José Miguel Hernández-Lobato

264 papers A* 72A 6B 1C 1Journal 155Unranked 29

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Diffusive Classification Loss for Learning Energy-based Generative Models. CoRR Ruikang Ouyang, Louis Grenioux, José Miguel Hernández-Lobato
2026	J	`jnl`	Activation-Space Uncertainty Quantification for Pretrained Networks. CoRR Richard Bergna, Stefan Depeweg, Sergio Calvo-Ordoñez, Jonathan Plenk, Álvaro Cartea, José Miguel Hernández-Lobato
2026	J	`jnl`	BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching. Trans. Mach. Learn. Res. Ruikang Ouyang, Bo Qiang, José Miguel Hernández-Lobato
2026	J	`jnl`	Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models. CoRR Jiajun He, Zongyu Guo, Zhaoyang Jia, Xiaoyi Zhang, Jiahao Li, Xiao Li, Bin Li, José Miguel Hernández-Lobato, Yan Lu
2026	J	`jnl`	Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations. CoRR Dai Shi, Lequan Lin, Andi Han, Luke Thompson, José Miguel Hernández-Lobato, Zhiyong Wang, Junbin Gao
2026	J	`jnl`	Richer Bayesian Last Layers with Subsampled NTK Features. CoRR Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna, Álvaro Cartea, Yarin Gal, José Miguel Hernández-Lobato, Kamil Ciosek
2026	J	`jnl`	Stochastic Interpolants in Hilbert Spaces. CoRR James Boran Yu, Ruikang Ouyang, Julien Horwood, José Miguel Hernández-Lobato
2026	J	`jnl`	Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations. CoRR Dai Shi, Luke Thompson, Andi Han, Peiyan Hu, Junbin Gao, José Miguel Hernández-Lobato
2025	A*	`conf`	Aligning Multimodal Representations through an Information Bottleneck. ICML Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana, Ricard Marxer, Alfonso Ortega
2025	J	`jnl`	Aligning Multimodal Representations through an Information Bottleneck. CoRR Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana, Ricard Marxer, Alfonso Ortega
2025	J	`jnl`	Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum. J. Chem. Inf. Model. Jenna C. Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato, Connor W. Coley
2025	J	`jnl`	Best practices for multi-fidelity Bayesian optimization in materials and molecular research. Nat. Comput. Sci. Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch
2025	J	`jnl`	CREPE: Controlling Diffusion with Replica Exchange. CoRR Jiajun He, Paul Jeha, Peter Potaptchik, Leo Zhang, José Miguel Hernández-Lobato, Yuanqi Du, Saifuddin Syed, Francisco Vargas
2025	J	`jnl`	Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption. CoRR Wei Chen, Ruichu Cai, Jie Qiao, Yuguang Yan, José Miguel Hernández-Lobato
2025	A*	`conf`	Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space. ICML Zixing Song, Ziqiao Meng, José Miguel Hernández-Lobato
2025	J	`jnl`	Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models. CoRR Fengzhe Zhang, Laurence I. Midgley, José Miguel Hernández-Lobato
2025	J	`jnl`	Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models. Trans. Mach. Learn. Res. Fengzhe Zhang, Laurence Illing Midgley, José Miguel Hernández-Lobato
2025	J	`jnl`	FEAT: Free energy Estimators with Adaptive Transport. CoRR Jiajun He, Yuanqi Du, Francisco Vargas, Yuanqing Wang, Carla P. Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden
2025	—	`conf`	FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency. WWW (Companion Volume) Zixing Song, Irwin King, José Miguel Hernández-Lobato
2025	J	`jnl`	Improving Iterative Gaussian Processes via Warm Starting Sequential Posteriors. CoRR Alan Yufei Dong, Jihao Andreas Lin, José Miguel Hernández-Lobato
2025	J	`jnl`	LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction. CoRR Asal Mehradfar, Mohammad Shahab Sepehri, José Miguel Hernández-Lobato, Glen S. Kwon, Mahdi Soltanolkotabi, Salman Avestimehr, Morteza Rasoulianboroujeni
2025	J	`jnl`	Long-term Causal Inference via Modeling Sequential Latent Confounding. CoRR Weilin Chen, Ruichu Cai, Yuguang Yan, Zhifeng Hao, José Miguel Hernández-Lobato
2025	J	`jnl`	Mitigating Forgetting in Low Rank Adaptation. CoRR Joanna Sliwa, Frank Schneider, Philipp Hennig, José Miguel Hernández-Lobato
2025	J	`jnl`	No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. CoRR Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, Ruikang Ouyang, Carla P. Gomes, José Miguel Hernández-Lobato
2025	J	`jnl`	Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination. CoRR Weilin Chen, Ruichu Cai, Junjie Wan, Zeqin Yang, José Miguel Hernández-Lobato
2025	J	`jnl`	Observation Noise and Initialization in Wide Neural Networks. CoRR Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna, Álvaro Cartea, José Miguel Hernández-Lobato, Konstantina Palla, Kamil Ciosek
2025	J	`jnl`	Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes. CoRR Richard Bergna, Stefan Depeweg, Sergio Calvo-Ordoñez, Jonathan Plenk, Álvaro Cartea, José Miguel Hernández-Lobato
2025	A*	`conf`	Progressive Tempering Sampler with Diffusion. ICML Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
2025	J	`jnl`	Progressive Tempering Sampler with Diffusion. CoRR Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
2025	J	`jnl`	RNE: a plug-and-play framework for diffusion density estimation and inference-time control. CoRR Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du, Francisco Vargas
2025	A*	`conf`	Scalable Gaussian Processes with Latent Kronecker Structure. ICML Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes with Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	There Was Never a Bottleneck in Concept Bottleneck Models. CoRR Antonio Almudévar, José Miguel Hernández-Lobato, Alfonso Ortega
2025	J	`jnl`	Towards Training One-Step Diffusion Models Without Distillation. CoRR Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber
2025	—	`conf`	Track and Tweak: Monitoring and Improving Group Fairness for Temporal Graph Neural Networks in Real Time. KDD (2) Zixing Song, Muzhi Li, Yifei Zhang, Irwin King, José Miguel Hernández-Lobato
2025	A	`conf`	Training Neural Samplers with Reverse Diffusive KL Divergence. AISTATS Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
2025	A*	`conf`	Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. ICLR Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka, Pietro Lio, José Miguel Hernández-Lobato
2025	J	`jnl`	Vector Quantization using Gaussian Variational Autoencoder. CoRR Tongda Xu, Wendi Zheng, Jiajun He, José Miguel Hernández-Lobato, Yan Wang, Ya-Qin Zhang, Jie Tang
2025	J	`jnl`	Weighted Conditional Flow Matching. CoRR Sergio Calvo-Ordoñez, Matthieu Meunier, Álvaro Cartea, Christoph Reisinger, Yarin Gal, José Miguel Hernández-Lobato
2024	J	`jnl`	A Deep Generative Model for the Design of Synthesizable Ionizable Lipids. CoRR Yuxuan Ou, Jingyi Zhao, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato
2024	A*	`conf`	A Generative Model of Symmetry Transformations. NeurIPS James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	A Generative Model of Symmetry Transformations. CoRR James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	A*	`conf`	Accelerating Relative Entropy Coding with Space Partitioning. NeurIPS Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
2024	J	`jnl`	Accelerating Relative Entropy Coding with Space Partitioning. CoRR Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
2024	J	`jnl`	BEnDEM:A Boltzmann Sampler Based on Bootstrapped Denoising Energy Matching. CoRR Ruikang Ouyang, Bo Qiang, José Miguel Hernández-Lobato
2024	J	`jnl`	Batched Bayesian optimization with correlated candidate uncertainties. CoRR Jenna C. Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato, Connor W. Coley
2024	J	`jnl`	Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research. CoRR Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch
2024	J	`jnl`	Diagnosing and fixing common problems in Bayesian optimization for molecule design. CoRR Austin Tripp, José Miguel Hernández-Lobato
2024	A*	`conf`	Diffusive Gibbs Sampling. ICML Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	J	`jnl`	Diffusive Gibbs Sampling. CoRR Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	J	`jnl`	Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. CoRR Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato
2024	A*	`conf`	Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. ICML Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato
2024	J	`jnl`	Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. CoRR Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato
2024	J	`jnl`	Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
2024	J	`jnl`	Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach. CoRR Jingyi Zhao, Yuxuan Ou, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato
2024	J	`jnl`	Getting Free Bits Back from Rotational Symmetries in LLMs. CoRR Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
2024	J	`jnl`	Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta
2024	J	`jnl`	Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò
2024	A*	`conf`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. NeurIPS Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. Pattern Recognit. Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina
2024	J	`jnl`	Leveraging Task Structures for Improved Identifiability in Neural Network Representations. Trans. Mach. Learn. Res. Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato
2024	A*	`conf`	On conditional diffusion models for PDE simulations. NeurIPS Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
2024	J	`jnl`	On conditional diffusion models for PDE simulations. CoRR Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu
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	A*	`conf`	RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. ICLR Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato
2024	A*	`conf`	Retro-fallback: retrosynthetic planning in an uncertain world. ICLR Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
2024	J	`jnl`	Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. Trans. Mach. Learn. Res. Elre Talea Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
2024	A*	`conf`	Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2024	A*	`conf`	Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens. ICML Ross M. Clarke, José Miguel Hernández-Lobato
2024	J	`jnl`	Training Neural Samplers with Reverse Diffusive KL Divergence. CoRR Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
2024	J	`jnl`	Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. CoRR Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka, Pietro Liò, José Miguel Hernández-Lobato
2024	J	`jnl`	Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato
2023	J	`jnl`	Adam through a Second-Order Lens. CoRR Ross M. Clarke, Baiyu Su, José Miguel Hernández-Lobato
2023	A*	`conf`	Compression with Bayesian Implicit Neural Representations. NeurIPS Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
2023	J	`jnl`	Compression with Bayesian Implicit Neural Representations. CoRR Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
2023	J	`jnl`	Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato
2023	J	`jnl`	Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin
2023	A*	`conf`	Faster Relative Entropy Coding with Greedy Rejection Coding. NeurIPS Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
2023	J	`jnl`	Faster Relative Entropy Coding with Greedy Rejection Coding. CoRR Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
2023	A*	`conf`	Flow Annealed Importance Sampling Bootstrap. ICLR Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	J	`jnl`	Genetic algorithms are strong baselines for molecule generation. CoRR Austin Tripp, José Miguel Hernández-Lobato
2023	J	`jnl`	Graph Neural Stochastic Differential Equations. CoRR Richard Bergna, Felix L. Opolka, Pietro Liò, José Miguel Hernández-Lobato
2023	J	`jnl`	Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
2023	J	`jnl`	Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. CoRR Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina
2023	J	`jnl`	Leveraging Task Structures for Improved Identifiability in Neural Network Representations. CoRR Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato
2023	A*	`conf`	Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. ICLR Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
2023	J	`jnl`	Minimal Random Code Learning with Mean-KL Parameterization. CoRR Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato
2023	J	`jnl`	Online Laplace Model Selection Revisited. CoRR Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
2023	J	`jnl`	RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. CoRR Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato
2023	J	`jnl`	Retro-fallback: retrosynthetic planning in an uncertain world. CoRR Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
2023	A*	`conf`	SE(3) Equivariant Augmented Coupling Flows. NeurIPS Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	J	`jnl`	SE(3) Equivariant Augmented Coupling Flows. CoRR Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	A*	`conf`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	A*	`conf`	Sampling-based inference for large linear models, with application to linearised Laplace. ICLR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023	J	`jnl`	Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. CoRR Elre T. Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato
2023	J	`jnl`	Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2023	A*	`conf`	Tanimoto Random Features for Scalable Molecular Machine Learning. NeurIPS Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
2023	J	`jnl`	Tanimoto Random Features for Scalable Molecular Machine Learning. CoRR Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
2023	J	`jnl`	Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2023	J	`jnl`	normflows: A PyTorch Package for Normalizing Flows. CoRR Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato
2023	J	`jnl`	normflows: A PyTorch Package for Normalizing Flows. J. Open Source Softw. Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato
2022	J	`jnl`	A Probabilistic Deep Image Prior for Computational Tomography. CoRR Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2022	A*	`conf`	Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
2022	A*	`conf`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	A*	`conf`	BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis. WWW Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen
2022	J	`jnl`	Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
2022	J	`jnl`	DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J. Chem. Inf. Model. Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado
2022	A*	`conf`	Fast Relative Entropy Coding with A* coding. ICML Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
2022	J	`jnl`	Fast Relative Entropy Coding with A* coding. CoRR Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
2022	J	`jnl`	Flow Annealed Importance Sampling Bootstrap. CoRR Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
2022	A*	`conf`	Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
2022	J	`jnl`	Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. CoRR Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
2022	A*	`conf`	Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. NeurIPS Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
2022	J	`jnl`	Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. CoRR Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
2022	A	`conf`	Resampling Base Distributions of Normalizing Flows. AISTATS Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato
2022	J	`jnl`	Sampling-based inference for large linear models, with application to linearised Laplace. CoRR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2022	A*	`conf`	Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. ICLR Ross M. Clarke, Elre Talea Oldewage, José Miguel Hernández-Lobato
2021	A*	`conf`	A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. ICML Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang
2021	J	`jnl`	Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
2021	A*	`conf`	Activation-level uncertainty in deep neural networks. ICLR Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato
2021	A*	`conf`	Active Slices for Sliced Stein Discrepancy. ICML Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	J	`jnl`	Active Slices for Sliced Stein Discrepancy. CoRR Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	—	`conf`	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	J	`jnl`	Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	A*	`conf`	Bayesian Deep Learning via Subnetwork Inference. ICML Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	J	`jnl`	Bootstrap Your Flow. CoRR Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, José Miguel Hernández-Lobato
2021	J	`jnl`	Contextual HyperNetworks for Novel Feature Adaptation. CoRR Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
2021	J	`jnl`	DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. CoRR Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado
2021	J	`jnl`	Depth Uncertainty Networks for Active Learning. CoRR Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2021	A*	`conf`	Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang
2021	A*	`conf`	Functional Variational Inference based on Stochastic Process Generators. NeurIPS Chao Ma, José Miguel Hernández-Lobato
2021	A*	`conf`	Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
2021	A*	`conf`	Improving black-box optimization in VAE latent space using decoder uncertainty. NeurIPS Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
2021	J	`jnl`	Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal
2021	J	`jnl`	Nonlinear Invariant Risk Minimization: A Causal Approach. CoRR Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
2021	A	`conf`	Predictive Complexity Priors. AISTATS Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
2021	J	`jnl`	Resampling Base Distributions of Normalizing Flows. CoRR Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato
2021	J	`jnl`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
2021	J	`jnl`	Sampling the Variational Posterior with Local Refinement. Entropy Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
2021	J	`jnl`	Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. CoRR Ross M. Clarke, Elre T. Oldewage, José Miguel Hernández-Lobato
2021	A*	`conf`	Sliced Kernelized Stein Discrepancy. ICLR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2021	A*	`conf`	Symmetry-Aware Actor-Critic for 3D Molecular Design. ICLR Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato
2020	A*	`conf`	A Generative Model for Molecular Distance Geometry. ICML Gregor N. C. Simm, José Miguel Hernández-Lobato
2020	A	`conf`	A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs. ISLPED Kshitij Bhardwaj, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel Hernández-Lobato, Gu-Yeon Wei
2020	A*	`conf`	Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	J	`jnl`	Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	J	`jnl`	Combining deep generative and discriminative models for Bayesian semi-supervised learning. Pattern Recognit. Jonathan Gordon, José Miguel Hernández-Lobato
2020	A*	`conf`	Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. NeurIPS Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
2020	J	`jnl`	Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. CoRR Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
2020	J	`jnl`	DRIFT: Deep Reinforcement Learning for Functional Software Testing. CoRR Luke Harries, Rebekah Storan Clarke, Timothy Chapman, Swamy V. P. L. N. Nallamalli, Levent Özgür, Shuktika Jain, Alex Leung, Steve Lim, Aaron Dietrich, José Miguel Hernández-Lobato, Tom Ellis, Cheng Zhang, Kamil Ciosek
2020	A*	`conf`	Depth Uncertainty in Neural Networks. NeurIPS Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2020	J	`jnl`	Depth Uncertainty in Neural Networks. CoRR Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2020	J	`jnl`	Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
2020	J	`jnl`	Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures. CoRR Alonso Marco, Alexander von Rohr, Dominik Baumann, José Miguel Hernández-Lobato, Sebastian Trimpe
2020	J	`jnl`	Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato
2020	J	`jnl`	FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks. CoRR Weijie He, Xiaohao Mao, Chao Ma, José Miguel Hernández-Lobato, Ting Chen
2020	J	`jnl`	Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
2020	J	`jnl`	Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. CoRR Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang
2020	J	`jnl`	Predictive Complexity Priors. CoRR Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
2020	A*	`conf`	Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. ICML Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato
2020	J	`jnl`	Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. CoRR Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato
2020	—	`conf`	Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang
2020	A*	`conf`	Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
2020	J	`jnl`	Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
2020	J	`jnl`	Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf
2020	J	`jnl`	Sliced Kernelized Stein Discrepancy. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2020	J	`jnl`	Symmetry-Aware Actor-Critic for 3D Molecular Design. CoRR Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato
2020	A*	`conf`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2020	J	`jnl`	VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang
2020	J	`jnl`	Variational Depth Search in ResNets. CoRR Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato
2019	J	`jnl`	'In-Between' Uncertainty in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2019	J	`jnl`	A COLD Approach to Generating Optimal Samples. CoRR Omar Mahmood, José Miguel Hernández-Lobato
2019	—	`conf`	A Generative Model For Electron Paths. ICLR (Poster) John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	A Generative Model for Molecular Distance Geometry. CoRR Gregor N. C. Simm, José Miguel Hernández-Lobato
2019	A*	`conf`	A Model to Search for Synthesizable Molecules. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	A Model to Search for Synthesizable Molecules. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	A*	`conf`	Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
2019	J	`jnl`	Bayesian Batch Active Learning as Sparse Subset Approximation. CoRR Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato
2019	J	`jnl`	Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. CoRR Erik A. Daxberger, José Miguel Hernández-Lobato
2019	J	`jnl`	Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization. IEEE Comput. Archit. Lett. Kshitij Bhardwaj, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel Hernández-Lobato, Gu-Yeon Wei
2019	—	`conf`	Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
2019	A*	`conf`	Dropout as a Structured Shrinkage Prior. ICML Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth
2019	A*	`conf`	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. ICML Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
2019	—	`conf`	Generating Molecules via Chemical Reactions. DGS@ICLR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	—	`conf`	HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
2019	A*	`conf`	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang
2019	—	`conf`	Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2019	—	`conf`	Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. ICLR (Poster) Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
2019	A*	`conf`	Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. NeurIPS David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
2019	A*	`conf`	Variational Implicit Processes. ICML Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
2018	A*	`conf`	Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. ICML Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2018	J	`jnl`	Deconfounding Reinforcement Learning in Observational Settings. CoRR Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato
2018	J	`jnl`	EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. CoRR Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
2018	J	`jnl`	Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
2018	A*	`conf`	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. NeurIPS Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
2018	J	`jnl`	Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. CoRR Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
2018	—	`conf`	Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2018	J	`jnl`	Meta-Learning for Stochastic Gradient MCMC. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018	J	`jnl`	Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. CoRR Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
2018	J	`jnl`	Predicting Electron Paths. CoRR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2018	B	`conf`	Sensitivity analysis for predictive uncertainty. ESANN Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. Runkler
2018	J	`jnl`	Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek
2018	—	`conf`	Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control. ISC Workshops Moritz August, José Miguel Hernández-Lobato
2018	J	`jnl`	Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control. CoRR Moritz August, José Miguel Hernández-Lobato
2018	J	`jnl`	Variational Implicit Processes. CoRR Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
2018	J	`jnl`	Variational Measure Preserving Flows. CoRR Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani
2017	A	`conf`	A case for efficient accelerator design space exploration via Bayesian optimization. ISLPED Brandon Reagen, José Miguel Hernández-Lobato, Robert Adolf, Michael A. Gelbart, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks
2017	J	`jnl`	Actively Learning what makes a Discrete Sequence Valid. CoRR David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato
2017	J	`jnl`	Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. CoRR Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017	A*	`conf`	Grammar Variational Autoencoder. ICML Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
2017	J	`jnl`	Learning a Generative Model for Validity in Complex Discrete Structures. CoRR David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2017	—	`conf`	Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR (Poster) Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017	A*	`conf`	Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. ICML José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik
2017	A*	`conf`	Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. ICML Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck
2016	J	`jnl`	A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani
2016	A*	`conf`	Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. CVPR Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto
2016	J	`jnl`	Automatic chemical design using a data-driven continuous representation of molecules. CoRR Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
2016	A*	`conf`	Black-Box Alpha Divergence Minimization. ICML José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
2016	A*	`conf`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2016	J	`jnl`	GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. CoRR Matt J. Kusner, José Miguel Hernández-Lobato
2016	J	`jnl`	Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. CoRR Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2016	A*	`conf`	Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. ISCA Brandon Reagen, Paul N. Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, David M. Brooks
2016	A*	`conf`	Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams
2016	A	`conf`	Scalable Gaussian Process Classification via Expectation Propagation. AISTATS Daniel Hernández-Lobato, José Miguel Hernández-Lobato
2015	A*	`conf`	A Probabilistic Model for Dirty Multi-task Feature Selection. ICML Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani
2015	J	`jnl`	Expectation propagation in linear regression models with spike-and-slab priors. Mach. Learn. José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
2015	A*	`conf`	Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani
2015	A*	`conf`	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. ICML José Miguel Hernández-Lobato, Ryan P. Adams
2015	—	`conf`	Stochastic Expectation Propagation. NIPS Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2015	J	`jnl`	Stochastic Expectation Propagation. CoRR Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2014	A*	`conf`	Cold-start Active Learning with Robust Ordinal Matrix Factorization. ICML Neil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani
2014	—	`conf`	Gaussian Process Volatility Model. NIPS Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
2014	—	`conf`	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
2014	J	`jnl`	Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. CoRR José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
2014	A*	`conf`	Probabilistic Matrix Factorization with Non-random Missing Data. ICML José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani
2014	A*	`conf`	Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. ICML José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani
2013	—	`conf`	Bringing Representativeness into Social Media Monitoring and Analysis. HICSS Michael Kaschesky, Pawel Sobkowicz, José Miguel Hernández-Lobato, Guillaume Bouchard, Cédric Archambeau, Nicolas Scharioth, Robert Manchin, Adrian Gschwend, Reinhard Riedl
2013	—	`conf`	Dynamic Covariance Models for Multivariate Financial Time Series. ICML (3) Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
2013	—	`conf`	Gaussian Process Conditional Copulas with Applications to Financial Time Series. NIPS José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato
2013	—	`conf`	Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani
2013	J	`jnl`	Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation. J. Mach. Learn. Res. Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont
2013	—	`conf`	Learning Feature Selection Dependencies in Multi-task Learning. NIPS Daniel Hernández-Lobato, José Miguel Hernández-Lobato
2013	J	`jnl`	Semi-Supervised Domain Adaptation with Non-Parametric Copulas CoRR David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
2012	—	`conf`	Collaborative Gaussian Processes for Preference Learning. NIPS Neil Houlsby, José Miguel Hernández-Lobato, Ferenc Huszar, Zoubin Ghahramani
2012	—	`conf`	Semi-Supervised Domain Adaptation with Non-Parametric Copulas. NIPS David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
2011	A*	`conf`	Gaussianity Measures for Detecting the Direction of Causal Time Series. IJCAI José Miguel Hernández-Lobato, Pablo Morales-Mombiela, Alberto Suárez
2011	J	`jnl`	Network-based sparse Bayesian classification. Pattern Recognit. José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
2011	—	`conf`	Robust Multi-Class Gaussian Process Classification. NIPS Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont
2011	J	`jnl`	Semiparametric bivariate Archimedean copulas. Comput. Stat. Data Anal. José Miguel Hernández-Lobato, Alberto Suárez
2010	—	`conf`	Expectation Propagation for Bayesian Multi-task Feature Selection. ECML/PKDD (1) Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont
2010	J	`jnl`	Expectation Propagation for microarray data classification. Pattern Recognit. Lett. Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez
2010	—	`conf`	Hub Gene Selection Methods for the Reconstruction of Transcription Networks. ECML/PKDD (1) José Miguel Hernández-Lobato, Tjeerd Dijkstra
2008	J	`jnl`	Bayes Machines for binary classification. Pattern Recognit. Lett. Daniel Hernández-Lobato, José Miguel Hernández-Lobato
2007	—	`conf`	GARCH Processes with Non-parametric Innovations for Market Risk Estimation. ICANN (2) José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
2007	—	`conf`	Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes
2006	—	`conf`	Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis. ICANN (2) José Miguel Hernández-Lobato, Alberto Suárez
2006	C	`conf`	Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. IDEAL Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Rubén Ruiz-Torrubiano, Ángel Valle