Estimating Gradients for Discrete Random Variables by Sampling without Replacement

14 February 2020

Papers citing "Estimating Gradients for Discrete Random Variables by Sampling without Replacement"

35 / 35 papers shown

Title
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement Jonathan Pirnay D. G. Grimm 42 10 0 22 Mar 2024
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick Lennert De Smet Emanuele Sansone Pedro Zuidberg Dos Martires 22 11 0 21 Nov 2023
DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation Pau Mulet Arabí Alec Flowers Lukas Mauch Fabien Cardinaux BDL 19 0 0 07 Sep 2023
On Many-Actions Policy Gradient Michal Nauman Marek Cygan 9 0 0 24 Oct 2022
Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models Pasquale Minervini Luca Franceschi Mathias Niepert 38 11 0 11 Sep 2022
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks Yifan Chen Tianning Xu Dilek Z. Hakkani-Tür Di Jin Yun Yang Ruoqing Zhu 13 4 0 01 Jun 2022
Sparse Graph Learning from Spatiotemporal Time Series Andrea Cini Daniele Zambon C. Alippi CML AI4TS 35 18 0 26 May 2022
Unbiased and Efficient Sampling of Dependency Trees Milovs Stanojević 18 3 0 25 May 2022
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies Alon Berliner Guy Rotman Yossi Adi Roi Reichart Tamir Hazan BDL DRL 24 4 0 03 May 2022
Learning Group Importance using the Differentiable Hypergeometric Distribution Thomas M. Sutter Laura Manduchi Alain Ryser Julia E. Vogt 36 7 0 03 Mar 2022
Gradient Estimation with Discrete Stein Operators Jiaxin Shi Yuhao Zhou Jessica Hwang Michalis K. Titsias Lester W. Mackey 17 22 0 19 Feb 2022
Bayesian Nonparametrics for Offline Skill Discovery Valentin Villecroze H. Braviner Panteha Naderian Chris J. Maddison G. Loaiza-Ganem BDL OffRL 14 8 0 09 Feb 2022
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation Honguk Woo Hyunsung Lee Sangwook Cho 11 5 0 24 Dec 2021
Scaling Structured Inference with Randomization Yao Fu John P. Cunningham Mirella Lapata BDL 27 2 0 07 Dec 2021
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator Alek Dimitriev Mingyuan Zhou 6 7 0 26 Oct 2021
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning Iris A. M. Huijben W. Kool Max B. Paulus Ruud J. G. van Sloun 24 92 0 04 Oct 2021
Conditional Poisson Stochastic Beam Search Clara Meister Afra Amini Tim Vieira Ryan Cotterell 24 10 0 22 Sep 2021
Coupled Gradient Estimators for Discrete Latent Variables Zhe Dong A. Mnih George Tucker BDL 22 13 0 15 Jun 2021
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions Mathias Niepert Pasquale Minervini Luca Franceschi 24 81 0 03 Jun 2021
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile van Krieken Jakub M. Tomczak A. T. Teije ODL OffRL 21 15 0 01 Apr 2021
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents E. Guiraud Jakob Drefs Jörg Lücke DRL 25 3 0 27 Nov 2020
Experimental design for MRI by greedy policy search Tim Bakker H. V. Hoof Max Welling 20 55 0 30 Oct 2020
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 35 98 0 22 Oct 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 127 48 0 20 Oct 2020
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator Max B. Paulus Chris J. Maddison Andreas Krause BDL 34 38 0 09 Oct 2020
Learning from eXtreme Bandit Feedback Romain Lopez Inderjit S. Dhillon Michael I. Jordan OffRL 20 25 0 27 Sep 2020
Natural Reweighted Wake-Sleep Csongor-Huba Várady Riccardo Volpi Luigi Malagò Nihat Ay BDL 11 0 0 15 Aug 2020
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling Mark Bun Jörg Drechsler Marco Gaboardi Audra McMillan Jayshree Sarathy 29 7 0 24 Jul 2020
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity Gonçalo M. Correia Vlad Niculae Wilker Aziz André F. T. Martins BDL 14 22 0 03 Jul 2020
Gradient Estimation with Stochastic Softmax Tricks Max B. Paulus Dami Choi Daniel Tarlow Andreas Krause Chris J. Maddison BDL 34 85 0 15 Jun 2020
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model Jiaqi Ma Xinyang Yi Weijing Tang Zhe Zhao Lichan Hong Ed H. Chi Qiaozhu Mei 14 12 0 09 Jun 2020
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models Zhijian Ou Yunfu Song BDL 13 8 0 28 May 2020
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables Weonyoung Joo Dongjun Kim Seung-Jae Shin Il-Chul Moon 21 1 0 04 Mar 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 16 29 0 19 Dec 2019
Classical Structured Prediction Losses for Sequence to Sequence Learning Sergey Edunov Myle Ott Michael Auli David Grangier MarcÁurelio Ranzato AIMat 48 185 0 14 Nov 2017