ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2106.09920
  4. Cited By
Being Properly Improper

Being Properly Improper

18 June 2021
Tyler Sypherd
Richard Nock
Lalitha Sankar
    FaML
ArXivPDFHTML

Papers citing "Being Properly Improper"

10 / 10 papers shown
Title
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest
  Neighbors Label Spreading
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
Nathan Stromberg
Rohan Ayyagari
Sanmi Koyejo
Richard Nock
Lalitha Sankar
46
0
0
13 Jun 2024
Boosting with Tempered Exponential Measures
Boosting with Tempered Exponential Measures
Richard Nock
Ehsan Amid
Manfred K. Warmuth
8
5
0
08 Jun 2023
Smoothly Giving up: Robustness for Simple Models
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
LegendreTron: Uprising Proper Multiclass Loss Learning
Kevin Lam
Christian J. Walder
S. Penev
Richard Nock
47
0
0
27 Jan 2023
AugLoss: A Robust Augmentation-based Fine Tuning Methodology
AugLoss: A Robust Augmentation-based Fine Tuning Methodology
Kyle Otstot
J. Cava
Tyler Sypherd
Lalitha Sankar
21
0
0
05 Jun 2022
What killed the Convex Booster ?
What killed the Convex Booster ?
Yishay Mansour
Richard Nock
Robert C. Williamson
16
1
0
19 May 2022
Fair Wrapping for Black-box Predictions
Fair Wrapping for Black-box Predictions
Alexander Soen
Ibrahim M. Alabdulmohsin
Sanmi Koyejo
Yishay Mansour
Nyalleng Moorosi
Richard Nock
Ke Sun
Lexing Xie
FaML
51
6
0
31 Jan 2022
Generative Trees: Adversarial and Copycat
Generative Trees: Adversarial and Copycat
Richard Nock
Mathieu Guillame-Bert
24
5
0
26 Jan 2022
On Tilted Losses in Machine Learning: Theory and Applications
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li
Ahmad Beirami
Maziar Sanjabi
Virginia Smith
55
43
0
13 Sep 2021
A Tunable Loss Function for Robust Classification: Calibration,
  Landscape, and Generalization
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
Tyler Sypherd
Mario Díaz
J. Cava
Gautam Dasarathy
Peter Kairouz
Lalitha Sankar
15
27
0
05 Jun 2019
1