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. 2012.07278
  4. Cited By
Learning how to approve updates to machine learning algorithms in
  non-stationary settings

Learning how to approve updates to machine learning algorithms in non-stationary settings

14 December 2020
Jean Feng
ArXivPDFHTML

Papers citing "Learning how to approve updates to machine learning algorithms in non-stationary settings"

13 / 13 papers shown
Title
Approval policies for modifications to Machine Learning-Based Software
  as a Medical Device: A study of bio-creep
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep
Jean Feng
S. Emerson
N. Simon
46
20
0
28 Dec 2019
Introduction to Online Convex Optimization
Introduction to Online Convex Optimization
Elad Hazan
OffRL
83
1,919
0
07 Sep 2019
Feature Robustness in Non-stationary Health Records: Caveats to
  Deployable Model Performance in Common Clinical Machine Learning Tasks
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
Bret A. Nestor
Matthew B. A. McDermott
Willie Boag
G. Berner
Tristan Naumann
Michael C. Hughes
Anna Goldenberg
Marzyeh Ghassemi
OOD
35
109
0
02 Aug 2019
The Frontiers of Fairness in Machine Learning
The Frontiers of Fairness in Machine Learning
Alexandra Chouldechova
Aaron Roth
FaML
121
413
0
20 Oct 2018
Fairness Without Demographics in Repeated Loss Minimization
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori B. Hashimoto
Megha Srivastava
Hongseok Namkoong
Percy Liang
FaML
72
581
0
20 Jun 2018
A Review of Challenges and Opportunities in Machine Learning for Health
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi
Tristan Naumann
Peter F. Schulam
Andrew L. Beam
Irene Y. Chen
Rajesh Ranganath
18
263
0
01 Jun 2018
Delayed Impact of Fair Machine Learning
Delayed Impact of Fair Machine Learning
Lydia T. Liu
Sarah Dean
Esther Rolf
Max Simchowitz
Moritz Hardt
FaML
65
475
0
12 Mar 2018
Manipulating and Measuring Model Interpretability
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh
D. Goldstein
Jake M. Hofman
Jennifer Wortman Vaughan
Hanna M. Wallach
65
693
0
21 Feb 2018
Efficient tracking of a growing number of experts
Efficient tracking of a growing number of experts
Jaouad Mourtada
Odalric-Ambrym Maillard
39
22
0
31 Aug 2017
Selective Classification for Deep Neural Networks
Selective Classification for Deep Neural Networks
Yonatan Geifman
Ran El-Yaniv
CVBM
74
522
0
23 May 2017
Multitask learning and benchmarking with clinical time series data
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan
Hrant Khachatrian
David C. Kale
Greg Ver Steeg
Aram Galstyan
OOD
AI4TS
114
864
0
22 Mar 2017
Online Learning with Predictable Sequences
Online Learning with Predictable Sequences
Alexander Rakhlin
Karthik Sridharan
100
355
0
18 Aug 2012
Adapting to Non-stationarity with Growing Expert Ensembles
Adapting to Non-stationarity with Growing Expert Ensembles
C. Shalizi
Abigail Z. Jacobs
Kristina Lisa Klinkner
A. Clauset
AI4TS
44
26
0
04 Mar 2011
1