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Black-box $α$ -divergence Minimization

10 November 2015

José Miguel Hernández-Lobato

Yingzhen Li

Mark Rowland

Daniel Hernández-Lobato

T. Bui

Richard Turner

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Papers citing "Black-box $α$-divergence Minimization"

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Trusted Fake Audio Detection Based on Dirichlet Distribution Chi Ding Junxiao Xue Cong Wang Hao Zhou 138 0 0 03 Jun 2025
Stability-based Generalization Bounds for Variational Inference Yadi Wei Roni Khardon BDL 192 0 0 17 Feb 2025
Alpha Entropy Search for New Information-based Bayesian OptimizationKnowledge-Based Systems (KBS), 2024 Daniel Fernández-Sánchez Eduardo C. Garrido-Merchán Daniel Hernández-Lobato 244 2 0 25 Nov 2024
Bayesian neural networks for predicting uncertainty in full-field material response G. Pasparakis Lori Graham-Brady Michael D. Shields AI4CE 188 12 0 21 Jun 2024
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution Johannes Zenn Kushagra Pandey 148 0 0 23 May 2024
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding Lingdong Kong Xiang Xu Jun Cen Wenwei Zhang Liang Pan Kai-xiang Chen Ziwei Liu 260 12 0 25 Mar 2024
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs C. Margossian Loucas Pillaud-Vivien Lawrence K. Saul UD 457 6 0 20 Mar 2024
Forward $χ^2$ Divergence Based Variational Importance SamplingInternational Conference on Learning Representations (ICLR), 2023 Chengrui Li Yule Wang Weihan Li Anqi Wu BDL 156 2 0 04 Nov 2023
Adaptive importance sampling for heavy-tailed distributions via $α$ -divergence minimizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Thomas Guilmeau Nicola Branchini Émilie Chouzenoux Victor Elvira 180 3 0 25 Oct 2023
Learning variational autoencoders via MCMC speed measuresStatistics and computing (Stat. Comput.), 2023 Marcel Hirt Vasileios Kreouzis P. Dellaportas BDL DRL 130 2 0 26 Aug 2023
Transport meets Variational Inference: Controlled Monte Carlo DiffusionsInternational Conference on Learning Representations (ICLR), 2023 Francisco Vargas Shreyas Padhy Denis Blessing Nikolas Nusken DiffM OT 685 12 0 03 Jul 2023
Parameter Estimation in DAGs from Incomplete Data via Optimal TransportInternational Conference on Machine Learning (ICML), 2023 Vy Vo Trung Le L. Vuong He Zhao Edwin V. Bonilla Dinh Q. Phung OT 226 4 0 25 May 2023
On the Convergence of Black-Box Variational InferenceNeural Information Processing Systems (NeurIPS), 2023 Kyurae Kim Jisu Oh Kaiwen Wu Yi-An Ma Jacob R. Gardner BDL 292 20 0 24 May 2023
Nonlinear Kalman Filtering with Reparametrization GradientsInternational Conference Frontiers Signal Processing (ICFSP), 2023 San Gultekin B. Kitts A. Flores John Paisley 84 1 0 08 Mar 2023
Variational Bayesian Neural Networks via Resolution of SingularitiesJournal of Computational And Graphical Statistics (JCGS), 2023 Susan Wei Edmund Lau BDL 186 2 0 13 Feb 2023
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 304 4 0 09 Nov 2022
GFlowNets and variational inferenceInternational Conference on Learning Representations (ICLR), 2022 Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 390 102 0 02 Oct 2022
Correcting Model Bias with Sparse Implicit Processes Simón Rodríguez Santana Luis A. Ortega Andrés Daniel Hernández-Lobato B. Zaldívar BDL 103 1 0 21 Jul 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep LearningInternational Conference on Machine Learning (ICML), 2022 Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 220 33 0 17 Jun 2022
Deep Variational Implicit ProcessesInternational Conference on Learning Representations (ICLR), 2022 Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL 183 6 0 14 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian GradientsNeural Information Processing Systems (NeurIPS), 2022 Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 288 8 0 13 Jun 2022
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkNeural Information Processing Systems (NeurIPS), 2022 Ziyi Huang Henry Lam A. Meisami Haofeng Zhang 302 4 0 31 Jan 2022
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution ShiftThe Web Conference (WWW), 2022 Hongrui Liu Binbin Hu Xiao Wang Chuan Shi Qing Cui Jun Zhou 256 62 0 27 Jan 2022
Variational Inference with Holder Bounds Junya Chen Danni Lu Zidi Xiu Ke Bai Lawrence Carin Chenyang Tao 107 7 0 04 Nov 2021
Function-space Inference with Sparse Implicit Processes Simón Rodríguez Santana B. Zaldívar Daniel Hernández-Lobato 141 13 0 14 Oct 2021
Challenges and Opportunities in High-dimensional Variational InferenceNeural Information Processing Systems (NeurIPS), 2021 Akash Kumar Dhaka Alejandro Catalina Manushi K. V. Welandawe Michael Riis Andersen Jonathan H. Huggins Aki Vehtari 157 47 0 01 Mar 2021
On the Difficulty of Unbiased Alpha Divergence MinimizationInternational Conference on Machine Learning (ICML), 2020 Tomas Geffner Justin Domke 283 18 0 19 Oct 2020
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures Benjamin Kompa Jasper Snoek Andrew L. Beam UQCV BDL 261 33 0 06 Oct 2020
Gaussian Process Molecule Property Prediction with FlowMO Henry B. Moss Ryan-Rhys Griffiths 255 29 0 02 Oct 2020
Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point UpdatesNeural Computation (Neural Comput.), 2020 Chen Zeno Itay Golan Elad Hoffer Daniel Soudry OOD FedML 201 49 0 01 Oct 2020
Bridging Maximum Likelihood and Adversarial Learning via $α$ -DivergenceAAAI Conference on Artificial Intelligence (AAAI), 2020 Miaoyun Zhao Yulai Cong Shuyang Dai Lawrence Carin GAN 100 10 0 13 Jul 2020
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes Ryan-Rhys Griffiths Jake L. Greenfield Aditya R. Thawani Arian R. Jamasb Henry B. Moss Anthony Bourached Penelope Jones William McCorkindale Alexander A. Aldrick Matthew J. Fuchter Alpha A. Lee 256 18 0 28 Jun 2020
Infinite-dimensional gradient-based descent for alpha-divergence minimisation Kamélia Daudel Randal Douc Franccois Portier 218 18 0 20 May 2020
Scalable Approximate Inference and Some Applications Jun Han BDL 143 1 0 07 Mar 2020
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2019 Rui Zhang Christian J. Walder Edwin V. Bonilla Marian-Andrei Rizoiu Lexing Xie 180 2 0 21 Dec 2019
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural NetworkIEEE International Conference on Robotics and Automation (ICRA), 2019 Chen Tang Jianyu Chen Masayoshi Tomizuka 90 17 0 11 Nov 2019
Parametric Gaussian Process Regressors M. Jankowiak Geoffrey Pleiss Jacob R. Gardner UQCV 200 6 0 16 Oct 2019
Validated Variational Inference via Practical Posterior Error BoundsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Jonathan H. Huggins Mikolaj Kasprzak Trevor Campbell Tamara Broderick 308 41 0 09 Oct 2019
The $f$ -Divergence Expectation Iteration Scheme Kamélia Daudel Randal Douc Franccois Portier François Roueff 207 1 0 26 Sep 2019
Adversarial $α$ -divergence Minimization for Bayesian Approximate Inference Simón Rodríguez Santana Daniel Hernández-Lobato UQCV BDL 142 8 0 13 Sep 2019
On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Y. K. Foong David R. Burt Yingzhen Li Richard Turner UQCV BDL 171 20 0 02 Sep 2019
Generalized Variational Inference: Three arguments for deriving new Posteriors Jeremias Knoblauch Jack Jewson Theodoros Damoulas DRL BDL 356 114 0 03 Apr 2019
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks K. Posch J. Pilz UQCV BDL 165 30 0 02 Apr 2019
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning Andreas Sedlmeier Thomas Gabor Thomy Phan Lenz Belzner Claudia Linnhoff-Popien UQCV 156 27 0 08 Jan 2019
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 185 57 0 27 Nov 2018
Model-Based Reinforcement Learning for Sepsis Treatment Aniruddh Raghu Matthieu Komorowski Sumeetpal S. Singh OffRL LM&MA 99 55 0 23 Nov 2018
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights Melanie F. Pradier Weiwei Pan Jiayu Yao S. Ghosh Finale Doshi-velez UQCV BDL 189 10 0 16 Nov 2018
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods Caroline Werther M. Ferguson K. Park Cuixian Chen Stephen J. Roberts ODL 142 4 0 08 Nov 2018
Tuning Fairness by Balancing Target Labels T. Kehrenberg Zexun Chen Novi Quadrianto 267 4 0 12 Oct 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 135 81 0 13 Jun 2018

Black-box ααα-divergence Minimization

Papers citing "Black-box $α$-divergence Minimization"

Black-box $α$ -divergence Minimization