Sequential Monte Carlo for model selection and estimation of neural networks. - Christophe Andrieu; Nando de Freitas (ICASSP, 2000)

The Unscented Particle Filter. - Rudolph van der Merwe; Arnaud Doucet; Nando de Freitas; Eric A. Wan (NIPS, 2000)

Reversible Jump MCMC Simulated Annealing for Neural Networks. - Christophe Andrieu; Nando de Freitas; Arnaud Doucet (UAI, 2000)

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. - Arnaud Doucet; Nando de Freitas; Kevin P. Murphy; Stuart Russell 001 (UAI, 2000)

An Introduction to Sequential Monte Carlo Methods. - Arnaud Doucet; Nando de Freitas; Neil J. Gordon (Sequential Monte Carlo Methods in Practice, 2001)

Sequential Monte Carlo Methods for Neural Networks. - Nando de Freitas; Christophe Andrieu; Pedro A. d. F. R. Højen-Sørensen; M. Niranjan; A. Gee (Sequential Monte Carlo Methods in Practice, 2001)

Rao-Blackwellised Particle Filtering via Data Augmentation. - Christophe Andrieu; Nando de Freitas; Arnaud Doucet (NIPS, 2001)

Variational MCMC. - Nando de Freitas; Pedro A. d. F. R. Højen-Sørensen; Stuart Russell 001 (UAI, 2001)

Robust Full Bayesian Learning for Radial Basis Networks. - Christophe Andrieu; Nando de Freitas; Arnaud Doucet (Neural Comput., 2001)

"Name That Song!" A Probabilistic Approach to Querying on Music and Text. - Eric Brochu; Nando de Freitas (NIPS, 2002)

Real-Time Monitoring of Complex Industrial Processes with Particle Filters. - Rubén Morales-Menéndez; Nando de Freitas; David Poole 001 (NIPS, 2002)

The Sound of an Album Cover: A Probabilistic Approach to Multimedia. - Eric Brochu; Nando de Freitas; Kejie Bao (AISTATS, 2003)

Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition. - Paul Gustafson; Peter Carbonetto; Natalie Thompson; Nando de Freitas (AISTATS, 2003)

A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference. - Pinar Muyan; Nando de Freitas (AISTATS, 2003)

Estimation and control of industrial processes with particle filters. - Rubén Morales-Menéndez; Nando de Freitas; David Poole 001 (ACC, 2003)

Matching Words and Pictures. - Kobus Barnard; Pinar Duygulu; David A. Forsyth; Nando de Freitas; David M. Blei; Michael I. Jordan (J. Mach. Learn. Res., 2003)

An Introduction to MCMC for Machine Learning. - Christophe Andrieu; Nando de Freitas; Arnaud Doucet; Michael I. Jordan (Mach. Learn., 2003)

A Statistical Model for General Contextual Object Recognition. - Peter Carbonetto; Nando de Freitas; Kobus Barnard (ECCV (1), 2004)

A Constrained Semi-supervised Learning Approach to Data Association. - Hendrik Kück; Peter Carbonetto; Nando de Freitas (ECCV (3), 2004)

A Boosted Particle Filter: Multitarget Detection and Tracking. - Kenji Okuma; Ali Taleghani; Nando de Freitas; James J. Little; David G. Lowe (ECCV (1), 2004)

Beat Tracking the Graphical Model Way. - Dustin Lang; Nando de Freitas (NIPS, 2004)

From Fields to Trees. - Firas Hamze; Nando de Freitas (UAI, 2004)

Diagnosis by a waiter and a Mars explorer. - Nando de Freitas; Richard Dearden; Frank Hutter; Rubén Morales-Menéndez; Jim Mutch; David Poole 001 (Proc. IEEE, 2004)

Fast maximum a-posteriori inference on Monte Carlo state spaces. - Mike Klaas; Dustin Lang; Nando de Freitas (AISTATS, 2005)

Fast Computational Methods for Visually Guided Robots. - Maryam Mahdaviani; Nando de Freitas; Bob Fraser; Firas Hamze (ICRA, 2005)

Fast Krylov Methods for N-Body Learning. - Nando de Freitas; Yang Wang 003; Maryam Mahdaviani; Dustin Lang (NIPS, 2005)

Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. - Firas Hamze; Nando de Freitas (NIPS, 2005)

Nonparametric Bayesian Logic. - Peter Carbonetto; Jacek Kisynski; Nando de Freitas; David Poole 001 (UAI, 2005)

Learning about Individuals from Group Statistics. - Nando de Freitas; Hendrik Kück (UAI, 2005)

Toward Practical N2 Monte Carlo: the Marginal Particle Filter. - Mike Klaas; Nando de Freitas; Arnaud Doucet (UAI, 2005)

A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues. - Peter Carbonetto; Gyuri Dorkó; Cordelia Schmid; Hendrik Kück; Nando de Freitas (Toward Category-Level Object Recognition, 2006)

Robust Visual Tracking for Multiple Targets. - Yizheng Cai; Nando de Freitas; James J. Little (ECCV (4), 2006)

Fast particle smoothing: if I had a million particles. - Mike Klaas; Mark Briers; Nando de Freitas; Arnaud Doucet; Simon Maskell; Dustin Lang (ICML, 2006)

Conditional mean field. - Peter Carbonetto; Nando de Freitas (NIPS, 2006)

Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM. - Ruben Martinez-Cantin; Nando de Freitas; José A. Castellanos 001 (ICRA, 2007)

Active Preference Learning with Discrete Choice Data. - Eric Brochu; Nando de Freitas; Abhijeet Ghosh (NIPS, 2007)

Bayesian Policy Learning with Trans-Dimensional MCMC. - Matt Hoffman 001; Arnaud Doucet; Nando de Freitas; Ajay Jasra (NIPS, 2007)

Active Policy Learning for Robot Planning and Exploration under Uncertainty. - Ruben Martinez-Cantin; Nando de Freitas; Arnaud Doucet; José A. Castellanos 001 (Robotics: Science and Systems, 2007)

Preference galleries for material design. - Eric Brochu; Abhijeet Ghosh; Nando de Freitas (SIGGRAPH Posters, 2007)

Large-Flip Importance Sampling. - Firas Hamze; Nando de Freitas (UAI, 2007)

Target-directed attention: Sequential decision-making for gaze planning. - Julia Vogel; Nando de Freitas (ICRA, 2008)

An interior-point stochastic approximation method and an L1-regularized delta rule. - Peter Carbonetto; Mark Schmidt 001; Nando de Freitas (NIPS, 2008)

Learning to Recognize Objects with Little Supervision. - Peter Carbonetto; Gyuri Dorkó; Cordelia Schmid; Hendrik Kück; Nando de Freitas (Int. J. Comput. Vis., 2008)

Inference and Learning for Active Sensing, Experimental Design and Control. - Hendrik Kück; Matt Hoffman 001; Arnaud Doucet; Nando de Freitas (IbPRIA, 2009)

New inference strategies for solving Markov Decision Processes using reversible jump MCMC. - Matthias Hoffman; Hendrik Kück; Nando de Freitas; Arnaud Doucet (UAI, 2009)

A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. - Ruben Martinez-Cantin; Nando de Freitas; Eric Brochu; José A. Castellanos 001; Arnaud Doucet (Auton. Robots, 2009)

An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. - Matthew Hoffman 002; Nando de Freitas; Arnaud Doucet; Jan Peters 001 (AISTATS, 2009)

A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. - Kevin Swersky; Bo Chen 019; Benjamin M. Marlin; Nando de Freitas (ITA, 2010)

A Bayesian Interactive Optimization Approach to Procedural Animation Design. - Eric Brochu; Tyson Brochu; Nando de Freitas (Symposium on Computer Animation, 2010)

Intracluster Moves for Constrained Discrete-Space MCMC. - Firas Hamze; Nando de Freitas (UAI, 2010)

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning - Eric Brochu; Vlad M. Cora; Nando de Freitas (CoRR, 2010)

Inductive Principles for Restricted Boltzmann Machine Learning. - Benjamin M. Marlin; Kevin Swersky; Bo Chen 019; Nando de Freitas (AISTATS, 2010)

Learning attentional policies for tracking and recognition in video with deep networks. - Loris Bazzani; Nando de Freitas; Hugo Larochelle; Vittorio Murino; Jo-Anne Ting (ICML, 2011)

On Autoencoders and Score Matching for Energy Based Models. - Kevin Swersky; Marc'Aurelio Ranzato; David Buchman; Benjamin M. Marlin; Nando de Freitas (ICML, 2011)

Portfolio Allocation for Bayesian Optimization. - Matthew Hoffman 002; Eric Brochu; Nando de Freitas (UAI, 2011)

Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. - Benjamin M. Marlin; Nando de Freitas (UAI, 2011)

A Machine Learning Perspective on Predictive Coding with PAQ - Byron Knoll; Nando de Freitas (CoRR, 2011)

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. - Michael A. Osborne; Roman Garnett; Kevin Swersky; Nando de Freitas (AAAI, 2012)

A Machine Learning Perspective on Predictive Coding with PAQ8. - Byron Knoll; Nando de Freitas (DCC, 2012)

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. - Nando de Freitas; Alexander J. Smola; Masrour Zoghi (ICML, 2012)

Regret Bounds for Deterministic Gaussian Process Bandits - Nando de Freitas; Alexander J. Smola; Masrour Zoghi (CoRR, 2012)

Decentralized, Adaptive, Look-Ahead Particle Filtering - Mohamed Osama Ahmed; Pouyan T. Bibalan; Nando de Freitas; Simon Fauvel (CoRR, 2012)

Recklessly Approximate Sparse Coding - Misha Denil; Nando de Freitas (CoRR, 2012)

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. - David Buchman; Mark Schmidt 001; Shakir Mohamed; David Poole 001; Nando de Freitas (AISTATS, 2012)

Adaptive MCMC with Bayesian Optimization. - Nimalan Mahendran; Ziyu Wang 001; Firas Hamze; Nando de Freitas (AISTATS, 2012)

Learning Where to Attend with Deep Architectures for Image Tracking. - Misha Denil; Loris Bazzani; Hugo Larochelle; Nando de Freitas (Neural Comput., 2012)

Consistency of Online Random Forests. - Misha Denil; David Matheson; Nando de Freitas (ICML (3), 2013)

Adaptive Hamiltonian and Riemann Manifold Monte Carlo. - Ziyu Wang 001; Shakir Mohamed; Nando de Freitas (ICML (3), 2013)

Bayesian Optimization in High Dimensions via Random Embeddings. - Ziyu Wang 001; Masrour Zoghi; Frank Hutter; David Matheson; Nando de Freitas (IJCAI, 2013)

Predicting Parameters in Deep Learning. - Misha Denil; Babak Shakibi; Laurent Dinh; Marc'Aurelio Ranzato; Nando de Freitas (NIPS, 2013)

Efficient Learning of Practical Markov Random Fields with Exact Inference. - Yariv Dror Mizrahi; Nando de Freitas; Luis Tenorio (CoRR, 2013)

Variational MCMC - Nando de Freitas; Pedro A. d. F. R. Højen-Sørensen; Michael I. Jordan; Stuart Russell 001 (CoRR, 2013)

Herded Gibbs Sampling - Luke Bornn; Yutian Chen 001; Nando de Freitas; Mareija Eskelin; Jing Fang; Max Welling (ICLR, 2013)

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012) - Nando de Freitas; Kevin P. Murphy (CoRR, 2013)

Best arm identification via Bayesian gap-based exploration - Matthew W. Hoffman; Bobak Shahriari; Nando de Freitas (CoRR, 2013)

Self-Avoiding Random Dynamics on Integer Complex Systems. - Firas Hamze; Ziyu Wang 001; Nando de Freitas (ACM Trans. Model. Comput. Simul., 2013)

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. - Matthew W. Hoffman; Bobak Shahriari; Nando de Freitas (AISTATS, 2014)

Bayesian Multi-Scale Optimistic Optimization. - Ziyu Wang 001; Babak Shakibi; Lin Jin; Nando de Freitas (AISTATS, 2014)

Narrowing the Gap: Random Forests In Theory and In Practice. - Misha Denil; David Matheson; Nando de Freitas (ICML, 2014)

Linear and Parallel Learning of Markov Random Fields. - Yariv Dror Mizrahi; Misha Denil; Nando de Freitas (ICML, 2014)

Distributed Parameter Estimation in Probabilistic Graphical Models. - Yariv Dror Mizrahi; Misha Denil; Nando de Freitas (NIPS, 2014)

Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search. - Julieta Martinez 001; James J. Little; Nando de Freitas (WACV, 2014)

Heteroscedastic Treed Bayesian Optimisation. - John-Alexander M. Assael; Ziyu Wang 001; Nando de Freitas (CoRR, 2014)

Extraction of Salient Sentences from Labelled Documents. - Misha Denil; Alban Demiraj; Nando de Freitas (CoRR, 2014)

Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network. - Misha Denil; Alban Demiraj; Nal Kalchbrenner; Phil Blunsom; Nando de Freitas (CoRR, 2014)

A Deep Architecture for Semantic Parsing. - Edward Grefenstette; Phil Blunsom; Nando de Freitas; Karl Moritz Hermann (CoRR, 2014)

Deep Multi-Instance Transfer Learning. - Dimitrios Kotzias; Misha Denil; Phil Blunsom; Nando de Freitas (CoRR, 2014)

An Entropy Search Portfolio for Bayesian Optimization. - Bobak Shahriari; Ziyu Wang 001; Matthew W. Hoffman; Alexandre Bouchard-Côté; Nando de Freitas (CoRR, 2014)

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters. - Ziyu Wang 001; Nando de Freitas (CoRR, 2014)

Deep Apprenticeship Learning for Playing Video Games. - Miroslav Bogdanovic; Dejan Markovikj; Misha Denil; Nando de Freitas (AAAI Workshop: Learning for General Competency in Video Games, 2015)

Extraction of Morphological QRS-based Biomarkers in Hypertrophic Cardiomyopathy for Risk Stratification Using L1 Regularized Logistic Regression. - Aurore Lyon; Ana Mincholé; Rina Ariga; Pablo Laguna; Stefan Neubauer; Hugh Watkins; Nando de Freitas; Blanca Rodríguez (CinC, 2015)

Deep Fried Convnets. - Zichao Yang; Marcin Moczulski; Misha Denil; Nando de Freitas; Alexander J. Smola; Le Song; Ziyu Wang 001 (ICCV, 2015)

From Group to Individual Labels Using Deep Features. - Dimitrios Kotzias; Misha Denil; Nando de Freitas; Padhraic Smyth (KDD, 2015)

Dueling Network Architectures for Deep Reinforcement Learning. - Ziyu Wang 001; Nando de Freitas; Marc Lanctot (CoRR, 2015)

Unbounded Bayesian Optimization via Regularization. - Bobak Shahriari; Alexandre Bouchard-Côté; Nando de Freitas (AISTATS, 2016)

Dueling Network Architectures for Deep Reinforcement Learning. - Ziyu Wang 001; Tom Schaul; Matteo Hessel; Hado van Hasselt; Marc Lanctot; Nando de Freitas (ICML, 2016)

Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality. - Nando de Freitas (KDD, 2016)

Learning to learn by gradient descent by gradient descent. - Marcin Andrychowicz; Misha Denil; Sergio Gomez Colmenarejo; Matthew W. Hoffman; David Pfau; Tom Schaul; Nando de Freitas (NIPS, 2016)

Learning to Communicate with Deep Multi-Agent Reinforcement Learning. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (NIPS, 2016)

LipNet: Sentence-level Lipreading. - Yannis M. Assael; Brendan Shillingford; Shimon Whiteson; Nando de Freitas (CoRR, 2016)

Learning to Learn for Global Optimization of Black Box Functions. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Nando de Freitas (CoRR, 2016)

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks. - Jakob N. Foerster; Yannis M. Assael; Nando de Freitas; Shimon Whiteson (CoRR, 2016)

ACDC: A Structured Efficient Linear Layer. - Marcin Moczulski; Misha Denil; Jeremy Appleyard; Nando de Freitas (ICLR (Poster), 2016)

Neural Programmer-Interpreters. - Scott E. Reed; Nando de Freitas (ICLR, 2016)

Bayesian Optimization in a Billion Dimensions via Random Embeddings. - Ziyu Wang 001; Frank Hutter; Masrour Zoghi; David Matheson; Nando de Freitas (J. Artif. Intell. Res., 2016)

Taking the Human Out of the Loop: A Review of Bayesian Optimization. - Bobak Shahriari; Kevin Swersky; Ziyu Wang 001; Ryan P. Adams; Nando de Freitas (Proc. IEEE, 2016)

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. - Serkan Cabi; Sergio Gomez Colmenarejo; Matthew W. Hoffman; Misha Denil; Ziyu Wang 001; Nando de Freitas (CoRL, 2017)

Sample Efficient Actor-Critic with Experience Replay. - Ziyu Wang 001; Victor Bapst; Nicolas Heess; Volodymyr Mnih; Rémi Munos; Koray Kavukcuoglu; Nando de Freitas (ICLR (Poster), 2017)

Learning to Perform Physics Experiments via Deep Reinforcement Learning. - Misha Denil; Pulkit Agrawal 001; Tejas D. Kulkarni; Tom Erez; Peter W. Battaglia; Nando de Freitas (ICLR (Poster), 2017)

Learning to Learn without Gradient Descent by Gradient Descent. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Matthew M. Botvinick; Nando de Freitas (ICML, 2017)

Parallel Multiscale Autoregressive Density Estimation. - Scott E. Reed; Aäron van den Oord; Nal Kalchbrenner; Sergio Gomez Colmenarejo; Ziyu Wang 001; Yutian Chen 001; Dan Belov; Nando de Freitas (ICML, 2017)

Learned Optimizers that Scale and Generalize. - Olga Wichrowska; Niru Maheswaranathan; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Nando de Freitas; Jascha Sohl-Dickstein (ICML, 2017)

Robust Imitation of Diverse Behaviors. - Ziyu Wang 001; Josh Merel; Scott E. Reed; Nando de Freitas; Gregory Wayne; Nicolas Heess (NIPS, 2017)

Cortical microcircuits as gated-recurrent neural networks. - Rui Ponte Costa; Yannis M. Assael; Brendan Shillingford; Nando de Freitas; Tim P. Vogels (NIPS, 2017)

Programmable Agents. - Misha Denil; Sergio Gomez Colmenarejo; Serkan Cabi; David Saxton; Nando de Freitas (CoRR, 2017)

Parallel Multiscale Autoregressive Density Estimation. - Scott E. Reed; Aäron van den Oord; Nal Kalchbrenner; Sergio Gomez Colmenarejo; Ziyu Wang 001; Dan Belov; Nando de Freitas (CoRR, 2017)

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. - Matthew M. Botvinick; David G. T. Barrett; Peter W. Battaglia; Nando de Freitas; Dharshan Kumaran; Joel Z. Leibo; Tim Lillicrap; Joseph Modayil; S. Mohamed; Neil C. Rabinowitz; Danilo Jimenez Rezende; Adam Santoro; et al. (CoRR, 2017)

Learning Awareness Models. - Brandon Amos; Laurent Dinh; Serkan Cabi; Thomas Rothörl; Sergio Gomez Colmenarejo; Alistair Muldal; Tom Erez; Yuval Tassa; Nando de Freitas; Misha Denil (ICLR (Poster), 2018)

Compositional Obverter Communication Learning from Raw Visual Input. - Edward Choi; Angeliki Lazaridou; Nando de Freitas (ICLR (Poster), 2018)

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. - Scott E. Reed; Yutian Chen 001; Thomas Paine; Aäron van den Oord; S. M. Ali Eslami; Danilo J. Rezende; Oriol Vinyals; Nando de Freitas (ICLR (Poster), 2018)

Playing hard exploration games by watching YouTube. - Yusuf Aytar; Tobias Pfaff; David Budden; Tom Le Paine; Ziyu Wang 001; Nando de Freitas (NeurIPS, 2018)

Reinforcement and Imitation Learning for Diverse Visuomotor Skills. - Yuke Zhu; Ziyu Wang 001; Josh Merel; Andrei A. Rusu; Tom Erez; Serkan Cabi; Saran Tunyasuvunakool; János Kramár; Raia Hadsell; Nando de Freitas; Nicolas Heess (Robotics: Science and Systems, 2018)

Large-Scale Visual Speech Recognition. - Brendan Shillingford; Yannis M. Assael; Matthew W. Hoffman; Thomas Paine; Cían Hughes; Utsav Prabhu; Hank Liao; Hasim Sak; Kanishka Rao; Lorrayne Bennett; Marie Mulville; Ben Coppin; et al. (CoRR, 2018)

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL. - Tom Le Paine; Sergio Gomez Colmenarejo; Ziyu Wang 001; Scott E. Reed; Yusuf Aytar; Tobias Pfaff; Matthew W. Hoffman; Gabriel Barth-Maron; Serkan Cabi; David Budden; Nando de Freitas (CoRR, 2018)

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL. - Natasha Jaques; Angeliki Lazaridou; Edward Hughes 001; Çaglar Gülçehre; Pedro A. Ortega; DJ Strouse; Joel Z. Leibo; Nando de Freitas (CoRR, 2018)

Bayesian Optimization in AlphaGo. - Yutian Chen 001; Aja Huang; Ziyu Wang 001; Ioannis Antonoglou; Julian Schrittwieser; David Silver; Nando de Freitas (CoRR, 2018)

Sample Efficient Adaptive Text-to-Speech. - Yutian Chen 001; Yannis M. Assael; Brendan Shillingford; David Budden; Scott E. Reed; Heiga Zen; Quan Wang; Luis C. Cobo; Andrew Trask; Ben Laurie; Çaglar Gülçehre; Aäron van den Oord; et al. (ICLR (Poster), 2019)

Hyperbolic Attention Networks. - Çaglar Gülçehre; Misha Denil; Mateusz Malinowski; Ali Razavi; Razvan Pascanu; Karl Moritz Hermann; Peter W. Battaglia; Victor Bapst; David Raposo; Adam Santoro; Nando de Freitas (ICLR (Poster), 2019)

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. - Natasha Jaques; Angeliki Lazaridou; Edward Hughes 001; Çaglar Gülçehre; Pedro A. Ortega; DJ Strouse; Joel Z. Leibo; Nando de Freitas (ICML, 2019)

Large-Scale Visual Speech Recognition. - Brendan Shillingford; Yannis M. Assael; Matthew W. Hoffman; Thomas Paine; Cían Hughes; Utsav Prabhu; Hank Liao; Hasim Sak; Kanishka Rao; Lorrayne Bennett; Marie Mulville; Misha Denil; et al. (INTERSPEECH, 2019)

Learning Compositional Neural Programs with Recursive Tree Search and Planning. - Thomas Pierrot; Guillaume Ligner; Scott E. Reed; Olivier Sigaud; Nicolas Perrin 001; Alexandre Laterre; David Kas; Karim Beguir; Nando de Freitas (NeurIPS, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

A Framework for Data-Driven Robotics. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (CoRR, 2019)

Task-Relevant Adversarial Imitation Learning. - Konrad Zolna; Scott E. Reed; Alexander Novikov 001; Sergio Gómez Colmenarejo; David Budden; Serkan Cabi; Misha Denil; Nando de Freitas; Ziyu Wang 001 (CoRL, 2020)

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. - Çaglar Gülçehre; Tom Le Paine; Bobak Shahriari; Misha Denil; Matt Hoffman 001; Hubert Soyer; Richard Tanburn; Steven Kapturowski; Neil C. Rabinowitz; Duncan Williams; Gabriel Barth-Maron; Ziyu Wang 001; et al. (ICLR, 2020)

Critic Regularized Regression. - Ziyu Wang 001; Alexander Novikov 001; Konrad Zolna; Josh Merel; Jost Tobias Springenberg; Scott E. Reed; Bobak Shahriari; Noah Y. Siegel; Çaglar Gülçehre; Nicolas Heess; Nando de Freitas (NeurIPS, 2020)

Modular Meta-Learning with Shrinkage. - Yutian Chen 001; Abram L. Friesen; Feryal M. P. Behbahani; Arnaud Doucet; David Budden; Matthew Hoffman 002; Nando de Freitas (NeurIPS, 2020)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 2020)

Scaling data-driven robotics with reward sketching and batch reinforcement learning. - Serkan Cabi; Sergio Gómez Colmenarejo; Alexander Novikov 001; Ksenia Konyushkova; Scott E. Reed; Rae Jeong; Konrad Zolna; Yusuf Aytar; David Budden; Mel Vecerík; Oleg Sushkov; David Barker; et al. (Robotics: Science and Systems, 2020)

Acme: A Research Framework for Distributed Reinforcement Learning. - Matt Hoffman 001; Bobak Shahriari; John Aslanides; Gabriel Barth-Maron; Feryal M. P. Behbahani; Tamara Norman; Abbas Abdolmaleki; Albin Cassirer; Fan Yang; Kate Baumli; Sarah Henderson; Alexander Novikov 001; et al. (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Hyperparameter Selection for Offline Reinforcement Learning. - Tom Le Paine; Cosmin Paduraru; Andrea Michi; Çaglar Gülçehre; Konrad Zolna; Alexander Novikov 001; Ziyu Wang 001; Nando de Freitas (CoRR, 2020)

Learning Compositional Neural Programs for Continuous Control. - Thomas Pierrot; Nicolas Perrin 001; Feryal M. P. Behbahani; Alexandre Laterre; Olivier Sigaud; Karim Beguir; Nando de Freitas (CoRR, 2020)

Large-scale multilingual audio visual dubbing. - Yi Yang 007; Brendan Shillingford; Yannis M. Assael; Miaosen Wang; Wendi Liu; Yutian Chen 001; Yu Zhang; Eren Sezener; Luis C. Cobo; Misha Denil; Yusuf Aytar; Nando de Freitas (CoRR, 2020)

Offline Learning from Demonstrations and Unlabeled Experience. - Konrad Zolna; Alexander Novikov 001; Ksenia Konyushkova; Çaglar Gülçehre; Ziyu Wang 001; Yusuf Aytar; Misha Denil; Nando de Freitas; Scott E. Reed (CoRR, 2020)

Semi-supervised reward learning for offline reinforcement learning. - Ksenia Konyushkova; Konrad Zolna; Yusuf Aytar; Alexander Novikov 001; Scott E. Reed; Serkan Cabi; Nando de Freitas (CoRR, 2020)

Learning Deep Features in Instrumental Variable Regression. - Liyuan Xu; Yutian Chen 001; Siddarth Srinivasan; Nando de Freitas; Arnaud Doucet; Arthur Gretton (ICLR, 2021)

Active Offline Policy Selection. - Ksenia Konyushkova; Yutian Chen 001; Thomas Paine; Çaglar Gülçehre; Cosmin Paduraru; Daniel J. Mankowitz; Misha Denil; Nando de Freitas (NeurIPS, 2021)

Regularized Behavior Value Estimation. - Çaglar Gülçehre; Sergio Gómez Colmenarejo; Ziyu Wang 001; Jakub Sygnowski; Thomas Paine; Konrad Zolna; Yutian Chen 001; Matthew W. Hoffman; Razvan Pascanu; Nando de Freitas (CoRR, 2021)

Shaking the foundations: delusions in sequence models for interaction and control. - Pedro A. Ortega; Markus Kunesch; Grégoire Delétang; Tim Genewein; Jordi Grau-Moya; Joel Veness; Jonas Buchli; Jonas Degrave; Bilal Piot; Julien Pérolat; Tom Everitt; Corentin Tallec; et al. (CoRR, 2021)

Towards Learning Universal Hyperparameter Optimizers with Transformers. - Yutian Chen 001; Xingyou Song; Chansoo Lee; Zi Wang; Richard Zhang 003; David Dohan; Kazuya Kawakami; Greg Kochanski; Arnaud Doucet; Marc'Aurelio Ranzato; Sagi Perel; Nando de Freitas (NeurIPS, 2022)

Competition-Level Code Generation with AlphaCode. - Yujia Li 001; David H. Choi; Junyoung Chung; Nate Kushman; Julian Schrittwieser; Rémi Leblond; Tom Eccles; James Keeling; Felix Gimeno; Agustin Dal Lago; Thomas Hubert; Peter Choy; et al. (CoRR, 2022)

Multi-step Planning for Automated Hyperparameter Optimization with OptFormer. - Lucio M. Dery; Abram L. Friesen; Nando de Freitas; Marc'Aurelio Ranzato; Yutian Chen 001 (CoRR, 2022)

On Instrumental Variable Regression for Deep Offline Policy Evaluation. - Yutian Chen 001; Liyuan Xu; Çaglar Gülçehre; Tom Le Paine; Arthur Gretton; Nando de Freitas; Arnaud Doucet (J. Mach. Learn. Res., 2022)

Restoring and attributing ancient texts using deep neural networks. - Yannis M. Assael; Thea Sommerschield; Brendan Shillingford; Mahyar Bordbar; John Pavlopoulos; Marita Chatzipanagiotou; Ion Androutsopoulos; Jonathan Prag; Nando de Freitas (Nat., 2022)

A Generalist Agent. - Scott E. Reed; Konrad Zolna; Emilio Parisotto; Sergio Gómez Colmenarejo; Alexander Novikov 001; Gabriel Barth-Maron; Mai Gimenez; Yury Sulsky; Jackie Kay; Jost Tobias Springenberg; Tom Eccles; Jake Bruce; et al. (Trans. Mach. Learn. Res., 2022)

Vision-Language Models as Success Detectors. - Yuqing Du; Ksenia Konyushkova; Misha Denil; Akhil Raju; Jessica Landon; Felix Hill; Nando de Freitas; Serkan Cabi (CoLLAs, 2023)

Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning. - Patrick Emedom-Nnamdi; Abram L. Friesen; Bobak Shahriari; Nando de Freitas; Matthew W. Hoffman (CoRR, 2023)

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning. - Michaël Mathieu; Sherjil Ozair; Srivatsan Srinivasan; Çaglar Gülçehre; Shangtong Zhang; Ray Jiang; Tom Le Paine; Richard Powell; Konrad Zolna; Julian Schrittwieser; David H. Choi; Petko Georgiev; et al. (CoRR, 2023)

Reinforced Self-Training (ReST) for Language Modeling. - Çaglar Gülçehre; Tom Le Paine; Srivatsan Srinivasan; Ksenia Konyushkova; Lotte Weerts; Abhishek Sharma 015; Aditya Siddhant; Alex Ahern; Miaosen Wang; Chenjie Gu; Wolfgang Macherey; Arnaud Doucet; et al. (CoRR, 2023)

Machine Learning for Ancient Languages: A Survey. - Thea Sommerschield; Yannis M. Assael; John Pavlopoulos; Vanessa Stefanak; Andrew W. Senior; Chris Dyer; John Bodel; Jonathan Prag; Ion Androutsopoulos; Nando de Freitas (Comput. Linguistics, 2023)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. - Soham De; Samuel L. Smith; Anushan Fernando; Aleksandar Botev; George-Cristian Muraru; Albert Gu; Ruba Haroun; Leonard Berrada; Yutian Chen 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (CoRR, 2024)

Imagen 3. - Jason Baldridge; Jakob Bauer; Mukul Bhutani; Nicole Brichtova; Andrew Bunner; Kelvin Chan; Yichang Chen; Sander Dieleman; Yuqing Du; Zach Eaton-Rosen; Hongliang Fei; Nando de Freitas; et al. (CoRR, 2024)