Philipp Hennig
- Benchmarking Neural Network Training Algorithms
2023/06/12 by George E. Dahl, Dahl, George E., Frank Schneider +47 · 2 voices · 8 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques #cs.LG #stat.ML
- Laplace Redux -- Effortless Bayesian Deep Learning
2021/06/28 by Erik Daxberger, Agustinus Kristiadi, Daxberger, Erik +9 · 34 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms #Domain Adaptation and Few-Shot Learning
- Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
2016/05/23 by Aaron Klein, Klein, Aaron, Stefan Falkner +7 · 25 citations
Computer Science · Decision Sciences · #Machine Learning and Data Classification #Advanced Bandit Algorithms Research #Machine Learning and Algorithms
- Entropy Search for Information-Efficient Global Optimization
2011/12/06 by Philipp Hennig, Hennig, Philipp, Christian J. Schuler +1 · 18 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Metaheuristic Optimization Algorithms Research
- Batch Bayesian Optimization via Local Penalization
2015/05/29 by Javier González, Zhenwen Dai, González, Javier +5 · 21 citations
Computer Science · Biochemistry, Genetics and Molecular Biology · #Gaussian Processes and Bayesian Inference #Spectroscopy Techniques in Biomedical and Chemical Research #Advanced Multi-Objective Optimization Algorithms
- Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
2020/07/03 by Robin M. Schmidt, Frank Schneider, Schmidt, Robin M. +3 · 10 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Mobile Crowdsensing and Crowdsourcing #Stochastic Gradient Optimization Techniques
- Early Stopping without a Validation Set
2017/03/28 by Maren Mahsereci, Lukas Balles, Mahsereci, Maren +5 · 7 citations
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Probabilistic ODE Solvers with Runge-Kutta Means
2014/06/10 by Michael Schober, David Duvenaud, Schober, Michael +3 · 6 citations
Computer Science · Decision Sciences · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Scientific Research and Discoveries #Simulation Techniques and Applications
- A probabilistic model for the numerical solution of initial value problems
2018/01/08 by Michael Schober, Simo Särkkä, Philipp Hennig · 9 citations
Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference
- Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
2022/12/23 by Marvin Pförtner, Ingo Steinwart, Pförtner, Marvin +5 · 11 citations
Computer Science · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
- Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
2024/02/01 by Theodore Papamarkou, Maria Skoularidou, Papamarkou, Theodore +47 · 1 voice · 11 citations
#cs.LG #stat.ML
- Learnable Uncertainty under Laplace Approximations
2020/10/06 by Agustinus Kristiadi, Kristiadi, Agustinus, Matthias Hein +3 · 5 citations
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning and Algorithms
- Informed Equation Learning
2021/05/13 by Matthias Werner, Werner, Matthias, Andrej Junginger +5 · 5 citations
Computer Science · Physics and Astronomy · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications
- Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
2023/11/01 by Runa Eschenhagen, Eschenhagen, Runa, Alexander Immer +7 · 8 citations
Computer Science · #Advanced Neural Network Applications #Stochastic Gradient Optimization Techniques #Machine Learning and Data Classification
- Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
2021/07/01 by Jonathan Wenger, Geoff Pleiss, Wenger, Jonathan +7 · 5 citations
Computer Science · #Machine Learning and Data Classification #Advanced Neural Network Applications #Gaussian Processes and Bayesian Inference
- BackPACK: Packing more into backprop
2019/12/23 by Felix Dangel, Dangel, Felix, Frederik Künstner +3 · 4 citations
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Tensor decomposition and applications
- Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
2022/08/02 by Emilia Magnani, Magnani, Emilia, Nicholas Krämer +7 · 5 citations
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Reservoir Engineering and Simulation Methods
- Convergence Guarantees for Adaptive Bayesian Quadrature Methods
2019/05/24 by Motonobu Kanagawa, Kanagawa, Motonobu, Philipp Hennig +1 · 3 citations
Computer Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Numerical methods in inverse problems #Statistical and numerical algorithms
- Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective
2019/09/18 by Filip Tronarp, Hans Kersting, Simo Särkkä +1 · 3 citations
Computer Science · Engineering · #Control Systems and Identification #Gaussian Processes and Bayesian Inference #Target Tracking and Data Fusion in Sensor Networks
- High-Dimensional Gaussian Process Inference with Derivatives
2021/02/15 by Filip de Roos, Alexandra Gessner, de Roos, Filip +3 · 3 citations
Computer Science · Mathematics · Physics and Astronomy · #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Scientific Research and Discoveries
- Integrals over Gaussians under Linear Domain Constraints
2019/10/21 by Alexandra Gessner, Oindrila Kanjilal, Gessner, Alexandra +3 · 2 citations
Computer Science · Mathematics · Engineering · #Gaussian Processes and Bayesian Inference #Advanced Statistical Methods and Models #Reservoir Engineering and Simulation Methods
- Fenrir: Physics-Enhanced Regression for Initial Value Problems
2022/02/02 by Filip Tronarp, Tronarp, Filip, Nathanael Bosch +3 · 2 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
- Quasi-Newton Methods: A New Direction
2012/06/18 by Philipp Hennig, Hennig, Philipp, Martin Kiefel +1 · 1 citation
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA)
- Exact Sampling from Determinantal Point Processes
2016/09/22 by Philipp Hennig, Hennig, Philipp, Roman Garnett +1 · 1 citation
Mathematics · #Point processes and geometric inequalities #Markov Chains and Monte Carlo Methods #Random Matrices and Applications
- Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
2024/02/19 by Jonas Beck, Beck, Jonas, Nathanael Bosch +11 · 2 citations
Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks
- Probabilistic Active Learning of Functions in Structural Causal Models
2017/06/30 by Paul K. Rubenstein, Ilya Tolstikhin, Rubenstein, Paul K. +4 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Machine Learning and Algorithms #Advanced Causal Inference Techniques
- Gaussian Processes and Reproducing Kernels: Connections and Equivalences
2025/06/20 by Motonobu Kanagawa, Philipp Hennig, Kanagawa, Motonobu +5 · 3 voices · 4 citations
#stat.ML #cs.LG #math.NA #math.PR #math.ST
- FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
2024/07/18 by Tristan Cinquin, Cinquin, Tristan, Marvin Pförtner +7 · 1 voice · 2 citations
Computer Science · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #cs.AI #cs.LG
- Linearization Turns Neural Operators into Function-Valued Gaussian Processes
2024/06/07 by Emilia Magnani, Magnani, Emilia, Marvin Pförtner +5 · 2 citations
Computer Science · #FOS: Computer and information sciences #G.1.0 #G.1.8 #G.3 #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
2022/05/20 by Agustinus Kristiadi, Runa Eschenhagen, Kristiadi, Agustinus +3 · 1 citation
Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference
- Computation-Aware Kalman Filtering and Smoothing
2024/05/14 by Marvin Pförtner, Jonathan Wenger, Pförtner, Marvin +5 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Inertial Sensor and Navigation #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Target Tracking and Data Fusion in Sensor Networks
- Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
2024/11/01 by Jonathan Wenger, Wenger, Jonathan, Kaiwen Wu +9 · 1 citation
Computer Science · Engineering · #Gaussian Processes and Bayesian Inference #Fault Detection and Control Systems #Target Tracking and Data Fusion in Sensor Networks
- Debiasing Mini-Batch Quadratics for Applications in Deep Learning
2024/10/18 by Lukas Tatzel, Bálint Mucsányi, Tatzel, Lukas +5 · 1 citation
Computer Science · Physics and Astronomy · #Electromagnetic Scattering and Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matrix Theory and Algorithms