICML (2017)
Isabel Valera, Zoubin Ghahramani
Automatic Discovery of the Statistical Types of Variables in a Dataset. ICML (2017)
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Deep Bayesian Active Learning with Image Data. CoRR (2017)
Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani
General Latent Feature Modeling for Data Exploration Tasks. CoRR (2017)
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 (2017)
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
Magnetic Hamiltonian Monte Carlo. ICML (2017)
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 (2017)
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)
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
Lost Relatives of the Gumbel Trick. ICML (2017)
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi
Bayesian inference on random simple graphs with power law degree distributions. ICML (2017)
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)
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Deep Bayesian Active Learning with Image Data. ICML (2017)
Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
The Mondrian Kernel. UAI (2016)
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 (2016)
Amar Shah, Zoubin Ghahramani
Markov Beta Processes for Time Evolving Dictionary Learning. UAI (2016)
Tomoharu Iwata, James Robert Lloyd, Zoubin Ghahramani
Unsupervised Many-to-Many Object Matching for Relational Data. IEEE Trans. Pattern Anal. Mach. Intell. 38 (3) (2016)
Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR (2016)
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)
Yarin Gal, Zoubin Ghahramani
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS (2016)
Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy
A study of the effect of JPG compression on adversarial images. CoRR (2016)
Yutian Chen, Zoubin Ghahramani
Scalable Discrete Sampling as a Multi-Armed Bandit Problem. ICML (2016)
Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS (2016)
Yarin Gal, Zoubin Ghahramani
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML (2016)
Amar Shah, Zoubin Ghahramani
Pareto Frontier Learning with Expensive Correlated Objectives. ICML (2016)
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) (2016)
Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani
Distributed Flexible Nonlinear Tensor Factorization. NIPS (2016)
Yarin Gal, Zoubin Ghahramani
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. CoRR (2015)
Amar Shah, Zoubin Ghahramani
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions. CoRR (2015)
John P. Cunningham, Zoubin Ghahramani
Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16 (2015)
Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani
Distributed Inference for Dirichlet Process Mixture Models. ICML (2015)
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) (2015)
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)
Christian Steinruecken, Zoubin Ghahramani, David J. C. MacKay
Improving PPM with Dynamic Parameter Updates. DCC (2015)
David A. Knowles, Zoubin Ghahramani
Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Yarin Gal, Yutian Chen, Zoubin Ghahramani
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. ICML (2015)
Amar Shah, David A. Knowles, Zoubin Ghahramani
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process. ICML (2015)
Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR (2015)
Amar Shah, Zoubin Ghahramani
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions. NIPS (2015)
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani
MCMC for Variationally Sparse Gaussian Processes. NIPS (2015)
Razvan Ranca, Zoubin Ghahramani
Slice Sampling for Probabilistic Programming. CoRR (2015)
Novi Quadrianto, Zoubin Ghahramani
A Very Simple Safe-Bayesian Random Forest. IEEE Trans. Pattern Anal. Mach. Intell. 37 (6) (2015)
Sébastien Bratières, Novi Quadrianto, Zoubin Ghahramani
GPstruct: Bayesian Structured Prediction Using Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 37 (7) (2015)
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Neural Adaptive Sequential Monte Carlo. CoRR (2015)
Adam Scibior, Zoubin Ghahramani, Andrew D. Gordon
Practical probabilistic programming with monads. Haskell (2015)
Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani
Particle Gibbs for Infinite Hidden Markov Models. NIPS (2015)
Yarin Gal, Zoubin Ghahramani
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. CoRR (2015)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML (2015)
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Neural Adaptive Sequential Monte Carlo. NIPS (2015)
Amar Shah, David A. Knowles, Zoubin Ghahramani
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process. CoRR (2015)
Zoubin Ghahramani
Probabilistic machine learning and artificial intelligence. Nat. 521 (7553) (2015)

Zoubin Ghahramani.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Zoubin Ghahramani