Learning about an exponential amount of conditional distributions

22 February 2019

Mohamed Ishmael Belghazi

Maxime Oquab

Papers citing "Learning about an exponential amount of conditional distributions"

23 / 23 papers shown

Title
Towards Universal Neural Inference Shreyas Bhat Brahmavar Yang Li Junier B. Oliva OOD 124 0 0 12 Aug 2025
Distribution Guided Active Feature Acquisition Yang Li Junier Oliva 258 1 0 04 Oct 2024
Adapting to Shifting Correlations with Unlabeled Data CalibrationEuropean Conference on Computer Vision (ECCV), 2024 Minh Le Nguyen Alan Q. Wang Heejong Kim Mert R. Sabuncu OOD 164 2 0 09 Sep 2024
Knockout: A simple way to handle missing inputs Minh Le Nguyen Batuhan K. Karaman Heejong Kim Alan Q. Wang Fengbei Liu M. Sabuncu OOD UQCV 298 3 0 30 May 2024
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use ThemData mining and knowledge discovery (DMKD), 2023 Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas FAtt 186 22 0 16 May 2023
Acquisition Conditioned Oracle for Nongreedy Active Feature AcquisitionInternational Conference on Machine Learning (ICML), 2023 M. Valancius M. Lennon Junier Oliva 135 5 0 27 Feb 2023
Model Joins: Enabling Analytics Over Joins of Absent Big Tables A. Shanghooshabad Peter Triantafillou 84 0 0 21 Jun 2022
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationEuropean Conference on Computer Vision (ECCV), 2022 Feng Wang Huiyu Wang Chen Wei Alan Yuille Wei Shen SSL VLM 232 37 0 22 Mar 2022
Posterior Matching for Arbitrary ConditioningNeural Information Processing Systems (NeurIPS), 2022 R. Strauss Junier B. Oliva CML BDL 198 7 0 28 Jan 2022
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference Sacha Lewin Gilles Louppe 238 8 0 01 Oct 2021
Partially Observed Exchangeable ModelingInternational Conference on Machine Learning (ICML), 2021 Yang Li Junier B. Oliva 98 5 0 11 Feb 2021
Arbitrary Conditional Distributions with EnergyNeural Information Processing Systems (NeurIPS), 2021 R. Strauss Junier B. Oliva 159 21 0 08 Feb 2021
Explaining by Removing: A Unified Framework for Model ExplanationJournal of machine learning research (JMLR), 2020 Ian Covert Scott M. Lundberg Su-In Lee FAtt 349 299 0 21 Nov 2020
Unsupervised Learning of Dense Visual Representations Pedro H. O. Pinheiro Amjad Almahairi Ryan Y. Benmalek Florian Golemo Aaron Courville SSL MDE 229 210 0 11 Nov 2020
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical ModelsNeural Information Processing Systems (NeurIPS), 2020 Adarsh K. Jeewajee L. Kaelbling BDL 193 1 0 09 Jul 2020
Generalized Adversarially Learned InferenceAAAI Conference on Artificial Intelligence (AAAI), 2020 Yatin Dandi Homanga Bharadhwaj Abhishek Kumar Piyush Rai GAN 128 8 0 15 Jun 2020
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference Ricardo Baptista Bamdad Hosseini Nikola B. Kovachki Youssef Marzouk OT GAN 232 32 0 11 Jun 2020
Low Distortion Block-Resampling with Spatially Stochastic Networks S. J. Hong Martín Arjovsky Darryl Barnhart Ian Thompson 122 8 0 09 Jun 2020
Shapley explainability on the data manifoldInternational Conference on Learning Representations (ICLR), 2020 Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 365 114 0 01 Jun 2020
Composing Normalizing Flows for Inverse ProblemsInternational Conference on Machine Learning (ICML), 2020 Jay Whang Erik M. Lindgren A. Dimakis TPM 257 55 0 26 Feb 2020
Masking schemes for universal marginalisers Divya Gautam Maria Lomeli Kostis Gourgoulias D. Thompson Saurabh Johri 173 1 0 16 Jan 2020
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs R. Walecki Kostis Gourgoulias Adam Baker Chris Hart Chris Lucas Max Zwiessele A. Buchard Maria Lomeli Yura N. Perov Saurabh Johri UQCV 145 0 0 16 Oct 2019
Flow Models for Arbitrary Conditional LikelihoodsInternational Conference on Machine Learning (ICML), 2019 Yongqian Li Shoaib Akbar Junier B. Oliva OOD AI4CE 163 42 0 13 Sep 2019