Papers citing 'Gaussian Process Conditional Density Estimation'

Title
Conditional Density Estimation with Histogram TreesNeural Information Processing Systems (NeurIPS), 2024 Lincen Yang Matthijs van Leeuwen 176 2 0 15 Oct 2024
Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution EstimationInternational Conference on Learning Representations (ICLR), 2024 Rong Tang Lizhen Lin Yun Yang DiffM 191 4 0 30 Sep 2024
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation LearningAAAI Conference on Artificial Intelligence (AAAI), 2024 Rishabh Agrawal Nathan Dahlin Rahul Jain Ashutosh Nayyar OffRL 203 1 0 17 Aug 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 431 6 0 22 Feb 2024
Conditional Kernel Imitation Learning for Continuous State EnvironmentsConference on Learning for Dynamics & Control (L4DC), 2023 Rishabh Agrawal Nathan Dahlin Rahul Jain A. Nayyar 186 1 0 24 Aug 2023
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural NetworksNeural Information Processing Systems (NeurIPS), 2023 Ziyi Huang Henry Lam Haofeng Zhang UQCV 252 9 0 09 Jun 2023
Bivariate Causal Discovery using Bayesian Model SelectionInternational Conference on Machine Learning (ICML), 2023 Anish Dhir Samuel Power Mark van der Wilk CML 250 6 0 05 Jun 2023
Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam Didong Li Jiayu Yao Diana Cai Andrew Jones Barbara E. Engelhardt OffRL BDL 295 3 0 13 Mar 2023
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learningJournal of Computational Physics (JCP), 2023 Ling Guo Hao Wu Wenwen Zhou Yan Wang Tao Zhou UQCV 292 21 0 07 Feb 2023
Evaluating Aleatoric Uncertainty via Conditional Generative Models Ziyi Huang Henry Lam Haofeng Zhang PER UD 137 6 0 09 Jun 2022
DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probesPhysical Review X (PRX), 2022 T. Kacprzak J. Fluri 159 16 0 17 Mar 2022
Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing FlowEuropean Signal Processing Conference (EUSIPCO), 2022 Xiongjie Chen Yunpeng Li 175 8 0 16 Mar 2022
Non-Gaussian Gaussian Processes for Few-Shot Regression Marcin Sendera Jacek Tabor A. Nowak Andrzej Bedychaj Massimiliano Patacchiola Tomasz Trzciñski Przemysław Spurek Maciej Ziȩba 192 20 0 26 Oct 2021
Quantifying Epistemic Uncertainty in Deep Learning Ziyi Huang Henry Lam Haofeng Zhang UQCV BDL UD PER 397 15 0 23 Oct 2021
Probabilistic Time Series Forecasts with Autoregressive Transformation Models David Rügamer Philipp F. M. Baumann Thomas Kneib Torsten Hothorn AI4TS 337 13 0 15 Oct 2021
Deconvolutional Density Network: Modeling Free-Form Conditional DistributionsAAAI Conference on Artificial Intelligence (AAAI), 2021 Bing Chen Mazharul Islam Jisuo Gao Lin Wang BDL CML 241 8 0 29 May 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 205 27 0 12 Apr 2021
Continuous Conditional Generative Adversarial Networks (cGAN) with Generator Regularization Yufeng Zheng Yunkai Zhang Zeyu Zheng GAN 112 9 0 27 Mar 2021
Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services Léa Ricard G. Desaulniers Andrea Lodi Louis-Martin Rousseau AI4TS 61 2 0 03 Feb 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 1.1K 61 0 27 Dec 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 368 35 0 03 Nov 2020
Conditional Density Estimation via Weighted Logistic Regressions Yiping Guo H. Bondell 68 0 0 21 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel LearningNeural Information Processing Systems (NeurIPS), 2020 A. Tompkins Rafael Oliveira F. Ramos 215 6 0 09 Oct 2020
Modulating Scalable Gaussian Processes for Expressive Statistical LearningPattern Recognition (Pattern Recognit.), 2020 Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang 155 4 0 29 Aug 2020
CD-split and HPD-split: efficient conformal regions in high dimensionsJournal of machine learning research (JMLR), 2020 Rafael Izbicki Gilson T. Shimizu R. Stern 633 70 0 24 Jul 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 233 10 0 18 Jun 2020
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects Qiming Du Gérard Biau Franccois Petit R. Porcher CML BDL 181 7 0 08 Jun 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 236 99 0 02 Mar 2020
Testing Goodness of Fit of Conditional Density Models with KernelsConference on Uncertainty in Artificial Intelligence (UAI), 2020 Wittawat Jitkrittum Heishiro Kanagawa Bernhard Schölkopf 186 29 0 24 Feb 2020
Noise Regularization for Conditional Density Estimation Jonas Rothfuss Fabio Ferreira S. Boehm Simon Walther Maxim Ulrich Tamim Asfour Andreas Krause 141 36 0 21 Jul 2019
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean EmbeddingsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Jean-François Ton Lucian Chan Yee Whye Teh Dino Sejdinovic 121 13 0 05 Jun 2019
Kernel Instrumental Variable RegressionNeural Information Processing Systems (NeurIPS), 2019 Rahul Singh M. Sahani Arthur Gretton 608 190 0 01 Jun 2019
Learning with Succinct Common Representation Based on Wyner's Common Information J. Jon Ryu Yoojin Choi Young-Han Kim Mostafa El-Khamy Jungwon Lee DRL GAN 94 3 0 27 May 2019
Deep Gaussian Processes with Importance-Weighted Variational InferenceInternational Conference on Machine Learning (ICML), 2019 Hugh Salimbeni Vincent Dutordoir J. Hensman M. Deisenroth BDL 213 45 0 14 May 2019
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu Yew-Soon Ong Jianfei Cai BDL 264 30 0 03 Nov 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018 Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 379 801 0 03 Jul 2018