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- affiliation: Google DeepMind, London, UK
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2020 – today
- 2024
[c103]Jake Bruce, Michael D. 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 C. Y. 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:
Genie: Generative Interactive Environments. ICML 2024- [i89]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:
Genie: Generative Interactive Environments. CoRR abs/2402.15391 (2024) - [i88]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, Caglar Gulcehre
:
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR abs/2402.19427 (2024) - [i87]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, Yilin Gao, Evgeny Gladchenko, Sergio Gómez Colmenarejo, Mandy Guo, Alex Haig, Will Hawkins, Hexiang Hu, Huilian Huang, Tobenna Peter Igwe, Christos Kaplanis, Siavash Khodadadeh, Yelin Kim, Ksenia Konyushkova, Karol Langner, Eric Lau, Shixin Luo, Sona Mokrá, Henna Nandwani, Yasumasa Onoe, Aäron van den Oord, Zarana Parekh, Jordi Pont-Tuset, Hang Qi, Rui Qian, Deepak Ramachandran, Poorva Rane, Abdullah Rashwan, Ali Razavi, Robert Riachi, Hansa Srinivasan, Srivatsan Srinivasan, Robin Strudel, Benigno Uria, Oliver Wang, Su Wang, Austin Waters, Chris Wolff, Auriel Wright, Zhisheng Xiao, Hao Xiong, Keyang Xu, Marc van Zee, Junlin Zhang, Katie Zhang, Wenlei Zhou, Konrad Zolna, Ola Aboubakar, Canfer Akbulut, Oscar Akerlund, Isabela Albuquerque, Nina Anderson, Marco Andreetto, Lora Aroyo, Ben Bariach, David Barker, Sherry Ben, Dana Berman, Courtney Biles, Irina Blok, Pankil Botadra, Jenny Brennan, Karla Brown, John Buckley, Rudy Bunel, Elie Bursztein, Christina Butterfield, Ben Caine, Viral Carpenter, Norman Casagrande, Ming-Wei Chang, Solomon Chang, Shamik Chaudhuri, Tony Chen, John Choi, Dmitry Churbanau, Nathan Clement, Matan Cohen, Forrester Cole, Mikhail Dektiarev, Vincent Du, Praneet Dutta, Tom Eccles, Ndidi Elue, Ashley Feden, Shlomi Fruchter, Frankie Garcia, Roopal Garg:
Imagen 3. CoRR abs/2408.07009 (2024) - 2023
- [j15]Thea Sommerschield, Yannis M. Assael, John Pavlopoulos, Vanessa Stefanak, Andrew W. Senior, Chris Dyer, John Bodel, Jonathan Prag, Ion Androutsopoulos, Nando de Freitas:
Machine Learning for Ancient Languages: A Survey. Comput. Linguistics 49(3): 703-747 (2023) - [c102]Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi:
Vision-Language Models as Success Detectors. CoLLAs 2023: 120-136 - [i86]Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi:
Vision-Language Models as Success Detectors. CoRR abs/2303.07280 (2023) - [i85]Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas, Matthew W. Hoffman:
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning. CoRR abs/2305.03870 (2023) - [i84]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:
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning. CoRR abs/2308.03526 (2023) - [i83]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:
Reinforced Self-Training (ReST) for Language Modeling. CoRR abs/2308.08998 (2023) - 2022
- [j14]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. J. Mach. Learn. Res. 23: 302:1-302:40 (2022) - [j13]Yannis M. Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag, Nando de Freitas:
Restoring and attributing ancient texts using deep neural networks. Nat. 603(7900): 280-283 (2022) - [j12]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:
A Generalist Agent. Trans. Mach. Learn. Res. 2022 (2022) - [c101]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:
Towards Learning Universal Hyperparameter Optimizers with Transformers. NeurIPS 2022 - [i82]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:
Competition-Level Code Generation with AlphaCode. CoRR abs/2203.07814 (2022) - [i81]Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez 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:
A Generalist Agent. CoRR abs/2205.06175 (2022) - [i80]Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. CoRR abs/2205.13320 (2022) - [i79]Lucio M. Dery, Abram L. Friesen, Nando de Freitas, Marc'Aurelio Ranzato, Yutian Chen:
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer. CoRR abs/2210.04971 (2022) - 2021
- [c100]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. ICLR 2021 - [c99]Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas:
Active Offline Policy Selection. NeurIPS 2021: 24631-24644 - [i78]Çaglar Gülçehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew W. Hoffman, Razvan Pascanu, Nando de Freitas:
Regularized Behavior Value Estimation. CoRR abs/2103.09575 (2021) - [i77]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR abs/2105.10148 (2021) - [i76]Ksenia Konyushkova, Yutian Chen, Thomas Paine, Çaglar Gülçehre, Cosmin Paduraru, Daniel J. Mankowitz, Misha Denil, Nando de Freitas:
Active Offline Policy Selection. CoRR abs/2106.10251 (2021) - [i75]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:
Shaking the foundations: delusions in sequence models for interaction and control. CoRR abs/2110.10819 (2021) - 2020
- [c98]Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gómez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang:
Task-Relevant Adversarial Imitation Learning. CoRL 2020: 247-263 - [c97]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:
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. ICLR 2020 - [c96]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:
Critic Regularized Regression. NeurIPS 2020 - [c95]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. NeurIPS 2020 - [c94]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:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS 2020 - [c93]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:
Scaling data-driven robotics with reward sketching and batch reinforcement learning. Robotics: Science and Systems 2020 - [i74]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:
Acme: A Research Framework for Distributed Reinforcement Learning. CoRR abs/2006.00979 (2020) - [i73]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:
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR abs/2006.13888 (2020) - [i72]Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Y. Siegel, Josh Merel, Çaglar Gülçehre, Nicolas Heess, Nando de Freitas:
Critic Regularized Regression. CoRR abs/2006.15134 (2020) - [i71]Tom Le Paine, Cosmin Paduraru, Andrea Michi, Çaglar Gülçehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas:
Hyperparameter Selection for Offline Reinforcement Learning. CoRR abs/2007.09055 (2020) - [i70]Thomas Pierrot, Nicolas Perrin, Feryal M. P. Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas:
Learning Compositional Neural Programs for Continuous Control. CoRR abs/2007.13363 (2020) - [i69]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. CoRR abs/2010.07154 (2020) - [i68]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:
Large-scale multilingual audio visual dubbing. CoRR abs/2011.03530 (2020) - [i67]Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Çaglar Gülçehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott E. Reed:
Offline Learning from Demonstrations and Unlabeled Experience. CoRR abs/2011.13885 (2020) - [i66]Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott E. Reed, Serkan Cabi, Nando de Freitas:
Semi-supervised reward learning for offline reinforcement learning. CoRR abs/2012.06899 (2020)
2010 – 2019
- 2019
- [c92]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:
Sample Efficient Adaptive Text-to-Speech. ICLR (Poster) 2019 - [c91]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:
Hyperbolic Attention Networks. ICLR (Poster) 2019 - [c90]Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas:
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. ICML 2019: 3040-3049 - [c89]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:
Large-Scale Visual Speech Recognition. INTERSPEECH 2019: 4135-4139 - [c88]Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas:
Learning Compositional Neural Programs with Recursive Tree Search and Planning. NeurIPS 2019: 14646-14656 - [i65]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i64]Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas:
Learning Compositional Neural Programs with Recursive Tree Search and Planning. CoRR abs/1905.12941 (2019) - [i63]Tom Le Paine, Çaglar Gülçehre, 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:
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. CoRR abs/1909.01387 (2019) - [i62]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas:
Modular Meta-Learning with Shrinkage. CoRR abs/1909.05557 (2019) - [i61]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:
A Framework for Data-Driven Robotics. CoRR abs/1909.12200 (2019) - [i60]Konrad Zolna, Scott E. Reed, Alexander Novikov, Sergio Gomez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang:
Task-Relevant Adversarial Imitation Learning. CoRR abs/1910.01077 (2019) - 2018
- [c87]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. ICLR (Poster) 2018 - [c86]Edward Choi, Angeliki Lazaridou, Nando de Freitas:
Compositional Obverter Communication Learning from Raw Visual Input. ICLR (Poster) 2018 - [c85]Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas:
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. ICLR (Poster) 2018 - [c84]Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas:
Playing hard exploration games by watching YouTube. NeurIPS 2018: 2935-2945 - [c83]Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess:
Reinforcement and Imitation Learning for Diverse Visuomotor Skills. Robotics: Science and Systems 2018 - [i59]Yuke Zhu, Ziyu Wang, Josh Merel, Andrei A. Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess:
Reinforcement and Imitation Learning for Diverse Visuomotor Skills. CoRR abs/1802.09564 (2018) - [i58]Edward Choi, Angeliki Lazaridou, Nando de Freitas:
Compositional Obverter Communication Learning From Raw Visual Input. CoRR abs/1804.02341 (2018) - [i57]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. CoRR abs/1804.06318 (2018) - [i56]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:
Hyperbolic Attention Networks. CoRR abs/1805.09786 (2018) - [i55]Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas:
Playing hard exploration games by watching YouTube. CoRR abs/1805.11592 (2018) - [i54]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:
Large-Scale Visual Speech Recognition. CoRR abs/1807.05162 (2018) - [i53]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:
Sample Efficient Adaptive Text-to-Speech. CoRR abs/1809.10460 (2018) - [i52]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:
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL. CoRR abs/1810.05017 (2018) - [i51]Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas:
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL. CoRR abs/1810.08647 (2018) - [i50]Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas:
Bayesian Optimization in AlphaGo. CoRR abs/1812.06855 (2018) - 2017
- [c82]Serkan Cabi, Sergio Gomez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas:
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. CoRL 2017: 207-216 - [c81]Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas:
Sample Efficient Actor-Critic with Experience Replay. ICLR (Poster) 2017 - [c80]Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas:
Learning to Perform Physics Experiments via Deep Reinforcement Learning. ICLR (Poster) 2017 - [c79]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas:
Learning to Learn without Gradient Descent by Gradient Descent. ICML 2017: 748-756 - [c78]Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando de Freitas:
Parallel Multiscale Autoregressive Density Estimation. ICML 2017: 2912-2921 - [c77]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. ICML 2017: 3751-3760 - [c76]Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels:
Cortical microcircuits as gated-recurrent neural networks. NIPS 2017: 272-283 - [c75]Ziyu Wang, Josh Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess:
Robust Imitation of Diverse Behaviors. NIPS 2017: 5320-5329 - [i49]Scott E. Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas:
Parallel Multiscale Autoregressive Density Estimation. CoRR abs/1703.03664 (2017) - [i48]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. CoRR abs/1703.04813 (2017) - [i47]Misha Denil, Sergio Gomez Colmenarejo, Serkan Cabi, David Saxton, Nando de Freitas:
Programmable Agents. CoRR abs/1706.06383 (2017) - [i46]Ziyu Wang, Josh Merel, Scott E. Reed, Greg Wayne, Nando de Freitas, Nicolas Heess:
Robust Imitation of Diverse Behaviors. CoRR abs/1707.02747 (2017) - [i45]Serkan Cabi, Sergio Gomez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas:
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. CoRR abs/1707.03300 (2017) - [i44]Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Jimenez Rezende, Oriol Vinyals, Nando de Freitas:
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. CoRR abs/1710.10304 (2017) - [i43]Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels:
Cortical microcircuits as gated-recurrent neural networks. CoRR abs/1711.02448 (2017) - [i42]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2016
- [j11]Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas:
Bayesian Optimization in a Billion Dimensions via Random Embeddings. J. Artif. Intell. Res. 55: 361-387 (2016) - [j10]Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. J. Mach. Learn. Res. 17: 10:1-10:29 (2016) - [j9]Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas:
Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104(1): 148-175 (2016) - [c74]Bobak Shahriari, Alexandre Bouchard-Côté, Nando de Freitas:
Unbounded Bayesian Optimization via Regularization. AISTATS 2016: 1168-1176 - [c73]Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas:
Dueling Network Architectures for Deep Reinforcement Learning. ICML 2016: 1995-2003 - [c72]Nando de Freitas:
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality. KDD 2016: 3 - [c71]Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning. NIPS 2016: 2137-2145 - [c70]Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas:
Learning to learn by gradient descent by gradient descent. NIPS 2016: 3981-3989 - [c69]Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas:
ACDC: A Structured Efficient Linear Layer. ICLR (Poster) 2016 - [c68]Scott E. Reed, Nando de Freitas:
Neural Programmer-Interpreters. ICLR 2016 - [i41]Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson:
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks. CoRR abs/1602.02672 (2016) - [i40]Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson:
Learning to Communicate with Deep Multi-Agent Reinforcement Learning. CoRR abs/1605.06676 (2016) - [i39]Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas:
Learning to learn by gradient descent by gradient descent. CoRR abs/1606.04474 (2016) - [i38]Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas:
Sample Efficient Actor-Critic with Experience Replay. CoRR abs/1611.01224 (2016) - [i37]Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas:
LipNet: Sentence-level Lipreading. CoRR abs/1611.01599 (2016) - [i36]Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas:
Learning to Perform Physics Experiments via Deep Reinforcement Learning. CoRR abs/1611.01843 (2016) - [i35]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas:
Learning to Learn for Global Optimization of Black Box Functions. CoRR abs/1611.03824 (2016) - 2015
- [c67]Miroslav Bogdanovic, Dejan Markovikj, Misha Denil, Nando de Freitas:
Deep Apprenticeship Learning for Playing Video Games. AAAI Workshop: Learning for General Competency in Video Games 2015 - [c66]Aurore Lyon, Ana Mincholé, Rina Ariga, Pablo Laguna, Stefan Neubauer, Hugh Watkins, Nando de Freitas, Blanca Rodríguez:
Extraction of Morphological QRS-based Biomarkers in Hypertrophic Cardiomyopathy for Risk Stratification Using L1 Regularized Logistic Regression. CinC 2015: 9-12 - [c65]Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, Ziyu Wang:
Deep Fried Convnets. ICCV 2015: 1476-1483 - [c64]Dimitrios Kotzias, Misha Denil, Nando de Freitas, Padhraic Smyth:
From Group to Individual Labels Using Deep Features. KDD 2015: 597-606 - [i34]Ziyu Wang, Nando de Freitas, Marc Lanctot:
Dueling Network Architectures for Deep Reinforcement Learning. CoRR abs/1511.06581 (2015) - 2014
- [c63]Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas:
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. AISTATS 2014: 365-374 - [c62]Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas:
Bayesian Multi-Scale Optimistic Optimization. AISTATS 2014: 1005-1014 - [c61]Yariv Dror Mizrahi, Misha Denil, Nando de Freitas:
Linear and Parallel Learning of Markov Random Fields. ICML 2014: 199-207 - [c60]Misha Denil, David Matheson, Nando de Freitas:
Narrowing the Gap: Random Forests In Theory and In Practice. ICML 2014: 665-673 - [c59]Yariv Dror Mizrahi, Misha Denil, Nando de Freitas:
Distributed Parameter Estimation in Probabilistic Graphical Models. NIPS 2014: 1700-1708 - [c58]Julieta Martinez, James J. Little, Nando de Freitas:
Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search. WACV 2014: 588-595 - [i33]Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas:
Bayesian Multi-Scale Optimistic Optimization. CoRR abs/1402.7005 (2014) - [i32]Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann:
A Deep Architecture for Semantic Parsing. CoRR abs/1404.7296 (2014) - [i31]Misha Denil, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, Nando de Freitas:
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network. CoRR abs/1406.3830 (2014) - [i30]Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas:
An Entropy Search Portfolio for Bayesian Optimization. CoRR abs/1406.4625 (2014) - [i29]Ziyu Wang, Nando de Freitas:
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters. CoRR abs/1406.7758 (2014) - [i28]John-Alexander M. Assael, Ziyu Wang, Nando de Freitas:
Heteroscedastic Treed Bayesian Optimisation. CoRR abs/1410.7172 (2014) - [i27]Dimitrios Kotzias, Misha Denil, Phil Blunsom, Nando de Freitas:
Deep Multi-Instance Transfer Learning. CoRR abs/1411.3128 (2014) - [i26]Misha Denil, Alban Demiraj, Nando de Freitas:
Extraction of Salient Sentences from Labelled Documents. CoRR abs/1412.6815 (2014) - [i25]Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, Ziyu Wang:
Deep Fried Convnets. CoRR abs/1412.7149 (2014) - 2013
- [j8]Firas Hamze, Ziyu Wang, Nando de Freitas:
Self-Avoiding Random Dynamics on Integer Complex Systems. ACM Trans. Model. Comput. Simul. 23(1): 9:1-9:25 (2013) - [c57]Misha Denil, David Matheson, Nando de Freitas:
Consistency of Online Random Forests. ICML (3) 2013: 1256-1264 - [c56]Ziyu Wang, Shakir Mohamed, Nando de Freitas:
Adaptive Hamiltonian and Riemann Manifold Monte Carlo. ICML (3) 2013: 1462-1470 - [c55]Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas:
Bayesian Optimization in High Dimensions via Random Embeddings. IJCAI 2013: 1778-1784 - [c54]Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas:
Predicting Parameters in Deep Learning. NIPS 2013: 2148-2156 - [c53]Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. ICLR 2013 - [i24]Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando de Freitas:
Bayesian Optimization in a Billion Dimensions via Random Embeddings. CoRR abs/1301.1942 (2013) - [i23]Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Michael I. Jordan, Stuart Russell:
Variational MCMC. CoRR abs/1301.2266 (2013) - [i22]Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Reversible Jump MCMC Simulated Annealing for Neural Networks. CoRR abs/1301.3833 (2013) - [i21]Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. CoRR abs/1301.3853 (2013) - [i20]Nando de Freitas, Kevin P. Murphy:
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012). CoRR abs/1301.4604 (2013) - [i19]Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas:
Best arm identification via Bayesian gap-based exploration. CoRR abs/1303.6746 (2013) - [i18]Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas:
Predicting Parameters in Deep Learning. CoRR abs/1306.0543 (2013) - [i17]Yariv Dror Mizrahi, Nando de Freitas, Luis Tenorio:
Efficient Learning of Practical Markov Random Fields with Exact Inference. CoRR abs/1308.6342 (2013) - [i16]Misha Denil, David Matheson, Nando de Freitas:
Narrowing the Gap: Random Forests In Theory and In Practice. CoRR abs/1310.1415 (2013) - 2012
- [j7]Misha Denil, Loris Bazzani, Hugo Larochelle, Nando de Freitas:
Learning Where to Attend with Deep Architectures for Image Tracking. Neural Comput. 24(8): 2151-2184 (2012) - [c52]Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas:
Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. AAAI 2012: 349-355 - [c51]Byron Knoll, Nando de Freitas:
A Machine Learning Perspective on Predictive Coding with PAQ8. DCC 2012: 377-386 - [c50]Nando de Freitas, Alexander J. Smola, Masrour Zoghi:
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. ICML 2012 - [c49]David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas:
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. AISTATS 2012: 173-181 - [c48]Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas:
Adaptive MCMC with Bayesian Optimization. AISTATS 2012: 751-760 - [e2]Nando de Freitas, Kevin P. Murphy:
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, August 14-18, 2012. AUAI Press 2012 [contents] - [i15]Benjamin M. Marlin, Nando de Freitas:
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. CoRR abs/1202.3746 (2012) - [i14]Nando de Freitas, Alexander J. Smola, Masrour Zoghi:
Regret Bounds for Deterministic Gaussian Process Bandits. CoRR abs/1203.2177 (2012) - [i13]Mohamed Osama Ahmed, Pouyan T. Bibalan, Nando de Freitas, Simon Fauvel:
Decentralized, Adaptive, Look-Ahead Particle Filtering. CoRR abs/1203.2394 (2012) - [i12]Firas Hamze, Nando de Freitas:
Intracluster Moves for Constrained Discrete-Space MCMC. CoRR abs/1203.3484 (2012) - [i11]Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet:
New inference strategies for solving Markov Decision Processes using reversible jump MCMC. CoRR abs/1205.2643 (2012) - [i10]Firas Hamze, Nando de Freitas:
Large-Flip Importance Sampling. CoRR abs/1206.5239 (2012) - [i9]Peter Carbonetto, Jacek Kisynski, Nando de Freitas, David Poole:
Nonparametric Bayesian Logic. CoRR abs/1207.1375 (2012) - [i8]Hendrik Kück, Nando de Freitas:
Learning about individuals from group statistics. CoRR abs/1207.1393 (2012) - [i7]Mike Klaas, Nando de Freitas, Arnaud Doucet:
Toward Practical N2 Monte Carlo: the Marginal Particle Filter. CoRR abs/1207.1396 (2012) - [i6]Firas Hamze, Nando de Freitas:
From Fields to Trees. CoRR abs/1207.4149 (2012) - [i5]Misha Denil, Nando de Freitas:
Recklessly Approximate Sparse Coding. CoRR abs/1208.0959 (2012) - 2011
- [c47]Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting:
Learning attentional policies for tracking and recognition in video with deep networks. ICML 2011: 937-944 - [c46]Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas:
On Autoencoders and Score Matching for Energy Based Models. ICML 2011: 1201-1208 - [c45]Matthew Hoffman, Eric Brochu, Nando de Freitas:
Portfolio Allocation for Bayesian Optimization. UAI 2011: 327-336 - [c44]Benjamin M. Marlin, Nando de Freitas:
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. UAI 2011: 497-505 - [i4]Byron Knoll, Nando de Freitas:
A Machine Learning Perspective on Predictive Coding with PAQ. CoRR abs/1108.3298 (2011) - [i3]Misha Denil, Loris Bazzani, Hugo Larochelle, Nando de Freitas:
Learning where to Attend with Deep Architectures for Image Tracking. CoRR abs/1109.3737 (2011) - 2010
- [c43]Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas:
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. ITA 2010: 80-89 - [c42]Eric Brochu, Tyson Brochu, Nando de Freitas:
A Bayesian Interactive Optimization Approach to Procedural Animation Design. Symposium on Computer Animation 2010: 103-112 - [c41]Firas Hamze, Nando de Freitas:
Intracluster Moves for Constrained Discrete-Space MCMC. UAI 2010: 236-243 - [c40]Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas:
Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS 2010: 509-516 - [i2]Eric Brochu, Matthew W. Hoffman, Nando de Freitas:
Portfolio Allocation for Bayesian Optimization. CoRR abs/1009.5419 (2010) - [i1]Eric Brochu, Vlad M. Cora, Nando de Freitas:
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. CoRR abs/1012.2599 (2010)
2000 – 2009
- 2009
- [j6]Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, José A. Castellanos, Arnaud Doucet:
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Auton. Robots 27(2): 93-103 (2009) - [c39]Hendrik Kück, Matt Hoffman, Arnaud Doucet, Nando de Freitas:
Inference and Learning for Active Sensing, Experimental Design and Control. IbPRIA 2009: 1-10 - [c38]Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet:
New inference strategies for solving Markov Decision Processes using reversible jump MCMC. UAI 2009: 223-231 - [c37]Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. AISTATS 2009: 232-239 - 2008
- [j5]Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas:
Learning to Recognize Objects with Little Supervision. Int. J. Comput. Vis. 77(1-3): 219-237 (2008) - [c36]Julia Vogel, Nando de Freitas:
Target-directed attention: Sequential decision-making for gaze planning. ICRA 2008: 2372-2379 - [c35]Peter Carbonetto, Mark Schmidt, Nando de Freitas:
An interior-point stochastic approximation method and an L1-regularized delta rule. NIPS 2008: 233-240 - 2007
- [c34]Ruben Martinez-Cantin, Nando de Freitas, José A. Castellanos:
Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM. ICRA 2007: 2415-2420 - [c33]Eric Brochu, Nando de Freitas, Abhijeet Ghosh:
Active Preference Learning with Discrete Choice Data. NIPS 2007: 409-416 - [c32]Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
Bayesian Policy Learning with Trans-Dimensional MCMC. NIPS 2007: 665-672 - [c31]Ruben Martinez-Cantin, Nando de Freitas, Arnaud Doucet, José A. Castellanos:
Active Policy Learning for Robot Planning and Exploration under Uncertainty. Robotics: Science and Systems 2007 - [c30]Eric Brochu, Abhijeet Ghosh, Nando de Freitas:
Preference galleries for material design. SIGGRAPH Posters 2007: 105 - [c29]Firas Hamze, Nando de Freitas:
Large-Flip Importance Sampling. UAI 2007: 167-174 - 2006
- [c28]Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas:
A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues. Toward Category-Level Object Recognition 2006: 277-300 - [c27]Yizheng Cai, Nando de Freitas, James J. Little:
Robust Visual Tracking for Multiple Targets. ECCV (4) 2006: 107-118 - [c26]Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang:
Fast particle smoothing: if I had a million particles. ICML 2006: 481-488 - [c25]Peter Carbonetto, Nando de Freitas:
Conditional mean field. NIPS 2006: 201-208 - 2005
- [c24]Mike Klaas, Dustin Lang, Nando de Freitas:
Fast maximum a-posteriori inference on Monte Carlo state spaces. AISTATS 2005: 158-165 - [c23]Maryam Mahdaviani, Nando de Freitas, Bob Fraser, Firas Hamze:
Fast Computational Methods for Visually Guided Robots. ICRA 2005: 138-143 - [c22]Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang:
Fast Krylov Methods for N-Body Learning. NIPS 2005: 251-258 - [c21]Firas Hamze, Nando de Freitas:
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. NIPS 2005: 491-498 - [c20]Peter Carbonetto, Jacek Kisynski, Nando de Freitas, David Poole:
Nonparametric Bayesian Logic. UAI 2005: 85-93 - [c19]Mike Klaas, Nando de Freitas, Arnaud Doucet:
Toward Practical N2 Monte Carlo: the Marginal Particle Filter. UAI 2005: 308-315 - [c18]Nando de Freitas, Hendrik Kück:
Learning about Individuals from Group Statistics. UAI 2005: 332-339 - 2004
- [j4]Nando de Freitas, Richard Dearden, Frank Hutter, Rubén Morales-Menéndez, Jim Mutch, David Poole:
Diagnosis by a waiter and a Mars explorer. Proc. IEEE 92(3): 455-468 (2004) - [c17]Hendrik Kück, Peter Carbonetto, Nando de Freitas:
A Constrained Semi-supervised Learning Approach to Data Association. ECCV (3) 2004: 1-12 - [c16]Kenji Okuma, Ali Taleghani, Nando de Freitas, James J. Little, David G. Lowe:
A Boosted Particle Filter: Multitarget Detection and Tracking. ECCV (1) 2004: 28-39 - [c15]Peter Carbonetto, Nando de Freitas, Kobus Barnard:
A Statistical Model for General Contextual Object Recognition. ECCV (1) 2004: 350-362 - [c14]Dustin Lang, Nando de Freitas:
Beat Tracking the Graphical Model Way. NIPS 2004: 745-752 - [c13]Firas Hamze, Nando de Freitas:
From Fields to Trees. UAI 2004: 243-250 - 2003
- [j3]Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan:
Matching Words and Pictures. J. Mach. Learn. Res. 3: 1107-1135 (2003) - [j2]Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan:
An Introduction to MCMC for Machine Learning. Mach. Learn. 50(1-2): 5-43 (2003) - [c12]Eric Brochu, Nando de Freitas, Kejie Bao:
The Sound of an Album Cover: A Probabilistic Approach to Multimedia. AISTATS 2003: 49-56 - [c11]Paul Gustafson, Peter Carbonetto, Natalie Thompson, Nando de Freitas:
Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition. AISTATS 2003: 124-131 - [c10]Pinar Muyan, Nando de Freitas:
A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference. AISTATS 2003: 217-224 - [c9]Rubén Morales-Menéndez, Nando de Freitas, David Poole:
Estimation and control of industrial processes with particle filters. ACC 2003: 579-584 - 2002
- [c8]Rubén Morales-Menéndez, Nando de Freitas, David Poole:
Real-Time Monitoring of Complex Industrial Processes with Particle Filters. NIPS 2002: 1433-1440 - [c7]Eric Brochu, Nando de Freitas:
"Name That Song!" A Probabilistic Approach to Querying on Music and Text. NIPS 2002: 1505-1512 - 2001
- [j1]Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Robust Full Bayesian Learning for Radial Basis Networks. Neural Comput. 13(10): 2359-2407 (2001) - [c6]Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Rao-Blackwellised Particle Filtering via Data Augmentation. NIPS 2001: 561-567 - [c5]Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Stuart Russell:
Variational MCMC. UAI 2001: 120-127 - [p2]Arnaud Doucet, Nando de Freitas, Neil J. Gordon:
An Introduction to Sequential Monte Carlo Methods. Sequential Monte Carlo Methods in Practice 2001: 3-14 - [p1]Nando de Freitas, Christophe Andrieu, Pedro A. d. F. R. Højen-Sørensen, M. Niranjan, A. Gee:
Sequential Monte Carlo Methods for Neural Networks. Sequential Monte Carlo Methods in Practice 2001: 359-379 - [e1]Arnaud Doucet, Nando de Freitas, Neil J. Gordon:
Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science, Springer 2001, ISBN 978-1-4419-2887-0 [contents] - 2000
- [c4]Christophe Andrieu, Nando de Freitas:
Sequential Monte Carlo for model selection and estimation of neural networks. ICASSP 2000: 3410-3413 - [c3]Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan:
The Unscented Particle Filter. NIPS 2000: 584-590 - [c2]Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Reversible Jump MCMC Simulated Annealing for Neural Networks. UAI 2000: 11-18 - [c1]Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. UAI 2000: 176-183
Coauthor Index
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