Nando de Freitas

According to our database, Nando de Freitas authored at least 163 papers between 2000 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Çaglar Gülçehre

CoRR, 2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

CoRR, 2024

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, September, 2023

Reinforced Self-Training (ReST) for Language Modeling.

Çaglar Gülçehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas

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, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

CoRR, 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

Vision-Language Models as Success Detectors.

Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

A Generalist Agent.

Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Trans. 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

On Instrumental Variable Regression for Deep Offline Policy Evaluation.

Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

J. Mach. Learn. Res., 2022

Multi-step Planning for Automated Hyperparameter Optimization with OptFormer.

Lucio M. Dery, Abram L. Friesen, Nando de Freitas, Marc'Aurelio Ranzato, Yutian Chen

CoRR, 2022

Competition-Level Code Generation with AlphaCode.

Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, Oriol Vinyals

CoRR, 2022

Towards Learning Universal Hyperparameter Optimizers with Transformers.

Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg

CoRR, 2021

Regularized Behavior Value Estimation.

Çaglar Gülçehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew W. Hoffman, Razvan Pascanu, Nando de Freitas

CoRR, 2021

Active Offline Policy Selection.

Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Deep Features in Instrumental Variable Regression.

Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Semi-supervised reward learning for offline reinforcement learning.

Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott E. Reed, Serkan Cabi, Nando de Freitas

CoRR, 2020

Offline Learning from Demonstrations and Unlabeled Experience.

Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Çaglar Gülçehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott E. Reed

CoRR, 2020

Large-scale multilingual audio visual dubbing.

Yi Yang, Brendan Shillingford, Yannis M. Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, Nando de Freitas

CoRR, 2020

Learning Compositional Neural Programs for Continuous Control.

Thomas Pierrot, Nicolas Perrin, Feryal M. P. Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas

CoRR, 2020

Hyperparameter Selection for Offline Reinforcement Learning.

Tom Le Paine, Cosmin Paduraru, Andrea Michi, Çaglar Gülçehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas

CoRR, 2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, 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, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the Robotics: Science and Systems XVI, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Modular Meta-Learning with Shrinkage.

Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Critic Regularized Regression.

Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team

Proceedings of the 8th International Conference on Learning Representations, 2020

Task-Relevant Adversarial Imitation Learning.

Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gómez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang

Proceedings of the 4th Conference on Robot Learning, 2020

2019

A Framework for Data-Driven Robotics.

Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott E. Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerík, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

CoRR, 2019

Meta-learning of Sequential Strategies.

Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg

CoRR, 2019

Learning Compositional Neural Programs with Recursive Tree Search and Planning.

Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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, Ben Coppin, Ben Laurie, Andrew W. Senior, Nando de Freitas

Proceedings of the Interspeech 2019, 2019

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Proceedings of the 36th International Conference on Machine Learning, 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

Proceedings of the 7th International Conference on Learning Representations, 2019

Sample Efficient Adaptive Text-to-Speech.

Yutian Chen, 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, Oriol Vinyals, Nando de Freitas

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Bayesian Optimization in AlphaGo.

Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas

CoRR, 2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL.

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

CoRR, 2018

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.

Tom Le Paine, Sergio Gomez Colmenarejo, Ziyu Wang, Scott E. Reed, Yusuf Aytar, Tobias Pfaff, Matthew W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

CoRR, 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, Ben Laurie, Andrew W. Senior, Nando de Freitas

CoRR, 2018

Reinforcement and Imitation Learning for Diverse Visuomotor Skills.

Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess

Proceedings of the Robotics: Science and Systems XIV, 2018

Playing hard exploration games by watching YouTube.

Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions.

Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas

Proceedings of the 6th International Conference on Learning Representations, 2018

Compositional Obverter Communication Learning from Raw Visual Input.

Edward Choi, Angeliki Lazaridou, Nando de Freitas

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

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, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

Parallel Multiscale Autoregressive Density Estimation.

Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas

CoRR, 2017

Programmable Agents.

Misha Denil, Sergio Gomez Colmenarejo, Serkan Cabi, David Saxton, Nando de Freitas

CoRR, 2017

Cortical microcircuits as gated-recurrent neural networks.

Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust Imitation of Diverse Behaviors.

Ziyu Wang, Josh Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 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

Proceedings of the 34th International Conference on Machine Learning, 2017

Parallel Multiscale Autoregressive Density Estimation.

Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando de Freitas

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Perform Physics Experiments via Deep Reinforcement Learning.

Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas

Proceedings of the 5th International Conference on Learning Representations, 2017

Sample Efficient Actor-Critic with Experience Replay.

Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas

Proceedings of the 5th International Conference on Learning Representations, 2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously.

Serkan Cabi, Sergio Gomez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016

Taking the Human Out of the Loop: A Review of Bayesian Optimization.

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas

Proc. IEEE, 2016

Herded Gibbs Sampling.

Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling

J. Mach. Learn. Res., 2016

Bayesian Optimization in a Billion Dimensions via Random Embeddings.

Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas

J. Artif. Intell. Res., 2016

Neural Programmer-Interpreters.

Scott E. Reed, Nando de Freitas

Proceedings of the 4th International Conference on Learning Representations, 2016

ACDC: A Structured Efficient Linear Layer.

Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas

Proceedings of the 4th International Conference on Learning Representations, 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

Learning to Learn for Global Optimization of Black Box Functions.

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas

CoRR, 2016

LipNet: Sentence-level Lipreading.

Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas

CoRR, 2016

Learning to Communicate with Deep Multi-Agent Reinforcement Learning.

Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality.

Nando de Freitas

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Dueling Network Architectures for Deep Reinforcement Learning.

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

Proceedings of the 33nd International Conference on Machine Learning, 2016

Unbounded Bayesian Optimization via Regularization.

Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Dueling Network Architectures for Deep Reinforcement Learning.

Ziyu Wang, Nando de Freitas, Marc Lanctot

CoRR, 2015

From Group to Individual Labels Using Deep Features.

Dimitrios Kotzias, Misha Denil, Nando de Freitas, Padhraic Smyth

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Deep Fried Convnets.

Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, Ziyu Wang

Proceedings of the 2015 IEEE International Conference on Computer Vision, 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

Proceedings of the Computing in Cardiology, 2015

Deep Apprenticeship Learning for Playing Video Games.

Miroslav Bogdanovic, Dejan Markovikj, Misha Denil, Nando de Freitas

Proceedings of the Learning for General Competency in Video Games, 2015

2014

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters.

Ziyu Wang, Nando de Freitas

CoRR, 2014

An Entropy Search Portfolio for Bayesian Optimization.

Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas

CoRR, 2014

Deep Multi-Instance Transfer Learning.

Dimitrios Kotzias, Misha Denil, 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

Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network.

Misha Denil, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, Nando de Freitas

CoRR, 2014

Extraction of Salient Sentences from Labelled Documents.

Misha Denil, Alban Demiraj, Nando de Freitas

CoRR, 2014

Heteroscedastic Treed Bayesian Optimisation.

John-Alexander M. Assael, Ziyu Wang, Nando de Freitas

CoRR, 2014

Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search.

Julieta Martinez, James J. Little, Nando de Freitas

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2014

Distributed Parameter Estimation in Probabilistic Graphical Models.

Yariv Dror Mizrahi, Misha Denil, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Linear and Parallel Learning of Markov Random Fields.

Yariv Dror Mizrahi, Misha Denil, Nando de Freitas

Proceedings of the 31th International Conference on Machine Learning, 2014

Narrowing the Gap: Random Forests In Theory and In Practice.

Misha Denil, David Matheson, Nando de Freitas

Proceedings of the 31th International Conference on Machine Learning, 2014

Bayesian Multi-Scale Optimistic Optimization.

Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning.

Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Self-Avoiding Random Dynamics on Integer Complex Systems.

Firas Hamze, Ziyu Wang, Nando de Freitas

ACM Trans. Model. Comput. Simul., 2013

Best arm identification via Bayesian gap-based exploration

Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

CoRR, 2013

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012)

Nando de Freitas, Kevin P. Murphy

CoRR, 2013

Variational MCMC

Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart Russell

CoRR, 2013

Efficient Learning of Practical Markov Random Fields with Exact Inference.

Yariv Dror Mizrahi, Nando de Freitas, Luis Tenorio

CoRR, 2013

Predicting Parameters in Deep Learning.

Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas

Proceedings of the 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

Bayesian Optimization in High Dimensions via Random Embeddings.

Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas

Proceedings of the IJCAI 2013, 2013

Adaptive Hamiltonian and Riemann Manifold Monte Carlo.

Ziyu Wang, Shakir Mohamed, Nando de Freitas

Proceedings of the 30th International Conference on Machine Learning, 2013

Consistency of Online Random Forests.

Misha Denil, David Matheson, Nando de Freitas

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Learning Where to Attend with Deep Architectures for Image Tracking.

Misha Denil, Loris Bazzani, Hugo Larochelle, Nando de Freitas

Neural Comput., 2012

Adaptive MCMC with Bayesian Optimization.

Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.

David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Recklessly Approximate Sparse Coding

Misha Denil, Nando de Freitas

CoRR, 2012

Decentralized, Adaptive, Look-Ahead Particle Filtering

Mohamed Osama Ahmed, Pouyan T. Bibalan, Nando de Freitas, Simon Fauvel

CoRR, 2012

Regret Bounds for Deterministic Gaussian Process Bandits

Nando de Freitas, Alexander J. Smola, Masrour Zoghi

CoRR, 2012

Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations.

Nando de Freitas, Alexander J. Smola, Masrour Zoghi

Proceedings of the 29th International Conference on Machine Learning, 2012

A Machine Learning Perspective on Predictive Coding with PAQ8.

Byron Knoll, Nando de Freitas

Proceedings of the 2012 Data Compression Conference, Snowbird, UT, USA, April 10-12, 2012, 2012

Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults.

Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

A Machine Learning Perspective on Predictive Coding with PAQ

Byron Knoll, Nando de Freitas

CoRR, 2011

Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood.

Benjamin M. Marlin, Nando de Freitas

Proceedings of the UAI 2011, 2011

Portfolio Allocation for Bayesian Optimization.

Matthew Hoffman, Eric Brochu, Nando de Freitas

Proceedings of the UAI 2011, 2011

On Autoencoders and Score Matching for Energy Based Models.

Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas

Proceedings of the 28th International Conference on Machine Learning, 2011

Learning attentional policies for tracking and recognition in video with deep networks.

Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Inductive Principles for Restricted Boltzmann Machine Learning.

Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 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

Intracluster Moves for Constrained Discrete-Space MCMC.

Firas Hamze, Nando de Freitas

Proceedings of the UAI 2010, 2010

A Bayesian Interactive Optimization Approach to Procedural Animation Design.

Eric Brochu, Tyson Brochu, Nando de Freitas

Proceedings of the 2010 Eurographics/ACM SIGGRAPH Symposium on Computer Animation, 2010

A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets.

Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas

Proceedings of the Information Theory and Applications Workshop, 2010

2009

An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward.

Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 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, Arnaud Doucet

Auton. Robots, 2009

New inference strategies for solving Markov Decision Processes using reversible jump MCMC.

Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet

Proceedings of the UAI 2009, 2009

Inference and Learning for Active Sensing, Experimental Design and Control.

Hendrik Kück, Matt Hoffman, Arnaud Doucet, Nando de Freitas

Proceedings of the Pattern Recognition and Image Analysis, 4th Iberian Conference, 2009

2008

Learning to Recognize Objects with Little Supervision.

Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas

Int. J. Comput. Vis., 2008

An interior-point stochastic approximation method and an L1-regularized delta rule.

Peter Carbonetto, Mark Schmidt, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Target-directed attention: Sequential decision-making for gaze planning.

Julia Vogel, Nando de Freitas

Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008

2007

Large-Flip Importance Sampling.

Firas Hamze, Nando de Freitas

Proceedings of the UAI 2007, 2007

Preference galleries for material design.

Eric Brochu, Abhijeet Ghosh, Nando de Freitas

Proceedings of the International Conference on Computer Graphics and Interactive Techniques, 2007

Active Policy Learning for Robot Planning and Exploration under Uncertainty.

Ruben Martinez-Cantin, Nando de Freitas, Arnaud Doucet, José A. Castellanos

Proceedings of the Robotics: Science and Systems III, 2007

Bayesian Policy Learning with Trans-Dimensional MCMC.

Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Active Preference Learning with Discrete Choice Data.

Eric Brochu, Nando de Freitas, Abhijeet Ghosh

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

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

Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007

2006

Conditional mean field.

Peter Carbonetto, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Fast particle smoothing: if I had a million particles.

Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang

Proceedings of the Machine Learning, 2006

Robust Visual Tracking for Multiple Targets.

Yizheng Cai, Nando de Freitas, James J. Little

Proceedings of the Computer Vision, 2006

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

Proceedings of the Toward Category-Level Object Recognition, 2006

2005

Toward Practical N2 Monte Carlo: the Marginal Particle Filter.

Mike Klaas, Nando de Freitas, Arnaud Doucet

Proceedings of the UAI '05, 2005

Learning about Individuals from Group Statistics.

Nando de Freitas, Hendrik Kück

Proceedings of the UAI '05, 2005

Nonparametric Bayesian Logic.

Peter Carbonetto, Jacek Kisynski, Nando de Freitas, David Poole

Proceedings of the UAI '05, 2005

Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs.

Firas Hamze, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Fast Krylov Methods for N-Body Learning.

Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Fast Computational Methods for Visually Guided Robots.

Maryam Mahdaviani, Nando de Freitas, Bob Fraser, Firas Hamze

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

Fast maximum a-posteriori inference on Monte Carlo state spaces.

Mike Klaas, Dustin Lang, Nando de Freitas

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Diagnosis by a waiter and a Mars explorer.

Nando de Freitas, Richard Dearden, Frank Hutter, Rubén Morales-Menéndez, Jim Mutch, David Poole

Proc. IEEE, 2004

From Fields to Trees.

Firas Hamze, Nando de Freitas

Proceedings of the UAI '04, 2004

Beat Tracking the Graphical Model Way.

Dustin Lang, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

A Boosted Particle Filter: Multitarget Detection and Tracking.

Kenji Okuma, Ali Taleghani, Nando de Freitas, James J. Little, David G. Lowe

Proceedings of the Computer Vision, 2004

A Constrained Semi-supervised Learning Approach to Data Association.

Hendrik Kück, Peter Carbonetto, Nando de Freitas

Proceedings of the Computer Vision, 2004

A Statistical Model for General Contextual Object Recognition.

Peter Carbonetto, Nando de Freitas, Kobus Barnard

Proceedings of the Computer Vision, 2004

2003

An Introduction to MCMC for Machine Learning.

Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan

Mach. Learn., 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

Estimation and control of industrial processes with particle filters.

Rubén Morales-Menéndez, Nando de Freitas, David Poole

Proceedings of the American Control Conference, 2003

A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference.

Pinar Muyan, Nando de Freitas

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition.

Paul Gustafson, Peter Carbonetto, Natalie Thompson, Nando de Freitas

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

The Sound of an Album Cover: A Probabilistic Approach to Multimedia.

Eric Brochu, Nando de Freitas, Kejie Bao

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

Real-Time Monitoring of Complex Industrial Processes with Particle Filters.

Rubén Morales-Menéndez, Nando de Freitas, David Poole

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

"Name That Song!" A Probabilistic Approach to Querying on Music and Text.

Eric Brochu, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Robust Full Bayesian Learning for Radial Basis Networks.

Christophe Andrieu, Nando de Freitas, Arnaud Doucet

Neural Comput., 2001

Variational MCMC.

Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Stuart Russell

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Rao-Blackwellised Particle Filtering via Data Augmentation.

Christophe Andrieu, Nando de Freitas, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Sequential Monte Carlo Methods for Neural Networks.

Nando de Freitas, Christophe Andrieu, Pedro A. d. F. R. Højen-Sørensen, M. Niranjan, A. Gee

Proceedings of the Sequential Monte Carlo Methods in Practice, 2001

An Introduction to Sequential Monte Carlo Methods.

Arnaud Doucet, Nando de Freitas, Neil J. Gordon

Proceedings of the Sequential Monte Carlo Methods in Practice, 2001

2000

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.

Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Reversible Jump MCMC Simulated Annealing for Neural Networks.

Christophe Andrieu, Nando de Freitas, Arnaud Doucet

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

The Unscented Particle Filter.

Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Sequential Monte Carlo for model selection and estimation of neural networks.

Christophe Andrieu, Nando de Freitas

Proceedings of the IEEE International Conference on Acoustics, 2000