Hernández-Lobato, José Miguel
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural\n Networks
2015/02/18 by José Miguel Hernández-Lobato, Hernández-Lobato, José Miguel, Ryan P. Adams +1 · 89 citations
Computer Science · #Machine Learning and Data Classification #Advanced Neural Network Applications #Bayesian Modeling and Causal Inference
- Predictive Entropy Search for Efficient Global Optimization of Black-box\n Functions
2014/06/10 by José Miguel Hernández-Lobato, Matthew W. Hoffman, Hernández-Lobato, José Miguel +3 · 38 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
- Grammar Variational Autoencoder
2017/03/06 by Matt J. Kusner, Brooks Paige, Kusner, Matt J. +3 · 26 citations
Computer Science · Materials Science · #Topic Modeling #Generative Adversarial Networks and Image Synthesis #Machine Learning in Materials Science
- Predictive Entropy Search for Multi-objective Bayesian Optimization
2015/11/17 by Hernández-Lobato, Daniel, Hernández-Lobato, José Miguel, Shah, Amar +1 · 14 citations
#FOS: Computer and information sciences #Machine Learning (stat.ML)
- GANS for Sequences of Discrete Elements with the Gumbel-softmax\n Distribution
2016/11/12 by Matt J. Kusner, Kusner, Matt J., José Miguel Hernández-Lobato +1 · 19 citations
Computer Science · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Processing and 3D Reconstruction #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Flow Annealed Importance Sampling Bootstrap
2022/08/03 by Laurence Illing Midgley, Vincent Stimper, Midgley, Laurence Illing +7 · 19 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM)
- Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
2016/11/09 by Jaques, Natasha, Gu, Shixiang, Bahdanau, Dzmitry +3 · 11 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
- EDDI: Efficient Dynamic Discovery of High-Value Information with Partial\n VAE
2018/09/28 by Chao Ma, Ma, Chao, Sebastian Tschiatschek +9 · 18 citations
Computer Science · #Machine Learning and Data Classification #Machine Learning in Healthcare #Gaussian Processes and Bayesian Inference
- Bayesian Deep Learning via Subnetwork Inference
2020/10/28 by Erik Daxberger, Daxberger, Erik, Eric Nalisnick +7 · 12 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Adversarial Robustness in Machine Learning #Machine Learning and Data Classification
- Parallel and Distributed Thompson Sampling for Large-scale Accelerated\n Exploration of Chemical Space
2017/06/06 by José Miguel Hernández-Lobato, James Requeima, Hernández-Lobato, José Miguel +5 · 11 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification
- Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
2015/02/18 by José Miguel Hernández-Lobato, Hernández-Lobato, José Miguel, Michael A. Gelbart +7 · 9 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Machine Learning and Algorithms
- Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
2024/02/01 by Theodore Papamarkou, Maria Skoularidou, Papamarkou, Theodore +48 · 1 voice · 14 citations
Computer Science · #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #cs.LG #stat.ML
- Deep Gaussian Processes for Regression using Approximate Expectation Propagation
2016/02/12 by Thang D. Bui, Daniel Hernández-Lobato, Bui, Thang D. +7 · 10 citations
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Target Tracking and Data Fusion in Sensor Networks
- Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
2020/06/16 by Tripp, Austin, Daxberger, Erik, Hernández-Lobato, José Miguel · 8 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- SE(3) Equivariant Augmented Coupling Flows
2023/08/20 by Laurence I. Midgley, Vincent Stimper, Midgley, Laurence I. +9 · 10 citations
Computer Science · Engineering · #Advanced Mathematical Modeling in Engineering #Computational Physics (physics.comp-ph) #Computer Graphics and Visualization Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Heat and Mass Transfer in Porous Media #Machine Learning (cs.LG)
- Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge
2020/07/23 by Zichao Wang, Wang, Zichao, Angus Lamb +21 · 6 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Machine Learning and Algorithms
- VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
2020/06/21 by Ma, Chao, Tschiatschek, Sebastian, Hernández-Lobato, José Miguel +2 · 6 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Black-box α-divergence Minimization
2015/11/10 by José Miguel Hernández-Lobato, Hernández-Lobato, José Miguel, Yingzhen Li +10 · 5 citations
Computer Science · Engineering · #Gaussian Processes and Bayesian Inference #Neural Networks and Applications #Fault Detection and Control Systems
- Getting a CLUE: A Method for Explaining Uncertainty Estimates
2020/06/11 by Javier Antorán, Antorán, Javier, Umang Bhatt +7 · 6 citations
Computer Science · Decision Sciences · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Scientific Computing and Data Management
- Bayesian Batch Active Learning as Sparse Subset Approximation
2019/08/06 by Robert Pinsler, Jonathan Gordon, Pinsler, Robert +5 · 6 citations
Computer Science · #Machine Learning and Algorithms #Gaussian Processes and Bayesian Inference #Algorithms and Data Compression
- Barking up the right tree: an approach to search over molecule synthesis DAGs
2020/12/21 by John Bradshaw, Bradshaw, John, Brooks Paige +7 · 6 citations
Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Protein Structure and Dynamics
- On conditional diffusion models for PDE simulations
2024/10/21 by Aliaksandra Shysheya, Shysheya, Aliaksandra, Cristiana Diaconu +11 · 13 citations
Physics and Astronomy · Computer Science · #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference #Machine Learning in Healthcare
- Constrained Bayesian Optimization for Automatic Chemical Design
2017/09/16 by Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato, Griffiths, Ryan-Rhys +1 · 6 citations
Materials Science · Computer Science · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Advanced Multi-Objective Optimization Algorithms
- DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
2021/10/29 by Miguel García-Ortegón, Gregor N. C. Simm, García-Ortegón, Miguel +9 · 5 citations
Computer Science · Materials Science · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Machine Learning in Materials Science #Protein Structure and Dynamics
- Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
2020/12/16 by Chaochao Lu, Lu, Chaochao, Biwei Huang +9 · 5 citations
Computer Science · Decision Sciences · Mathematics · #Reinforcement Learning in Robotics #Advanced Bandit Algorithms Research #Advanced Causal Inference Techniques
- Improving black-box optimization in VAE latent space using decoder uncertainty
2021/06/30 by Notin, Pascal, Hernández-Lobato, José Miguel, Gal, Yarin · 4 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- A Generative Model For Electron Paths
2018/05/23 by Bradshaw, John, Kusner, Matt J., Paige, Brooks +2 · 3 citations
#Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Action-Sufficient State Representation Learning for Control with Structural Constraints
2021/10/12 by Biwei Huang, Chaochao Lu, Huang, Biwei +11 · 4 citations
Computer Science · #Reinforcement Learning in Robotics #Explainable Artificial Intelligence (XAI) #Data Stream Mining Techniques
- Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
2018/06/14 by Marton Havasi, José Miguel Hernández-Lobato, Havasi, Marton +3 · 3 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
- Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
2018/09/30 by Marton Havasi, Robert Peharz, Havasi, Marton +3 · 3 citations
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Towards Training One-Step Diffusion Models Without Distillation
2025/02/11 by Zhang, Mingtian, Chen, Wenlin, He, Jiajun +4 · 1 voice · 3 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)
- Genetic algorithms are strong baselines for molecule generation
2023/10/13 by Tripp, Austin, Hernández-Lobato, José Miguel · 5 citations
#FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Quantitative Methods (q-bio.QM)
- A General Framework for Constrained Bayesian Optimization using Information-based Search
2015/11/30 by José Miguel Hernández-Lobato, Michael A. Gelbart, Hernández-Lobato, José Miguel +7 · 2 citations
Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Machine Learning and Algorithms
- Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
2022/02/09 by Peis, Ignacio, Ma, Chao, Hernández-Lobato, José Miguel · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
2025/02/10 by Jiajun He, Yuanqi Du, He, Jiajun +13 · 9 citations
Computer Science · Medicine · #Neural Networks and Applications #Explainable Artificial Intelligence (XAI) #Artificial Intelligence in Healthcare and Education
- 'In-Between' Uncertainty in Bayesian Neural Networks
2019/06/27 by Foong, Andrew Y. K., Li, Yingzhen, Hernández-Lobato, José Miguel +1 · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- A Model to Search for Synthesizable Molecules
2019/06/12 by John Bradshaw, Bradshaw, John, Brooks Paige +7 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microbial Metabolic Engineering and Bioproduction #Protein Structure and Dynamics
- Tanimoto Random Features for Scalable Molecular Machine Learning
2023/06/26 by Tripp, Austin, Bacallado, Sergio, Singh, Sukriti +1 · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Sliced Kernelized Stein Discrepancy
2020/06/30 by Gong, Wenbo, Li, Yingzhen, Hernández-Lobato, José Miguel · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Depth Uncertainty in Neural Networks
2020/06/15 by Antorán, Javier, Allingham, James Urquhart, Hernández-Lobato, José Miguel · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
2020/10/02 by Flamich, Gergely, Havasi, Marton, Hernández-Lobato, José Miguel · 2 citations
#94A08 (Primary) 94A34 (Secondary) #E.4 #FOS: Computer and information sciences #FOS: Electrical engineering #G.3 #H.1.1 #Image and Video Processing (eess.IV) #Information Theory (cs.IT) #Machine Learning (stat.ML) #electronic engineering #information engineering
- Training Neural Samplers with Reverse Diffusive KL Divergence
2024/10/16 by Jiajun He, Wenlin Chen, He, Jiajun +7 · 6 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
2022/05/05 by Chen, Wenlin, Tripp, Austin, Hernández-Lobato, José Miguel · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Retro-fallback: retrosynthetic planning in an uncertain world
2023/10/13 by Austin Tripp, Tripp, Austin, Krzysztof Maziarz +7 · 3 citations
Materials Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science
- Gaussian Process Conditional Copulas with Applications to Financial Time\n Series
2013/07/01 by José Miguel Hernández-Lobato, James Robert Lloyd, Hernández-Lobato, José Miguel +3 · 2 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · Social Sciences · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Forecasting Techniques and Applications #Gaussian Processes and Bayesian Inference #Insurance, Mortality, Demography, Risk Management #Machine Learning (stat.ML) #Statistical Methods and Inference #Time Series Analysis and Forecasting
- Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
2019/12/11 by Erik Daxberger, José Miguel Hernández-Lobato, Daxberger, Erik +1 · 2 citations
Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Gaussian Processes and Bayesian Inference
- Variational Implicit Processes
2018/06/06 by Chao Ma, Yingzhen Li, Ma, Chao +3 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
- BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
2024/09/15 by RuiKang OuYang, Bo Qiang, OuYang, RuiKang +3 · 4 citations
Arts and Humanities · Engineering · #Artificial Intelligence (cs.AI) #Computation (stat.CO) #Diverse Musicological Studies #Energy Load and Power Forecasting #FOS: Computer and information sciences #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Diffusive Gibbs Sampling
2024/02/05 by Chen, Wenlin, Zhang, Mingtian, Paige, Brooks +2 · 3 citations
#Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
2024/10/01 by Sabanza-Gil, Víctor, Barbano, Riccardo, Gutiérrez, Daniel Pacheco +4 · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
2022/06/17 by Javier Antorán, David M. Janz, Antorán, Javier +11 · 2 citations
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
- Scalable Gaussian Process Classification via Expectation Propagation
2015/07/16 by Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Hernández-Lobato, Daniel +1 · 1 citation
Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Target Tracking and Data Fusion in Sensor Networks
- Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
2016/05/23 by Depeweg, Stefan, Hernández-Lobato, José Miguel, Doshi-Velez, Finale +1 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Diagnosing and fixing common problems in Bayesian optimization for molecule design
2024/06/11 by Austin Tripp, Tripp, Austin, José Miguel Hernández-Lobato +1 · 3 citations
Computer Science · #Computational Drug Discovery Methods
- Deep Gaussian Processes with Decoupled Inducing Inputs
2018/01/09 by Havasi, Marton, Hernández-Lobato, José Miguel, Murillo-Fuentes, Juan José · 1 citation
#FOS: Computer and information sciences #Machine Learning (stat.ML)
- Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control
2018/02/12 by August, Moritz, Hernández-Lobato, José Miguel · 1 citation
#FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Physics (quant-ph)
- Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
2018/10/15 by David M. Janz, Janz, David, Jiri Hron +8 · 1 citation
Computer Science · #Artificial Intelligence in Games #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications #Reinforcement Learning in Robotics
- Deconfounding Reinforcement Learning in Observational Settings
2018/12/26 by Lu, Chaochao, Schölkopf, Bernhard, Hernández-Lobato, José Miguel · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- A Generative Model of Symmetry Transformations
2024/03/04 by Allingham, James Urquhart, Mlodozeniec, Bruno Kacper, Padhy, Shreyas +5 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- FEAT: Free energy Estimators with Adaptive Transport
2025/04/15 by Jiajun He, He, Jiajun, Yuanqi Du +12 · 1 voice · 4 citations
Materials Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Machine Learning in Materials Science #Quantum many-body systems #cs.LG #physics.chem-ph #physics.comp-ph #stat.ML
- Fast Relative Entropy Coding with A* coding
2022/01/30 by Gergely Flamich, Stratis Markou, Flamich, Gergely +3 · 1 citation
Computer Science · #68P30 #94A08 #94A20 #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #E.4 #FOS: Computer and information sciences #G.3 #Generative Adversarial Networks and Image Synthesis #H.1.1 #Information Theory (cs.IT)
- Improving Antibody Design with Force-Guided Sampling in Diffusion Models
2024/06/09 by Paulina Kulytė, Kulytė, Paulina, Francisco Vargas +9 · 2 citations
Biochemistry, Genetics and Molecular Biology · Medicine · #Biomolecules (q-bio.BM) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Monoclonal and Polyclonal Antibodies Research #Protein purification and stability #Quantitative Methods (q-bio.QM) #Viral Infectious Diseases and Gene Expression in Insects
- Aligning Multimodal Representations through an Information Bottleneck
2025/06/05 by Almudévar, Antonio, Hernández-Lobato, José Miguel, Khurana, Sameer +2 · 4 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)
- Minimal Random Code Learning with Mean-KL Parameterization
2023/07/15 by Lin, Jihao Andreas, Flamich, Gergely, Hernández-Lobato, José Miguel · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Compression with Bayesian Implicit Neural Representations
2023/05/30 by Guo, Zongyu, Flamich, Gergely, He, Jiajun +2 · 1 citation
#FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Progressive Tempering Sampler with Diffusion
2025/06/05 by Rissanen, Severi, OuYang, RuiKang, He, Jiajun +4 · 4 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
2024/05/28 by Jihao Andreas Lin, Shreyas Padhy, Lin, Jihao Andreas +7 · 2 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Data Classification
- Generative Active Learning for the Search of Small-molecule Protein Binders
2024/05/02 by Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh +31 · 1 citation
#Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG)
- Scalable Gaussian Processes with Latent Kronecker Structure
2025/06/07 by Jihao Andreas Lin, Lin, Jihao Andreas, Sebastian Ament +9 · 3 citations
Computer Science · Materials Science · #Gaussian Processes and Bayesian Inference #Machine Learning in Materials Science #Bayesian Modeling and Causal Inference
- There Was Never a Bottleneck in Concept Bottleneck Models
2025/06/05 by Antonio Almudévar, José Miguel Hernández-Lobato, Almudévar, Antonio +3 · 1 citation
Computer Science · #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG)
- Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
2025/05/27 by Zhang, Fengzhe, Midgley, Laurence I., Hernández-Lobato, José Miguel · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Accelerating Relative Entropy Coding with Space Partitioning
2024/05/20 by Jiajun He, He, Jiajun, Gergely Flamich +3 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Information Theory (cs.IT) #Machine Learning (cs.LG)
- Ergodic Inference: Accelerate Convergence by Optimisation
2018/05/25 by Yichuan Zhang, José Miguel Hernández-Lobato, Zhang, Yichuan +1 · 1 citation
Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Generative Adversarial Networks and Image Synthesis
- Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
2025/02/27 by Weilin Chen, Chen, Weilin, Ruichu Cai +7 · 1 citation
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
- Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
2025/02/26 by Weilin Chen, Ruichu Cai, Chen, Weilin +6 · 1 citation
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Statistical Methods and Inference