A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

5 June 2019

Papers citing "A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization"

9 / 9 papers shown

Title
An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification Hyenkyun Woo 20 0 0 26 Dec 2023
Smoothly Giving up: Robustness for Simple Models Tyler Sypherd Nathan Stromberg Richard Nock Visar Berisha Lalitha Sankar 21 1 0 17 Feb 2023
LegendreTron: Uprising Proper Multiclass Loss Learning Kevin Lam Christian J. Walder S. Penev Richard Nock 52 0 0 27 Jan 2023
Robust PAC $^m$ : Training Ensemble Models Under Misspecification and Outliers Matteo Zecchin Sangwoo Park Osvaldo Simeone Marios Kountouris David Gesbert 14 5 0 03 Mar 2022
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift Gholamali Aminian Mahed Abroshan Mohammad Mahdi Khalili Laura Toni M. Rodrigues OOD 28 27 0 24 Feb 2022
On Tilted Losses in Machine Learning: Theory and Applications Tian Li Ahmad Beirami Maziar Sanjabi Virginia Smith 55 38 0 13 Sep 2021
Being Properly Improper Tyler Sypherd Richard Nock Lalitha Sankar FaML 39 10 0 18 Jun 2021
Realizing GANs via a Tunable Loss Function Gowtham R. Kurri Tyler Sypherd Lalitha Sankar GAN 8 15 0 09 Jun 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 227 348 0 14 Jun 2018