Super-efficiency of automatic differentiation for functions defined as a
minimum

Super-efficiency of automatic differentiation for functions defined as a minimum

10 February 2020

Pierre Ablin

Papers citing "Super-efficiency of automatic differentiation for functions defined as a minimum"

9 / 9 papers shown

Title
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models Alexander Lin Bahareh Tolooshams Yves Atchadé Demba E. Ba 36 1 0 05 Jun 2023
Achieving Hierarchy-Free Approximation for Bilevel Programs With Equilibrium Constraints Jiayang Li Jiahao Yu Boyi Liu Zhaoran Wang Y. Nie 35 6 0 20 Feb 2023
Automatic differentiation of nonsmooth iterative algorithms Jérôme Bolte Edouard Pauwels Samuel Vaiter 23 22 0 31 May 2022
Trajectory Inference via Mean-field Langevin in Path Space Lénaïc Chizat Stephen X. Zhang Matthieu Heitz Geoffrey Schiebinger 34 22 0 14 May 2022
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation Yan Zhang David W. Zhang Simon Lacoste-Julien Gertjan J. Burghouts Cees G. M. Snoek BDL 43 21 0 23 Nov 2021
Gradients are Not All You Need Luke Metz C. Freeman S. Schoenholz Tal Kachman 30 93 0 10 Nov 2021
Understanding approximate and unrolled dictionary learning for pattern recovery Benoit Malézieux Thomas Moreau M. Kowalski MU 22 10 0 11 Jun 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization Valerii Likhosherstov Xingyou Song K. Choromanski Jared Davis Adrian Weller AI4CE 19 5 0 04 Jun 2021
Convergence Properties of Stochastic Hypergradients Riccardo Grazzi Massimiliano Pontil Saverio Salzo 22 26 0 13 Nov 2020