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Removing Biases from Molecular Representations via Information
  Maximization

Removing Biases from Molecular Representations via Information Maximization

1 December 2023
Chenyu Wang
Sharut Gupta
Caroline Uhler
Tommi Jaakkola
ArXiv (abs)PDFHTMLGithub (20★)

Papers citing "Removing Biases from Molecular Representations via Information Maximization"

20 / 20 papers shown
Title
An Information Criterion for Controlled Disentanglement of Multimodal Data
An Information Criterion for Controlled Disentanglement of Multimodal Data
Chenyu Wang
Sharut Gupta
Xinyi Zhang
Sana Tonekaboni
Stefanie Jegelka
Tommi Jaakkola
Caroline Uhler
DRL
100
2
0
31 Oct 2024
Molecule-Morphology Contrastive Pretraining for Transferable Molecular
  Representation
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
Cuong Q. Nguyen
Dante A. Pertusi
K. Branson
AI4CE
37
12
0
27 Apr 2023
Conditional Contrastive Learning with Kernel
Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai
Tianqi Li
Martin Q. Ma
Han Zhao
Kun Zhang
Louis-Philippe Morency
Ruslan Salakhutdinov
61
26
0
11 Feb 2022
Discovering Invariant Rationales for Graph Neural Networks
Discovering Invariant Rationales for Graph Neural Networks
Yingmin Wu
Xiang Wang
An Zhang
Xiangnan He
Tat-Seng Chua
OODAI4CE
170
234
0
30 Jan 2022
3D Infomax improves GNNs for Molecular Property Prediction
3D Infomax improves GNNs for Molecular Property Prediction
Hannes Stärk
Dominique Beaini
Gabriele Corso
Prudencio Tossou
Christian Dallago
Stephan Günnemann
Pietro Lio
AI4CE
92
208
0
08 Oct 2021
Can contrastive learning avoid shortcut solutions?
Can contrastive learning avoid shortcut solutions?
Joshua Robinson
Li Sun
Ke Yu
Kayhan Batmanghelich
Stefanie Jegelka
S. Sra
SSL
100
146
0
21 Jun 2021
Self-supervised Graph-level Representation Learning with Local and
  Global Structure
Self-supervised Graph-level Representation Learning with Local and Global Structure
Minghao Xu
Hang Wang
Bingbing Ni
Hongyu Guo
Jian Tang
SSL
52
212
0
08 Jun 2021
Fairness without Demographics through Adversarially Reweighted Learning
Fairness without Demographics through Adversarially Reweighted Learning
Preethi Lahoti
Alex Beutel
Jilin Chen
Kang Lee
Flavien Prost
Nithum Thain
Xuezhi Wang
Ed H. Chi
FaML
131
338
0
23 Jun 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
J. Leskovec
311
2,752
0
02 May 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
390
18,897
0
13 Feb 2020
Momentum Contrast for Unsupervised Visual Representation Learning
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He
Haoqi Fan
Yuxin Wu
Saining Xie
Ross B. Girshick
SSL
216
12,136
0
13 Nov 2019
Inherent Tradeoffs in Learning Fair Representations
Inherent Tradeoffs in Learning Fair Representations
Han Zhao
Geoffrey J. Gordon
FaML
70
218
0
19 Jun 2019
Contrastive Multiview Coding
Contrastive Multiview Coding
Yonglong Tian
Dilip Krishnan
Phillip Isola
SSL
182
2,409
0
13 Jun 2019
Learning Representations by Maximizing Mutual Information Across Views
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman
R. Devon Hjelm
William Buchwalter
SSL
195
1,479
0
03 Jun 2019
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
316
9,815
0
25 Oct 2017
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
343
1,838
0
02 Mar 2017
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
236
4,341
0
07 Oct 2016
Certifying and removing disparate impact
Certifying and removing disparate impact
Michael Feldman
Sorelle A. Friedler
John Moeller
C. Scheidegger
Suresh Venkatasubramanian
FaML
212
1,996
0
11 Dec 2014
Unsupervised Domain Adaptation by Backpropagation
Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin
Victor Lempitsky
OOD
249
6,043
0
26 Sep 2014
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OODSSL
286
12,460
0
24 Jun 2012
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