(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy

1 June 2023

Papers citing "(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy"

9 / 9 papers shown

Title
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach Dheeraj Baby Boran Han Shuai Zhang Cuixiong Hu Yuyang Wang Yu-Xiang Wang OOD 48 0 0 09 Apr 2025
Source-Free Domain-Invariant Performance Prediction Ekaterina Khramtsova Mahsa Baktashmotlagh Guido Zuccon Xi Wang Mathieu Salzmann UQCV 40 1 0 05 Aug 2024
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift Mona Schirmer Dan Zhang Eric T. Nalisnick 13 0 0 13 Dec 2023
One-Shot Strategic Classification Under Unknown Costs Elan Rosenfeld Nir Rosenfeld OffRL 33 7 0 05 Nov 2023
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 249 36,356 0 25 Aug 2016
Domain-Adversarial Training of Neural Networks Yaroslav Ganin E. Ustinova Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette M. Marchand Victor Lempitsky GAN OOD 165 9,327 0 28 May 2015
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 114 577 0 27 Feb 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 284 39,190 0 01 Sep 2014
Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour M. Mohri Afshin Rostamizadeh 179 789 0 19 Feb 2009