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ORCIDJosé Miguel Hernández-Lobato
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- affiliation: University of Cambridge, Cambridge, UK
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2020 – today
- 2024
[j17]Pablo Morales-Álvarez
, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina:
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. Pattern Recognit. 146: 110057 (2024)- [j16]Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta:
Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. 2024 (2024) - [j15]Elre Talea Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato:
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c97]Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato:
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. ICLR 2024 - [c96]Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz:
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR 2024 - [c95]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. ICLR 2024 - [c94]Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato:
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. ICML 2024 - [c93]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. ICML 2024 - [c92]Ross M. Clarke, José Miguel Hernández-Lobato:
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens. ICML 2024 - [c91]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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i101]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i100]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. CoRR abs/2402.03008 (2024) - [i99]Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato:
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation. CoRR abs/2402.08845 (2024) - [i98]James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato:
A Generative Model of Symmetry Transformations. CoRR abs/2403.01946 (2024) - [i97]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:
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR abs/2405.01616 (2024) - [i96]Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato:
Accelerating Relative Entropy Coding with Space Partitioning. CoRR abs/2405.12203 (2024) - [i95]Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato:
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR abs/2405.18328 (2024) - [i94]Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato:
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR abs/2405.18457 (2024) - [i93]Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò:
Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR abs/2406.05832 (2024) - [i92]Austin Tripp, José Miguel Hernández-Lobato:
Diagnosing and fixing common problems in Bayesian optimization for molecule design. CoRR abs/2406.07709 (2024) - [i91]Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka, Pietro Liò, José Miguel Hernández-Lobato:
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. CoRR abs/2408.16115 (2024) - [i90]Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato:
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models. CoRR abs/2409.07323 (2024) - [i89]Ruikang Ouyang, Bo Qiang, José Miguel Hernández-Lobato:
BEnDEM:A Boltzmann Sampler Based on Bootstrapped Denoising Energy Matching. CoRR abs/2409.09787 (2024) - [i88]Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch:
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research. CoRR abs/2410.00544 (2024) - [i87]Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato:
Getting Free Bits Back from Rotational Symmetries in LLMs. CoRR abs/2410.01309 (2024) - 2023
- [j14]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. J. Open Source Softw. 8(87): 5361 (2023) - [j13]Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin:
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. 2023 (2023) - [j12]Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan:
Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. 2023 (2023) - [c90]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR 2023 - [c89]Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato:
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. ICLR 2023 - [c88]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. ICLR 2023 - [c87]Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato:
Faster Relative Entropy Coding with Greedy Rejection Coding. NeurIPS 2023 - [c86]Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato:
Compression with Bayesian Implicit Neural Representations. NeurIPS 2023 - [c85]Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin:
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS 2023 - [c84]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. NeurIPS 2023 - [c83]Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato:
Tanimoto Random Features for Scalable Molecular Machine Learning. NeurIPS 2023 - [d4]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.0. Zenodo, 2023 [all versions]
- [d3]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.1. Zenodo, 2023 [all versions] - [d2]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.2. Zenodo, 2023 [all versions] - [d1]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.3. Zenodo, 2023 [all versions] - [i86]Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin:
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR abs/2302.10279 (2023) - [i85]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. CoRR abs/2302.12014 (2023) - [i84]Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato:
Compression with Bayesian Implicit Neural Representations. CoRR abs/2305.19185 (2023) - [i83]Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin:
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR abs/2306.11589 (2023) - [i82]Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato:
Tanimoto Random Features for Scalable Molecular Machine Learning. CoRR abs/2306.14809 (2023) - [i81]Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato:
Leveraging Task Structures for Improved Identifiability in Neural Network Representations. CoRR abs/2306.14861 (2023) - [i80]Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato:
Online Laplace Model Selection Revisited. CoRR abs/2307.06093 (2023) - [i79]Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato:
Minimal Random Code Learning with Mean-KL Parameterization. CoRR abs/2307.07816 (2023) - [i78]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. CoRR abs/2308.10364 (2023) - [i77]Richard Bergna, Felix L. Opolka, Pietro Liò, José Miguel Hernández-Lobato:
Graph Neural Stochastic Differential Equations. CoRR abs/2308.12316 (2023) - [i76]Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato:
Faster Relative Entropy Coding with Greedy Rejection Coding. CoRR abs/2309.15746 (2023) - [i75]Jiajun He, Gergely Flamich, Zongyu Guo, José Miguel Hernández-Lobato:
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations. CoRR abs/2309.17182 (2023) - [i74]Austin Tripp, José Miguel Hernández-Lobato:
Genetic algorithms are strong baselines for molecule generation. CoRR abs/2310.09267 (2023) - [i73]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. CoRR abs/2310.09270 (2023) - [i72]Elre T. Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato:
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. CoRR abs/2310.14901 (2023) - [i71]Ross M. Clarke, Baiyu Su, José Miguel Hernández-Lobato:
Adam through a Second-Order Lens. CoRR abs/2310.14963 (2023) - [i70]Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina:
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. CoRR abs/2310.19359 (2023) - [i69]Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato:
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR abs/2310.20053 (2023) - [i68]Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz:
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR abs/2310.20581 (2023) - 2022
- [j11]Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado:
DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J. Chem. Inf. Model. 62(15): 3486-3502 (2022) - [c82]Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. AISTATS 2022: 4915-4936 - [c81]Ross M. Clarke, Elre Talea Oldewage, José Miguel Hernández-Lobato:
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. ICLR 2022 - [c80]Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR 2022 - [c79]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML 2022: 796-821 - [c78]Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato:
Fast Relative Entropy Coding with A* coding. ICML 2022: 6548-6577 - [c77]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022: 9260-9279 - [c76]Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato:
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. NeurIPS 2022 - [c75]Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen:
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis. WWW 2022: 2511-2521 - [i67]Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato:
Fast Relative Entropy Coding with A* coding. CoRR abs/2201.12857 (2022) - [i66]Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato:
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo. CoRR abs/2202.04599 (2022) - [i65]Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin:
A Probabilistic Deep Image Prior for Computational Tomography. CoRR abs/2203.00479 (2022) - [i64]Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato:
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. CoRR abs/2205.02708 (2022) - [i63]Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR abs/2206.08900 (2022) - [i62]Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato:
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR abs/2207.05714 (2022) - [i61]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. CoRR abs/2208.01893 (2022) - [i60]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR abs/2210.04994 (2022) - 2021
- [j10]Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato:
Sampling the Variational Posterior with Local Refinement. Entropy 23(11): 1475 (2021) - [c74]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang:
Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI 2021: 15669-15677 - [c73]Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. AISTATS 2021: 694-702 - [c72]Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. ICBINB@NeurIPS 2021: 59-63 - [c71]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Sliced Kernelized Stein Discrepancy. ICLR 2021 - [c70]Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato:
Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR 2021 - [c69]Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato:
Activation-level uncertainty in deep neural networks. ICLR 2021 - [c68]Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato:
Symmetry-Aware Actor-Critic for 3D Molecular Design. ICLR 2021 - [c67]Andrew Campbell, Wenlong Chen, Vincent Stimper, José Miguel Hernández-Lobato, Yichuan Zhang:
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. ICML 2021: 1238-1248 - [c66]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Bayesian Deep Learning via Subnetwork Inference. ICML 2021: 2510-2521 - [c65]Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato:
Active Slices for Sliced Stein Discrepancy. ICML 2021: 3766-3776 - [c64]Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. NeurIPS 2021: 802-814 - [c63]Chao Ma, José Miguel Hernández-Lobato:
Functional Variational Inference based on Stochastic Process Generators. NeurIPS 2021: 21795-21807 - [i59]Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato:
Active Slices for Sliced Stein Discrepancy. CoRR abs/2102.03159 (2021) - [i58]Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Nonlinear Invariant Risk Minimization: A Causal Approach. CoRR abs/2102.12353 (2021) - [i57]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:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2104.04034 (2021) - [i56]Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang:
Contextual HyperNetworks for Novel Feature Adaptation. CoRR abs/2104.05860 (2021) - [i55]Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal:
Improving black-box optimization in VAE latent space using decoder uncertainty. CoRR abs/2107.00096 (2021) - [i54]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR abs/2110.05721 (2021) - [i53]Ross M. Clarke, Elre T. Oldewage, José Miguel Hernández-Lobato:
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation. CoRR abs/2110.10461 (2021) - [i52]Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado:
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. CoRR abs/2110.15486 (2021) - [i51]Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. CoRR abs/2110.15828 (2021) - [i50]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, José Miguel Hernández-Lobato:
Bootstrap Your Flow. CoRR abs/2111.11510 (2021) - [i49]Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Depth Uncertainty Networks for Active Learning. CoRR abs/2112.06796 (2021) - [i48]Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not. CoRR abs/2112.06926 (2021) - 2020
- [j9]Jonathan Gordon, José Miguel Hernández-Lobato:
Combining deep generative and discriminative models for Bayesian semi-supervised learning. Pattern Recognit. 100: 107156 (2020) - [c62]Gregor N. C. Simm, José Miguel Hernández-Lobato:
A Generative Model for Molecular Distance Geometry. ICML 2020: 8949-8958 - [c61]Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato:
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. ICML 2020: 8959-8969 - [c60]Kshitij Bhardwaj, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel Hernández-Lobato, Gu-Yeon Wei:
A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs. ISLPED 2020: 145-150 - [c59]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:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. NeurIPS (Competition and Demos) 2020: 191-205 - [c58]Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato:
Depth Uncertainty in Neural Networks. NeurIPS 2020 - [c57]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS 2020 - [c56]Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato:
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. NeurIPS 2020 - [c55]Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. NeurIPS 2020 - [c54]Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS 2020 - [i47]Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato:
Variational Depth Search in ResNets. CoRR abs/2002.02797 (2020) - [i46]Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato:
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics. CoRR abs/2002.07717 (2020) - [i45]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Cheng Zhang:
Large-Scale Educational Question Analysis with Partial Variational Auto-encoders. CoRR abs/2003.05980 (2020) - [i44]Alonso Marco, Alexander von Rohr, Dominik Baumann, José Miguel Hernández-Lobato, Sebastian Trimpe:
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures. CoRR abs/2005.07443 (2020) - [i43]Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato:
Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR abs/2006.06848 (2020) - [i42]Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato:
Depth Uncertainty in Neural Networks. CoRR abs/2006.08437 (2020) - [i41]Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR abs/2006.09191 (2020) - [i40]Eric T. Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato:
Predictive Complexity Priors. CoRR abs/2006.10801 (2020) - [i39]Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard E. Turner, Cheng Zhang:
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data. CoRR abs/2006.11941 (2020) - [i38]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Sliced Kernelized Stein Discrepancy. CoRR abs/2006.16531 (2020) - [i37]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:
DRIFT: Deep Reinforcement Learning for Functional Software Testing. CoRR abs/2007.08220 (2020) - [i36]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:
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2007.12061 (2020) - [i35]Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato:
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding. CoRR abs/2010.01185 (2020) - [i34]Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato:
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference. CoRR abs/2010.14689 (2020) - [i33]Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato:
Symmetry-Aware Actor-Critic for 3D Molecular Design. CoRR abs/2011.12747 (2020) - [i32]Weijie He, Xiaohao Mao, Chao Ma, José Miguel Hernández-Lobato, Ting Chen:
FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks. CoRR abs/2012.01065 (2020) - [i31]Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf:
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR abs/2012.09092 (2020) - [i30]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR abs/2012.11522 (2020)
2010 – 2019
- 2019
- [j8]Kshitij Bhardwaj, Marton Havasi, Yuan Yao, David M. Brooks, José Miguel Hernández-Lobato, Gu-Yeon Wei:
Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization. IEEE Comput. Archit. Lett. 18(2): 119-123 (2019) - [c53]Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI 2019: 1-8 - [c52]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Generative Model For Electron Paths. ICLR (Poster) 2019 - [c51]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Generating Molecules via Chemical Reactions. DGS@ICLR 2019 - [c50]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) 2019 - [c49]Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato:
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. ICLR (Poster) 2019 - [c48]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR 2019 - [c47]Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato:
Variational Implicit Processes. ICML 2019: 4222-4233 - [c46]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. ICML 2019: 4234-4243 - [c45]Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth:
Dropout as a Structured Shrinkage Prior. ICML 2019: 4712-4722 - [c44]David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek:
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. NeurIPS 2019: 4509-4518 - [c43]Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS 2019: 6356-6367 - [c42]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. NeurIPS 2019: 7935-7947 - [c41]Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS 2019: 14791-14802 - [i29]Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang:
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR abs/1905.02599 (2019) - [i28]Omar Mahmood, José Miguel Hernández-Lobato:
A COLD Approach to Generating Optimal Samples. CoRR abs/1905.09885 (2019) - [i27]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. CoRR abs/1906.05221 (2019) - [i26]Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
'In-Between' Uncertainty in Bayesian Neural Networks. CoRR abs/1906.11537 (2019) - [i25]Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. CoRR abs/1908.02144 (2019) - [i24]Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang:
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR abs/1908.04537 (2019) - [i23]Gregor N. C. Simm, José Miguel Hernández-Lobato:
A Generative Model for Molecular Distance Geometry. CoRR abs/1909.11459 (2019) - [i22]Erik A. Daxberger, José Miguel Hernández-Lobato:
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. CoRR abs/1912.05651 (2019) - 2018
- [c40]Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. Runkler:
Sensitivity analysis for predictive uncertainty. ESANN 2018 - [c39]David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato:
Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) 2018 - [c38]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. ICML 2018: 1192-1201 - [c37]Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes:
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. NeurIPS 2018: 7517-7527 - [c36]Moritz August, José Miguel Hernández-Lobato:
Taking Gradients Through Experiments: LSTMs and Memory Proximal Policy Optimization for Black-Box Quantum Control. ISC Workshops 2018: 591-613 - [i21]Moritz August, José Miguel Hernández-Lobato:
Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control. CoRR abs/1802.04063 (2018) - [i20]Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Variational Measure Preserving Flows. CoRR abs/1805.10377 (2018) - [i19]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Predicting Electron Paths. CoRR abs/1805.10970 (2018) - [i18]Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato:
Variational Implicit Processes. CoRR abs/1806.02390 (2018) - [i17]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Meta-Learning for Stochastic Gradient MCMC. CoRR abs/1806.04522 (2018) - [i16]Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes:
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. CoRR abs/1806.05490 (2018) - [i15]Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. CoRR abs/1809.11142 (2018) - [i14]Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato:
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. CoRR abs/1810.00440 (2018) - [i13]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR abs/1810.03958 (2018) - [i12]David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek:
Successor Uncertainties: exploration and uncertainty in temporal difference learning. CoRR abs/1810.06530 (2018) - [i11]Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Deconfounding Reinforcement Learning in Observational Settings. CoRR abs/1812.10576 (2018) - 2017
- [c35]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. ICLR (Poster) 2017 - [c34]José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik:
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. ICML 2017: 1470-1479 - [c33]Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck:
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. ICML 2017: 1645-1654 - [c32]Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato:
Grammar Variational Autoencoder. ICML 2017: 1945-1954 - [c31]Brandon Reagen, José Miguel Hernández-Lobato, Robert Adolf, Michael A. Gelbart, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks:
A case for efficient accelerator design space exploration via Bayesian optimization. ISLPED 2017: 1-6 - [i10]David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato:
Actively Learning what makes a Discrete Sequence Valid. CoRR abs/1708.04465 (2017) - [i9]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems. CoRR abs/1710.07283 (2017) - [i8]David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato:
Learning a Generative Model for Validity in Complex Discrete Structures. CoRR abs/1712.01664 (2017) - 2016
- [j7]José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani:
A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. 17: 160:1-160:53 (2016) - [c30]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation. AISTATS 2016: 168-176 - [c29]Viktoriia Sharmanska, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Novi Quadrianto:
Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. CVPR 2016: 2194-2202 - [c28]Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML 2016: 1472-1481 - [c27]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams:
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML 2016: 1492-1501 - [c26]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520 - [c25]Brandon Reagen, Paul N. Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, David M. Brooks:
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. ISCA 2016: 267-278 - [i7]Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR abs/1602.04133 (2016) - [i6]Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. CoRR abs/1605.07127 (2016) - [i5]Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik:
Automatic chemical design using a data-driven continuous representation of molecules. CoRR abs/1610.02415 (2016) - [i4]Matt J. Kusner, José Miguel Hernández-Lobato:
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. CoRR abs/1611.04051 (2016) - 2015
- [j6]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
Expectation propagation in linear regression models with spike-and-slab priors. Mach. Learn. 99(3): 437-487 (2015) - [c24]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Zoubin Ghahramani:
A Probabilistic Model for Dirty Multi-task Feature Selection. ICML 2015: 1073-1082 - [c23]José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani:
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML 2015: 1699-1707 - [c22]José Miguel Hernández-Lobato, Ryan P. Adams:
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. ICML 2015: 1861-1869 - [c21]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. NIPS 2015: 2323-2331 - [i3]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. CoRR abs/1506.04132 (2015) - 2014
- [c20]José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices. ICML 2014: 379-387 - [c19]Neil Houlsby, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Cold-start Active Learning with Robust Ordinal Matrix Factorization. ICML 2014: 766-774 - [c18]José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani:
Probabilistic Matrix Factorization with Non-random Missing Data. ICML 2014: 1512-1520 - [c17]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS 2014: 918-926 - [c16]Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Volatility Model. NIPS 2014: 1044-1052 - [i2]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. CoRR abs/1406.2541 (2014) - 2013
- [j5]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation. J. Mach. Learn. Res. 14(1): 1891-1945 (2013) - [c15]Michael Kaschesky, Pawel Sobkowicz, José Miguel Hernández-Lobato, Guillaume Bouchard, Cédric Archambeau, Nicolas Scharioth, Robert Manchin, Adrian Gschwend, Reinhard Riedl:
Bringing Representativeness into Social Media Monitoring and Analysis. HICSS 2013: 2003-2012 - [c14]David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) 2013: 10-18 - [c13]Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Dynamic Covariance Models for Multivariate Financial Time Series. ICML (3) 2013: 558-566 - [c12]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Learning Feature Selection Dependencies in Multi-task Learning. NIPS 2013: 746-754 - [c11]José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato:
Gaussian Process Conditional Copulas with Applications to Financial Time Series. NIPS 2013: 1736-1744 - [i1]David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. CoRR abs/1301.0142 (2013) - 2012
- [c10]David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. NIPS 2012: 674-682 - [c9]Neil Houlsby, José Miguel Hernández-Lobato, Ferenc Huszar, Zoubin Ghahramani:
Collaborative Gaussian Processes for Preference Learning. NIPS 2012: 2105-2113 - 2011
- [j4]José Miguel Hernández-Lobato, Alberto Suárez:
Semiparametric bivariate Archimedean copulas. Comput. Stat. Data Anal. 55(6): 2038-2058 (2011) - [j3]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
Network-based sparse Bayesian classification. Pattern Recognit. 44(4): 886-900 (2011) - [c8]José Miguel Hernández-Lobato, Pablo Morales-Mombiela, Alberto Suárez:
Gaussianity Measures for Detecting the Direction of Causal Time Series. IJCAI 2011: 1318-1323 - [c7]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Robust Multi-Class Gaussian Process Classification. NIPS 2011: 280-288 - 2010
- [j2]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Alberto Suárez:
Expectation Propagation for microarray data classification. Pattern Recognit. Lett. 31(12): 1618-1626 (2010) - [c6]José Miguel Hernández-Lobato, Tjeerd Dijkstra:
Hub Gene Selection Methods for the Reconstruction of Transcription Networks. ECML/PKDD (1) 2010: 506-521 - [c5]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Thibault Helleputte, Pierre Dupont:
Expectation Propagation for Bayesian Multi-task Feature Selection. ECML/PKDD (1) 2010: 522-537
2000 – 2009
- 2008
- [j1]Daniel Hernández-Lobato, José Miguel Hernández-Lobato:
Bayes Machines for binary classification. Pattern Recognit. Lett. 29(10): 1466-1473 (2008) - 2007
- [c4]José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez:
GARCH Processes with Non-parametric Innovations for Market Risk Estimation. ICANN (2) 2007: 718-727 - [c3]José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes:
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS 2007: 649-656 - 2006
- [c2]José Miguel Hernández-Lobato, Alberto Suárez:
Competitive and Collaborative Mixtures of Experts for Financial Risk Analysis. ICANN (2) 2006: 691-700 - [c1]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Rubén Ruiz-Torrubiano, Ángel Valle:
Pruning Adaptive Boosting Ensembles by Means of a Genetic Algorithm. IDEAL 2006: 322-329
Coauthor Index
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