Yee Whye Teh - RankMe

336 papers A* 74A 30B 2Journal 168Unranked 57

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Are We Evaluating the Edit Locality of LLM Model Editing Properly? CoRR Wei Liu, Haomei Xu, Hongkai Liu, Zhiying Deng, Ruixuan Li, Heng Huang, Yee Whye Teh, Wee Sun Lee
2026	J	`jnl`	Manifold Aware Denoising Score Matching (MAD). CoRR Alona Levy-Jurgenson, Alvaro Prat, James Cuin, Yee Whye Teh
2026	J	`jnl`	Meta Flow Maps enable scalable reward alignment. CoRR Peter Potaptchik, Adhi Saravanan, Abbas Mammadov, Alvaro Prat, Michael S. Albergo, Yee Whye Teh
2026	J	`jnl`	SkillCraft: Can LLM Agents Learn to Use Tools Skillfully? CoRR Shiqi Chen, Jingze Gai, Ruochen Zhou, Jinghan Zhang, Tongyao Zhu, Junlong Li, Kangrui Wang, Zihan Wang, Zhengyu Chen, Klara Kaleb, Ning Miao, Siyang Gao, Cong Lu, Manling Li, Junxian He, Yee Whye Teh
2026	J	`jnl`	Variational Flow Maps: Make Some Noise for One-Step Conditional Generation. CoRR Abbas Mammadov, So Takao, Bohan Chen, Ricardo Baptista, Morteza Mardani, Yee Whye Teh, Julius Berner
2025	J	`jnl`	Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey. CoRR Qiyuan Liu, Hao Xu, Xuhong Chen, Wei Chen, Yee Whye Teh, Ning Miao
2025	J	`jnl`	Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs. CoRR T. Duy Nguyen-Hien, Desi R. Ivanova, Yee Whye Teh, Wee Sun Lee
2025	J	`jnl`	Foundation of Intelligence: Review of Math Word Problems from Human Cognition Perspective. CoRR Zhenya Huang, Jiayu Liu, Xin Lin, Zhiyuan Ma, Shangzi Xue, Tong Xiao, Qi Liu, Yee Whye Teh, Enhong Chen
2025	J	`jnl`	GEM: A Gym for Agentic LLMs. CoRR Zichen Liu, Anya Sims, Keyu Duan, Changyu Chen, Simon Yu, Xiangxin Zhou, Haotian Xu, Shaopan Xiong, Bo Liu, Chenmien Tan, Chuen Yang Beh, Weixun Wang, Hao Zhu, Weiyan Shi, Diyi Yang, Michael Shieh, Yee Whye Teh, Wee Sun Lee, Min Lin
2025	J	`jnl`	Internalizing World Models via Self-Play Finetuning for Agentic RL. CoRR Shiqi Chen, Tongyao Zhu, Zian Wang, Jinghan Zhang, Kangrui Wang, Siyang Gao, Teng Xiao, Yee Whye Teh, Junxian He, Manling Li
2025	J	`jnl`	Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation. CoRR Wei Liu, Haomei Xu, Bingqing Liu, Zhiying Deng, Haozhao Wang, Jun Wang, Ruixuan Li, Yee Whye Teh, Wee Sun Lee
2025	A*	`conf`	L3Ms - Lagrange Large Language Models. ICLR Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola
2025	A*	`conf`	Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents. ICLR Dongjun Lee, Juyong Lee, Kyuyoung Kim, Jihoon Tack, Jinwoo Shin, Yee Whye Teh, Kimin Lee
2025	J	`jnl`	Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents. CoRR Dongjun Lee, Juyong Lee, Kyuyoung Kim, Jihoon Tack, Jinwoo Shin, Yee Whye Teh, Kimin Lee
2025	J	`jnl`	MARCOS: Deep Thinking by Markov Chain of Continuous Thoughts. CoRR Jiayu Liu, Zhenya Huang, Anya Sims, Enhong Chen, Yee Whye Teh, Ning Miao
2025	J	`jnl`	NoProp: Training Neural Networks without Back-propagation or Forward-propagation. CoRR Qinyu Li, Yee Whye Teh, Razvan Pascanu
2025	J	`jnl`	Rao-Blackwellised Reparameterisation Gradients. CoRR Kevin Lam, Thang Bui, George Deligiannidis, Yee Whye Teh
2025	J	`jnl`	SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion. CoRR Alvaro Prat, Leo Zhang, Charlotte M. Deane, Yee Whye Teh, Garrett M. Morris
2025	J	`jnl`	StochasTok: Improving Fine-Grained Subword Understanding in LLMs. CoRR Anya Sims, Thom Foster, Klara Kaleb, Tuan-Duy H. Nguyen, Joseph Lee, Jakob N. Foerster, Yee Whye Teh, Cong Lu
2025	A*	`conf`	SymDiff: Equivariant Diffusion via Stochastic Symmetrisation. ICLR Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh, Rob Cornish
2024	A*	`conf`	Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. ICML Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2024	J	`jnl`	Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design. CoRR Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2024	A*	`conf`	EvIL: Evolution Strategies for Generalisable Imitation Learning. ICML Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster
2024	J	`jnl`	EvIL: Evolution Strategies for Generalisable Imitation Learning. CoRR Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster
2024	J	`jnl`	Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR 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
2024	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2024	A*	`conf`	Kalman Filter for Online Classification of Non-Stationary Data. ICLR Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
2024	J	`jnl`	L3Ms - Lagrange Large Language Models. CoRR Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola
2024	J	`jnl`	Learning Loss Landscapes in Preference Optimization. CoRR Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster, Patrick Rebeschini, Yee Whye Teh
2024	A*	`conf`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. NeurIPS Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. CoRR Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	A*	`conf`	Online Adaptation of Language Models with a Memory of Amortized Contexts. NeurIPS Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz
2024	J	`jnl`	Online Adaptation of Language Models with a Memory of Amortized Contexts. CoRR Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	J	`jnl`	Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation. CoRR Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa
2024	J	`jnl`	RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz Gustavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
2024	J	`jnl`	Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
2024	A*	`conf`	SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. ICLR Ning Miao, Yee Whye Teh, Tom Rainforth
2024	J	`jnl`	SymDiff: Equivariant Diffusion via Stochastic Symmetrisation. CoRR Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh, Rob Cornish
2024	A*	`conf`	The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. NeurIPS Anya Sims, Cong Lu, Jakob N. Foerster, Yee Whye Teh
2024	J	`jnl`	The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. CoRR Anya Sims, Cong Lu, Yee Whye Teh
2024	A*	`conf`	Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts. ICML Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
2024	J	`jnl`	Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts. CoRR Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
2023	J	`jnl`	Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. Trans. Mach. Learn. Res. Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
2023	J	`jnl`	Continual Learning via Sequential Function-Space Variational Inference. CoRR Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2023	—	`conf`	Continually learning representations at scale. CoLLAs Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu
2023	A*	`conf`	Deep Stochastic Processes via Functional Markov Transition Operators. NeurIPS Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh
2023	J	`jnl`	Deep Stochastic Processes via Functional Markov Transition Operators. CoRR Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth, Yee Whye Teh
2023	A*	`conf`	Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. ICLR Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
2023	J	`jnl`	Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. CoRR Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh
2023	A*	`conf`	Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. ICML Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2023	J	`jnl`	Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. CoRR Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2023	A*	`conf`	Geometric Neural Diffusion Processes. NeurIPS Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	J	`jnl`	Geometric Neural Diffusion Processes. CoRR Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner
2023	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2023	J	`jnl`	Kalman Filter for Online Classification of Non-Stationary Data. CoRR Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
2023	A*	`conf`	Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
2023	A*	`conf`	Modality-Agnostic Variational Compression of Implicit Neural Representations. ICML Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin
2023	J	`jnl`	Modality-Agnostic Variational Compression of Implicit Neural Representations. CoRR Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin
2023	J	`jnl`	Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. J. Mach. Learn. Res. Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuan Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato
2023	A*	`conf`	Pre-training via Denoising for Molecular Property Prediction. ICLR Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
2023	J	`jnl`	SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. CoRR Ning Miao, Yee Whye Teh, Tom Rainforth
2023	A*	`conf`	Synthetic Experience Replay. NeurIPS Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder
2023	J	`jnl`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. CoRR Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2023	J	`jnl`	UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography. Trans. Mach. Learn. Res. Francisca Vasconcelos, Bobby He, Nalini M. Singh, Yee Whye Teh
2022	A	`conf`	Amortized Rejection Sampling in Universal Probabilistic Programming. AISTATS Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood
2022	—	`conf`	Bayesian Nonparametrics for Sparse Dynamic Networks. ECML/PKDD (5) Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
2022	J	`jnl`	Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
2022	J	`jnl`	COIN++: Data Agnostic Neural Compression. CoRR Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet
2022	J	`jnl`	COIN++: Neural Compression Across Modalities. Trans. Mach. Learn. Res. Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet
2022	J	`jnl`	Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. CoRR Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
2022	A*	`conf`	Conformal Off-Policy Prediction in Contextual Bandits. NeurIPS Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet
2022	J	`jnl`	Conformal Off-Policy Prediction in Contextual Bandits. CoRR Muhammad Faaiz Taufiq, Jean-Francois Ton, Robert Cornish, Yee Whye Teh, Arnaud Doucet
2022	A*	`conf`	Continual Learning via Sequential Function-Space Variational Inference. ICML Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal
2022	A	`conf`	Generative Models as Distributions of Functions. AISTATS Emilien Dupont, Yee Whye Teh, Arnaud Doucet
2022	J	`jnl`	Learning Instance-Specific Data Augmentations. CoRR Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
2022	J	`jnl`	Meta-Learning Sparse Compression Networks. CoRR Jonathan Richard Schwarz, Yee Whye Teh
2022	J	`jnl`	Meta-Learning Sparse Compression Networks. Trans. Mach. Learn. Res. Jonathan Schwarz, Yee Whye Teh
2022	J	`jnl`	NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. CoRR Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato
2022	A*	`conf`	On Incorporating Inductive Biases into VAEs. ICLR Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
2022	J	`jnl`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. CoRR Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2022	J	`jnl`	Pre-training via Denoising for Molecular Property Prediction. CoRR Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
2022	J	`jnl`	Riemannian Diffusion Schrödinger Bridge. CoRR James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet
2022	J	`jnl`	Riemannian Score-Based Generative Modeling. CoRR Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
2022	A*	`conf`	Riemannian Score-Based Generative Modelling. NeurIPS Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
2022	A*	`conf`	Tractable Function-Space Variational Inference in Bayesian Neural Networks. NeurIPS Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh, Yarin Gal
2022	J	`jnl`	UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography. CoRR Francisca Vasconcelos, Bobby He, Nalini M. Singh, Yee Whye Teh
2022	—	`conf`	When Does Re-initialization Work? ICBINB Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
2022	J	`jnl`	When Does Re-initialization Work? CoRR Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu
2021	J	`jnl`	An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions. J. Comput. Graph. Stat. Qi Wang, Vinayak Rao, Yee Whye Teh
2021	A*	`conf`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	COIN: COmpression with Implicit Neural representations. CoRR Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet
2021	J	`jnl`	Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet
2021	A*	`conf`	Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes. ICML Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh
2021	J	`jnl`	Generative Models as Distributions of Functions. CoRR Emilien Dupont, Yee Whye Teh, Arnaud Doucet
2021	A*	`conf`	Group Equivariant Subsampling. NeurIPS Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh
2021	J	`jnl`	Group Equivariant Subsampling. CoRR Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh
2021	J	`jnl`	InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
2021	A*	`conf`	LieTransformer: Equivariant Self-Attention for Lie Groups. ICML Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim
2021	A*	`conf`	Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh
2021	A	`conf`	Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. AISTATS Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
2021	A*	`conf`	On Contrastive Representations of Stochastic Processes. NeurIPS Emile Mathieu, Adam Foster, Yee Whye Teh
2021	J	`jnl`	On Contrastive Representations of Stochastic Processes. CoRR Emile Mathieu, Adam Foster, Yee Whye Teh
2021	A*	`conf`	On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. NeurIPS Tim G. J. Rudner, Cong Lu, Michael A. Osborne, Yarin Gal, Yee Whye Teh
2021	A*	`conf`	Powerpropagation: A sparsity inducing weight reparameterisation. NeurIPS Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
2021	J	`jnl`	Powerpropagation: A sparsity inducing weight reparameterisation. CoRR Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
2021	A*	`conf`	Robust Pruning at Initialization. ICLR Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh
2021	J	`jnl`	Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. CoRR Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
2021	A*	`conf`	Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. NeurIPS Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
2020	A	`conf`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2020	J	`jnl`	Attentive Clustering Processes. CoRR Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski
2020	A*	`conf`	Bayesian Deep Ensembles via the Neural Tangent Kernel. NeurIPS Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
2020	J	`jnl`	Bayesian Deep Ensembles via the Neural Tangent Kernel. CoRR Bobby He, Balaji Lakshminarayanan, Yee Whye Teh
2020	J	`jnl`	Behavior Priors for Efficient Reinforcement Learning. CoRR Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
2020	J	`jnl`	Bootstrapping Neural Processes. CoRR Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
2020	A*	`conf`	Bootstrapping neural processes. NeurIPS Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
2020	A*	`conf`	Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. ICML Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
2020	J	`jnl`	Equivariant Conditional Neural Processes. CoRR Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh
2020	A*	`conf`	Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. ICML Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban
2020	J	`jnl`	Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise. CoRR Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gürbüzbalaban
2020	A*	`conf`	Functional Regularisation for Continual Learning with Gaussian Processes. ICLR Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
2020	A*	`conf`	How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
2020	J	`jnl`	Importance Weighted Policy Learning and Adaption. CoRR Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess
2020	J	`jnl`	LieTransformer: Equivariant self-attention for Lie Groups. CoRR Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim
2020	J	`jnl`	Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning. CoRR Bryn Elesedy, Varun Kanade, Yee Whye Teh
2020	A*	`conf`	MetaFun: Meta-Learning with Iterative Functional Updates. ICML Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh
2020	A*	`conf`	Multiplicative Interactions and Where to Find Them. ICLR Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack W. Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2020	J	`jnl`	Neural Ensemble Search for Performant and Calibrated Predictions. CoRR Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, Yee Whye Teh
2020	A	`conf`	Non-exchangeable feature allocation models with sublinear growth of the feature sizes. AISTATS Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh
2020	J	`jnl`	Non-exchangeable feature allocation models with sublinear growth of the feature sizes. CoRR Giuseppe Di Benedetto, François Caron, Yee Whye Teh
2020	J	`jnl`	On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. CoRR Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal
2020	J	`jnl`	Probabilistic Symmetries and Invariant Neural Networks. J. Mach. Learn. Res. Benjamin Bloem-Reddy, Yee Whye Teh
2020	J	`jnl`	Pruning untrained neural networks: Principles and Analysis. CoRR Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh
2020	J	`jnl`	Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. CoRR Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020	A*	`conf`	Uncertainty Estimation Using a Single Deep Deterministic Neural Network. ICML Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2019	—	`conf`	A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2019	J	`jnl`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2019	J	`jnl`	Amortized Rejection Sampling in Universal Probabilistic Programming. CoRR Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Günes Baydin, Bradley Gram-Hansen, Christian Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood
2019	—	`conf`	Attentive Neural Processes. ICLR (Poster) Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
2019	J	`jnl`	Attentive Neural Processes. CoRR Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, S. M. Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
2019	A*	`conf`	Augmented Neural ODEs. NeurIPS Emilien Dupont, Arnaud Doucet, Yee Whye Teh
2019	J	`jnl`	Augmented Neural ODEs. CoRR Emilien Dupont, Arnaud Doucet, Yee Whye Teh
2019	A*	`conf`	Continual Unsupervised Representation Learning. NeurIPS Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell
2019	J	`jnl`	Continual Unsupervised Representation Learning. CoRR Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu, Raia Hadsell
2019	A*	`conf`	Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
2019	J	`jnl`	Deep Amortized Clustering. CoRR Juho Lee, Yoonho Lee, Yee Whye Teh
2019	J	`jnl`	Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality. CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Balaji Lakshminarayanan
2019	A*	`conf`	Disentangling Disentanglement in Variational Autoencoders. ICML Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
2019	J	`jnl`	Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. CoRR Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
2019	—	`conf`	Do Deep Generative Models Know What They Don't Know? ICLR (Poster) Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	J	`jnl`	Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. CoRR Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess
2019	J	`jnl`	Functional Regularisation for Continual Learning using Gaussian Processes. CoRR Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
2019	J	`jnl`	Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
2019	J	`jnl`	Hijacking Malaria Simulators with Probabilistic Programming. CoRR Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atilim Günes Baydin
2019	A*	`conf`	Hybrid Models with Deep and Invertible Features. ICML Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	J	`jnl`	Hybrid Models with Deep and Invertible Features. CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2019	—	`conf`	Information asymmetry in KL-regularized RL. ICLR (Poster) Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
2019	J	`jnl`	Information asymmetry in KL-regularized RL. CoRR Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
2019	J	`jnl`	Meta reinforcement learning as task inference. CoRR Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess
2019	J	`jnl`	Meta-Learning surrogate models for sequential decision making. CoRR Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR 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
2019	J	`jnl`	MetaFun: Meta-Learning with Iterative Functional Updates. CoRR Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh
2019	—	`conf`	Neural Probabilistic Motor Primitives for Humanoid Control. ICLR (Poster) Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
2019	J	`jnl`	Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. CoRR Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
2019	J	`jnl`	Probabilistic symmetry and invariant neural networks. CoRR Benjamin Bloem-Reddy, Yee Whye Teh
2019	A*	`conf`	Random Tessellation Forests. NeurIPS Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd T. Elliott
2019	J	`jnl`	Random Tessellation Forests. CoRR Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd T. Elliott
2019	A	`conf`	Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. UAI Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
2019	A*	`conf`	Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. ICML Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh
2019	A*	`conf`	Stacked Capsule Autoencoders. NeurIPS Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton
2019	J	`jnl`	Stacked Capsule Autoencoders. CoRR Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton
2019	J	`jnl`	Task Agnostic Continual Learning via Meta Learning. CoRR Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu
2019	A*	`conf`	Variational Bayesian Optimal Experimental Design. NeurIPS Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2019	J	`jnl`	Variational Estimators for Bayesian Optimal Experimental Design. CoRR Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2018	J	`jnl`	A Statistical Approach to Assessing Neural Network Robustness. CoRR Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2018	A	`conf`	An Analysis of Categorical Distributional Reinforcement Learning. AISTATS Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
2018	A*	`conf`	Causal Inference via Kernel Deviance Measures. NeurIPS Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2018	J	`jnl`	Causal Inference via Kernel Deviance Measures. CoRR Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2018	A*	`conf`	Conditional Neural Processes. ICML Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami
2018	J	`jnl`	Conditional Neural Processes. CoRR Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018	J	`jnl`	Controllable Semantic Image Inpainting. CoRR Jin Xu, Yee Whye Teh
2018	J	`jnl`	Disentangling Disentanglement. CoRR Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh
2018	J	`jnl`	Do Deep Generative Models Know What They Don't Know? CoRR Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan
2018	A*	`conf`	Faithful Inversion of Generative Models for Effective Amortized Inference. NeurIPS Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood
2018	J	`jnl`	Hamiltonian Descent Methods. CoRR Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet
2018	A*	`conf`	Mix & Match Agent Curricula for Reinforcement Learning. ICML Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu
2018	J	`jnl`	Mix&Match - Agent Curricula for Reinforcement Learning. CoRR Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu
2018	A*	`conf`	Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data. NeurIPS Xenia Miscouridou, Francois Caron, Yee Whye Teh
2018	J	`jnl`	Neural Processes. CoRR Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh
2018	J	`jnl`	Neural probabilistic motor primitives for humanoid control. CoRR Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
2018	A*	`conf`	On Big Data Learning for Small Data Problems. KDD Yee Whye Teh
2018	J	`jnl`	On Exploration, Exploitation and Learning in Adaptive Importance Sampling. CoRR Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh
2018	A*	`conf`	Progress & Compress: A scalable framework for continual learning. ICML Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell
2018	J	`jnl`	Progress & Compress: A scalable framework for continual learning. CoRR Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell
2018	J	`jnl`	Revisiting Reweighted Wake-Sleep. CoRR Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
2018	A	`conf`	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. UAI Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
2018	J	`jnl`	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. CoRR Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
2018	A	`conf`	Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. AISTATS Hyunjik Kim, Yee Whye Teh
2018	A*	`conf`	Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. NeurIPS Adam R. Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner
2018	J	`jnl`	Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. CoRR Adam R. Kosiorek, Hyunjik Kim, Ingmar Posner, Yee Whye Teh
2018	J	`jnl`	Set Transformer. CoRR Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh
2018	A*	`conf`	Stochastic Expectation Maximization with Variance Reduction. NeurIPS Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang
2018	A*	`conf`	Tighter Variational Bounds are Not Necessarily Better. ICML Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
2018	J	`jnl`	Tighter Variational Bounds are Not Necessarily Better. CoRR Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
2017	—	`ch.`	Bayesian Nonparametric Models. Encyclopedia of Machine Learning and Data Mining Peter Orbanz, Yee Whye Teh
2017	—	`conf`	Deep Kernel Machines via the Kernel Reparametrization Trick. ICLR (Workshop) Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2017	—	`ch.`	Dirichlet Process. Encyclopedia of Machine Learning and Data Mining Yee Whye Teh
2017	J	`jnl`	Distral: Robust Multitask Reinforcement Learning. CoRR Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
2017	—	`conf`	Distral: Robust multitask reinforcement learning. NIPS Yee Whye Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
2017	J	`jnl`	Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server. J. Mach. Learn. Res. Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh
2017	J	`jnl`	Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. CoRR Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood
2017	—	`conf`	Filtering Variational Objectives. NIPS Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	J	`jnl`	Filtering Variational Objectives. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
2017	—	`conf`	Particle Value Functions. ICLR (Workshop) Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	J	`jnl`	Particle Value Functions. CoRR Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
2017	J	`jnl`	Poisson Random Fields for Dynamic Feature Models. J. Mach. Learn. Res. Valerio Perrone, Paul A. Jenkins, Dario Spanò, Yee Whye Teh
2017	A	`conf`	Poisson intensity estimation with reproducing kernels. AISTATS Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic
2017	A*	`ed.`	Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 ICML Doina Precup, Yee Whye Teh
2017	A	`conf`	Relativistic Monte Carlo. AISTATS Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer
2017	J	`jnl`	Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. CoRR Hyunjik Kim, Yee Whye Teh
2017	—	`conf`	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR (Poster) Chris J. Maddison, Andriy Mnih, Yee Whye Teh
2016	J	`jnl`	Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. Yee Whye Teh, Alexandre H. Thiéry, Sebastian J. Vollmer
2016	A*	`conf`	DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. ICML Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2016	J	`jnl`	DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. CoRR Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2016	J	`jnl`	Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics. J. Mach. Learn. Res. Sebastian J. Vollmer, Konstantinos C. Zygalakis, Yee Whye Teh
2016	—	`conf`	Gaussian Processes for Survival Analysis. NIPS Tamara Fernandez, Nicolas Rivera, Yee Whye Teh
2016	J	`jnl`	Image Retrieval with a Bayesian Model of Relevance Feedback. CoRR Dorota Glowacka, Yee Whye Teh, John Shawe-Taylor
2016	A	`conf`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2016	—	`conf`	Scalable Structure Discovery in Regression using Gaussian Processes. AutoML@ICML Hyunjik Kim, Yee Whye Teh
2016	J	`jnl`	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR Chris J. Maddison, Andriy Mnih, Yee Whye Teh
2016	A	`conf`	The Mondrian Kernel. UAI Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
2016	J	`jnl`	Tucker Gaussian Process for Regression and Collaborative Filtering. CoRR Hyunjik Kim, Xiaoyu Lu, Seth R. Flaxman, Yee Whye Teh
2015	—	`conf`	A hybrid sampler for Poisson-Kingman mixture models. NIPS Maria Lomeli, Stefano Favaro, Yee Whye Teh
2015	J	`jnl`	Bayesian nonparametric crowdsourcing. J. Mach. Learn. Res. Pablo G. Moreno, Antonio Artés-Rodríguez, Yee Whye Teh, Fernando Pérez-Cruz
2015	J	`jnl`	Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR Yee Whye Teh, Leonard Hasenclever, Thibaut Liénart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell
2015	—	`conf`	Expectation Particle Belief Propagation. NIPS Thibaut Liénart, Yee Whye Teh, Arnaud Doucet
2015	J	`jnl`	Expectation Particle Belief Propagation. CoRR Thibaut Liénart, Yee Whye Teh, Arnaud Doucet
2015	J	`jnl`	Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
2015	J	`jnl`	Mondrian Forests for Large-Scale Regression when Uncertainty Matters. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	On a class of σ-stable Poisson-Kingman models and an effective marginalized sampler. Stat. Comput. Stefano Favaro, Maria Lomeli, Yee Whye Teh
2015	A	`conf`	Particle Gibbs for Bayesian Additive Regression Trees. AISTATS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	Particle Gibbs for Bayesian Additive Regression Trees. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2015	J	`jnl`	The Mondrian Process for Machine Learning. CoRR Matej Balog, Yee Whye Teh
2014	—	`conf`	Asynchronous Anytime Sequential Monte Carlo. NIPS Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh
2014	—	`conf`	Distributed Bayesian Posterior Sampling via Moment Sharing. NIPS Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
2014	—	`conf`	Mondrian Forests: Efficient Online Random Forests. NIPS Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2014	J	`jnl`	Mondrian Forests: Efficient Online Random Forests. CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2013	—	`conf`	Bayesian Hierarchical Community Discovery. NIPS Charles Blundell, Yee Whye Teh
2013	J	`jnl`	Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation CoRR Max Welling, Yee Whye Teh
2013	—	`conf`	Dependent Normalized Random Measures. ICML (3) Changyou Chen, Vinayak A. Rao, Wray L. Buntine, Yee Whye Teh
2013	J	`jnl`	Discovering Multiple Constraints that are Frequently Approximately Satisfied CoRR Geoffrey E. Hinton, Yee Whye Teh
2013	J	`jnl`	Fast MCMC sampling for Markov jump processes and extensions. J. Mach. Learn. Res. Vinayak A. Rao, Yee Whye Teh
2013	J	`jnl`	Inferring ground truth from multi-annotator ordinal data: a probabilistic approach CoRR Balaji Lakshminarayanan, Yee Whye Teh
2013	—	`conf`	Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space. NIPS Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
2013	—	`conf`	Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex. NIPS Sam Patterson, Yee Whye Teh
2013	J	`jnl`	Top-down particle filtering for Bayesian decision trees CoRR Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2013	—	`conf`	Top-down particle filtering for Bayesian decision trees. ICML (3) Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
2012	A*	`conf`	A fast and simple algorithm for training neural probabilistic language models. ICML Andriy Mnih, Yee Whye Teh
2012	—	`conf`	Actor-Critic Reinforcement Learning with Energy-Based Policies. EWRL Nicolas Heess, David Silver, Yee Whye Teh
2012	J	`jnl`	Bayesian Rose Trees CoRR Charles Blundell, Yee Whye Teh, Katherine A. Heller
2012	J	`jnl`	Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data CoRR Francois Caron, Yee Whye Teh, Thomas Brendan Murphy
2012	J	`jnl`	Bayesian nonparametric models for ranked data CoRR Francois Caron, Yee Whye Teh
2012	—	`conf`	Bayesian nonparametric models for ranked data. NIPS Francois Caron, Yee Whye Teh
2012	J	`jnl`	Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks CoRR Vinayak A. Rao, Yee Whye Teh
2012	J	`jnl`	Hybrid Variational/Gibbs Collapsed Inference in Topic Models CoRR Max Welling, Yee Whye Teh, Hilbert J. Kappen
2012	—	`conf`	Learning Label Trees for Probabilistic Modelling of Implicit Feedback. NIPS Andriy Mnih, Yee Whye Teh
2012	—	`conf`	MCMC for continuous-time discrete-state systems. NIPS Vinayak A. Rao, Yee Whye Teh
2012	J	`jnl`	On Smoothing and Inference for Topic Models CoRR Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
2012	—	`conf`	Scalable imputation of genetic data with a discrete fragmentation-coagulation process. NIPS Lloyd T. Elliott, Yee Whye Teh
2012	—	`conf`	Searching for objects driven by context. NIPS Bogdan Alexe, Nicolas Heess, Yee Whye Teh, Vittorio Ferrari
2012	J	`jnl`	Structured Region Graphs: Morphing EP into GBP CoRR Max Welling, Thomas P. Minka, Yee Whye Teh
2011	B	`conf`	(Invited talk) Bayesian Tools for Natural Language Learning. CoNLL Yee Whye Teh
2011	A*	`conf`	Bayesian Learning via Stochastic Gradient Langevin Dynamics. ICML Max Welling, Yee Whye Teh
2011	A	`conf`	Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks. UAI Vinayak A. Rao, Yee Whye Teh
2011	—	`conf`	Gaussian process modulated renewal processes. NIPS Vinayak A. Rao, Yee Whye Teh
2011	J	`jnl`	Learning Item Trees for Probabilistic Modelling of Implicit Feedback CoRR Andriy Mnih, Yee Whye Teh
2011	A	`conf`	Mixed Cumulative Distribution Networks. AISTATS Ricardo Bezerra de Andrade e Silva, Charles Blundell, Yee Whye Teh
2011	—	`conf`	Modelling Genetic Variations using Fragmentation-Coagulation Processes. NIPS Yee Whye Teh, Charles Blundell, Lloyd T. Elliott
2011	J	`jnl`	The sequence memoizer. Commun. ACM Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh
2010	—	`ch.`	Bayesian Nonparametric Models. Encyclopedia of Machine Learning Peter Orbanz, Yee Whye Teh
2010	A	`conf`	Bayesian Rose Trees. UAI Charles Blundell, Yee Whye Teh, Katherine A. Heller
2010	—	`ch.`	Dirichlet Process. Encyclopedia of Machine Learning Yee Whye Teh
2010	—	`conf`	Improvements to the Sequence Memoizer. NIPS Jan Gasthaus, Yee Whye Teh
2010	B	`conf`	Lossless Compression Based on the Sequence Memoizer. DCC Jan Gasthaus, Frank D. Wood, Yee Whye Teh
2010	J	`jnl`	Mixed Cumulative Distribution Networks CoRR Ricardo Bezerra de Andrade e Silva, Charles Blundell, Yee Whye Teh
2010	A	`conf`	Preface. AISTATS Yee Whye Teh, D. Mike Titterington
2010	A	`ed.`	Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010 AISTATS Yee Whye Teh, D. Mike Titterington
2009	A	`conf`	A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation. AISTATS Frank D. Wood, Yee Whye Teh
2009	A*	`conf`	A stochastic memoizer for sequence data. ICML Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
2009	—	`conf`	Hierarchical Dirichlet Trees for Information Retrieval. HLT-NAACL Gholamreza Haffari, Yee Whye Teh
2009	—	`conf`	Indian Buffet Processes with Power-law Behavior. NIPS Yee Whye Teh, Dilan Görür
2009	A	`conf`	Infinite Hierarchical Hidden Markov Models. AISTATS Katherine A. Heller, Yee Whye Teh, Dilan Görür
2009	A	`conf`	On Smoothing and Inference for Topic Models. UAI Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
2009	—	`conf`	Spatial Normalized Gamma Processes. NIPS Vinayak A. Rao, Yee Whye Teh
2009	A	`conf`	Variational Inference for the Indian Buffet Process. AISTATS Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh
2008	—	`conf`	A mixture model for the evolution of gene expression in non-homogeneous datasets. NIPS Gerald T. Quon, Yee Whye Teh, Esther T. Chan, Timothy R. Hughes, Michael Brudno, Quaid Morris
2008	—	`conf`	An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering. NIPS Dilan Görür, Yee Whye Teh
2008	A*	`conf`	Beam sampling for the infinite hidden Markov model. ICML Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani
2008	—	`conf`	Dependent Dirichlet Process Spike Sorting. NIPS Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh
2008	A	`conf`	Hybrid Variational/Gibbs Collapsed Inference in Topic Models. UAI Max Welling, Yee Whye Teh, Bert Kappen
2008	—	`conf`	The Infinite Factorial Hidden Markov Model. NIPS Jurgen Van Gael, Yee Whye Teh, Zoubin Ghahramani
2008	—	`conf`	The Mondrian Process. NIPS Daniel M. Roy, Yee Whye Teh
2007	—	`conf`	Bayesian Agglomerative Clustering with Coalescents. NIPS Yee Whye Teh, Hal Daumé III, Daniel M. Roy
2007	A*	`conf`	Collapsed Variational Dirichlet Process Mixture Models. IJCAI Kenichi Kurihara, Max Welling, Yee Whye Teh
2007	—	`conf`	Collapsed Variational Inference for HDP. NIPS Yee Whye Teh, Kenichi Kurihara, Max Welling
2007	—	`conf`	Cooled and Relaxed Survey Propagation for MRFs. NIPS Hai Leong Chieu, Wee Sun Lee, Yee Whye Teh
2007	—	`conf`	Improving Word Sense Disambiguation Using Topic Features. EMNLP-CoNLL Junfu Cai, Wee Sun Lee, Yee Whye Teh
2007	—	`conf`	NUS-ML: Improving Word Sense Disambiguation Using Topic Features. SemEval@ACL Junfu Cai, Wee Sun Lee, Yee Whye Teh
2007	A	`conf`	Stick-breaking Construction for the Indian Buffet Process. AISTATS Yee Whye Teh, Dilan Görür, Zoubin Ghahramani
2006	—	`conf`	A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. NIPS Yee Whye Teh, David Newman, Max Welling
2006	J	`jnl`	A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh
2006	A*	`conf`	A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes. ACL Yee Whye Teh
2006	A*	`conf`	Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. ICML Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh
2006	J	`jnl`	Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Cogn. Sci. Geoffrey E. Hinton, Simon Osindero, Max Welling, Yee Whye Teh
2005	A	`conf`	Semiparametric latent factor models. AISTATS Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan
2005	A	`conf`	Structured Region Graphs: Morphing EP into GBP. UAI Max Welling, Thomas P. Minka, Yee Whye Teh
2004	A*	`conf`	Approximate inference by Markov chains on union spaces. ICML Max Welling, Michal Rosen-Zvi, Yee Whye Teh
2004	J	`jnl`	Linear Response Algorithms for Approximate Inference in Graphical Models. Neural Comput. Max Welling, Yee Whye Teh
2004	—	`conf`	Making Latin Manuscripts Searchable using gHMMs. NIPS Jaety Edwards, Yee Whye Teh, David A. Forsyth, Roger Bock, Michael Maire, Grace Vesom
2004	—	`conf`	Names and Faces in the News. CVPR (2) Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik G. Learned-Miller, David A. Forsyth
2004	—	`conf`	Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei
2003	J	`jnl`	Approximate inference in Boltzmann machines. Artif. Intell. Max Welling, Yee Whye Teh
2003	—	`—`	Bethe free energy and contrastive divergence approximations for undirected graphical models. Yee Whye Teh
2003	J	`jnl`	Energy-Based Models for Sparse Overcomplete Representations. J. Mach. Learn. Res. Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton
2003	—	`conf`	Linear Response for Approximate Inference. NIPS Max Welling, Yee Whye Teh
2003	A	`conf`	On Improving the Efficiency of the Iterative Proportional Fitting Procedure. AISTATS Yee Whye Teh, Max Welling
2002	A*	`conf`	An Alternate Objective Function for Markovian Fields. ICML Sham M. Kakade, Yee Whye Teh, Sam T. Roweis
2002	—	`conf`	Automatic Alignment of Local Representations. NIPS Yee Whye Teh, Sam T. Roweis
2001	A	`conf`	Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. UAI Max Welling, Yee Whye Teh
2001	A	`conf`	Discovering Multiple Constraints that are Frequently Approximately Satisfied. UAI Geoffrey E. Hinton, Yee Whye Teh
2001	—	`conf`	The Unified Propagation and Scaling Algorithm. NIPS Yee Whye Teh, Max Welling
2000	—	`conf`	Rate-coded Restricted Boltzmann Machines for Face Recognition. NIPS Yee Whye Teh, Geoffrey E. Hinton
1999	—	`conf`	Learning to Parse Images. NIPS Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh
1998	—	`conf`	Making Forward Chaining Relevant. AIPS Fahiem Bacchus, Yee Whye Teh