What You See is What You Get: Principled Deep Learning via Distributional Generalization

7 April 2022

Papers citing "What You See is What You Get: Principled Deep Learning via Distributional Generalization"

7 / 7 papers shown

Title
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 33 3 0 01 Jul 2023
Certified Robust Neural Networks: Generalization and Corruption Resistance Amine Bennouna Ryan Lucas Bart P. G. Van Parys 38 10 0 03 Mar 2023
On the interplay of adversarial robustness and architecture components: patches, convolution and attention Francesco Croce Matthias Hein 41 6 0 14 Sep 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 168 350 0 25 Sep 2021
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 59 86 0 25 Aug 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 152 371 0 09 May 2020