Fetching the paper…
Reading the bibliography…
Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.g., images), studies on graph data are still limited.
A nonparametric estimation of the entropy for absolutely continuous distributions (corresp.)
I. Ahmad and P.-E. Lin · 1976
Earlier work this paper cites.
Principles of risk minimization for learning theory
V. Vapnik · 1991
Earlier work this paper cites.
Minimum impurity partitions
D. Burshtein, V. D. Pietra, D. Kanevsky, and A. Nadas · 1992
Earlier work this paper cites.
The art and practice of structure-based drug design: A molecular modeling perspective
R. S. Bohacek, C. McMartin, and W. C. Guida · 1996
Earlier work this paper cites.
Estimation and prediction for stochastic blockmodels for graphs with latent block structure
T. A. Snijders and K. Nowicki · 1997
Earlier work this paper cites.
Fisher discriminant analysis with kernels
S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Mullers · 1999
Earlier work this paper cites.
The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek · 1999
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
M. McPherson, L. Smith-Lovin, and J. M. Cook · 2001
Earlier work this paper cites.
An introduction to kernel-based learning algorithms
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf · 2001
Earlier work this paper cites.
A tutorial on ν \nu -support vector machines
P.-H. Chen, C.-J. Lin, and B. Schölkopf · 2005
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle, B. Schölkopf, and A. Zien · 2006
Earlier work this paper cites.
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
T. M. Cover and J. A. Thomas · 2006
Earlier work this paper cites.
Limits of dense graph sequences
L. Lovász and B. Szegedy · 2006
Earlier work this paper cites.
Learning a nonlinear embedding by preserving class neighbourhood structure
R. Salakhutdinov and G. E. Hinton · 2007
Earlier work this paper cites.
Information Theory and Network Coding
R. Yeung · 2008
Earlier work this paper cites.
Causality
J. Pearl · 2009
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts · 2013
Earlier work this paper cites.
Adaptive recursive neural network for target-dependent twitter sentiment classification
L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Nonparametric von mises estimators for entropies, divergences and mutual informations
K. Kandasamy, A. Krishnamurthy, B. Poczos, L. Wasserman, and j. m. robins · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Zinc 15 – ligand discovery for everyone
T. Sterling and J. J. Irwin · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. S. Lempitsky · 2016
Earlier work this paper cites.
Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Stochastic gradient methods for distributionally robust optimization with f-divergences
H. Namkoong and J. C. Duchi · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
Earlier work this paper cites.
Deep CORAL: correlation alignment for deep domain adaptation
B. Sun and K. Saenko · 2016
Earlier work this paper cites.
Deep variational information bottleneck
A. A. Alemi, I. Fischer, and J. V. D. and · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schlkopf · 2017
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
Earlier work this paper cites.
Recognition in terra incognita
S. Beery, G. V. Horn, and P. Perona · 2018
Earlier work this paper cites.
Mutual information neural estimation
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm · 2018
Earlier work this paper cites.
Allennlp: A deep semantic natural language processing platform
M. Gardner, J. Grus, M. Neumann, O. Tafjord, P. Dasigi, N. F. Liu, M. E. Peters, M. Schmitz, and L. Zettlemoyer · 2018
Earlier work this paper cites.
Invariant models for causal transfer learning
M. Rojas-Carulla, B. Schölkopf, R. Turner, and J. Peters · 2018
Earlier work this paper cites.
Graph networks as learnable physics engines for inference and control
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. A. Riedmiller, R. Hadsell, and P. W. Battaglia · 2018
Cited alongside, same era.
Measuring abstract reasoning in neural networks
A. Santoro, F. Hill, D. G. T. Barrett, A. S. Morcos, and T. P. Lillicrap · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization
O. Sener and V. Koltun · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. van den Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Neural execution of graph algorithms
P. Velickovic, R. Ying, M. Padovano, R. Hadsell, and C. Blundell · 2020
Later among the works it cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
T. Wang and P. Isola · 2020
Later among the works it cites.
What can neural networks reason about?
K. Xu, J. Li, M. Zhang, S. S. Du, K. Kawarabayashi, and S. Jegelka · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen · 2020
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
H. Yuan, H. Yu, S. Gui, and S. Ji · 2020
Later among the works it cites.
Invariance principle meets information bottleneck for out-of-distribution generalization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
Graphrnn: Generating realistic graphs with deep auto-regressive models
J. You, R. Ying, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Q. Dou, D. C. de Castro, K. Kamnitsas, and B. Glocker · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
Cited alongside, same era.
K. Ahuja, E. Caballero, D. Zhang, J.-C. Gagnon-Audet, Y. Bengio, I. Mitliagkas, and I. Rish · 2021
Later among the works it cites.
Linear unit-tests for invariance discovery
B. Aubin, A. Słowik, M. Arjovsky, L. Bottou, and D. Lopez-Paz · 2021
Later among the works it cites.
Size-invariant graph representations for graph classification extrapolations
B. Bevilacqua, Y. Zhou, and B. Ribeiro · 2021
Later among the works it cites.
Environment inference for invariant learning
E. Creager, J. Jacobsen, and R. S. Zemel · 2021
Later among the works it cites.
AI for radiographic COVID-19 detection selects shortcuts over signal
A. J. DeGrave, J. D. Janizek, and S. Lee · 2021
Later among the works it cites.
In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
Later among the works it cites.
Reliable graph neural networks for drug discovery under distributional shift
K. Han, B. Lakshminarayanan, and J. Z. Liu · 2021
Later among the works it cites.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. H. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik · 2021
Later among the works it cites.
WILDS: A benchmark of in-the-wild distribution shifts
P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, T. Lee, E. David, I. Stavness, W. Guo, B. Earnshaw, I. Haque, S. M. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2021
Later among the works it cites.
Out-of-distribution generalization via risk extrapolation (rex)
D. Krueger, E. Caballero, J. Jacobsen, A. Zhang, J. Binas, D. Zhang, R. L. Priol, and A. C. Courville · 2021
Later among the works it cites.
Self-supervised learning with data augmentations provably isolates content from style
J. V. Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, and F. Locatello · 2021
Later among the works it cites.
Generative causal explanations for graph neural networks
W. Lin, H. Lan, and B. Li · 2021
Later among the works it cites.
Graphdf: A discrete flow model for molecular graph generation
Y. Luo, K. Yan, and S. Ji · 2021
Later among the works it cites.
Improving graph representation learning by contrastive regularization
K. Ma, H. Yang, H. Yang, T. Jin, P. Chen, Y. Chen, B. F. Kamhoua, and J. Cheng · 2021
Later among the works it cites.
Domain generalization using causal matching
D. Mahajan, S. Tople, and A. Sharma · 2021
Later among the works it cites.
Weisfeiler and leman go machine learning: The story so far
C. Morris, Y. Lipman, H. Maron, B. Rieck, N. M. Kriege, M. Grohe, M. Fey, and K. M. Borgwardt · 2021
Later among the works it cites.
Understanding the failure modes of out-of-distribution generalization
V. Nagarajan, A. Andreassen, and B. Neyshabur · 2021
Later among the works it cites.
The risks of invariant risk minimization
E. Rosenfeld, P. K. Ravikumar, and A. Risteski · 2021
Later among the works it cites.
Toward causal representation learning
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio · 2021
Later among the works it cites.
Generalizing to unseen domains: A survey on domain generalization
J. Wang, C. Lan, C. Liu, Y. Ouyang, and T. Qin · 2021
Later among the works it cites.
How to transfer algorithmic reasoning knowledge to learn new algorithms?
L.-P. A. C. Xhonneux, A. Deac, P. Veličković, and J. Tang · 2021
Later among the works it cites.
Rethinking graph regularization for graph neural networks
H. Yang, K. Ma, and J. Cheng · 2021
Later among the works it cites.
From local structures to size generalization in graph neural networks
G. Yehudai, E. Fetaya, E. Meirom, G. Chechik, and H. Maron · 2021
Later among the works it cites.
Graph contrastive learning automated
Y. You, T. Chen, Y. Shen, and Z. Wang · 2021
Later among the works it cites.
Graph information bottleneck for subgraph recognition
J. Yu, T. Xu, Y. Rong, Y. Bian, J. Huang, and R. He · 2021
Later among the works it cites.
Contrastive learning inverts the data generating process
R. S. Zimmermann, Y. Sharma, S. Schneider, M. Bethge, and W. Brendel · 2021
Later among the works it cites.
Independent SE(3)-equivariant models for end-to-end rigid protein docking
O.-E. Ganea, X. Huang, C. Bunne, Y. Bian, R. Barzilay, T. S. Jaakkola, and A. Krause · 2022
Closest in time.
Y. Ji, L. Zhang, J. Wu, B. Wu, L.-K. Huang, T. Xu, Y. Rong, L. Li, J. Ren, D. Xue, H. Lai, S. Xu, J. Feng, W. Liu, P. Luo, S. Zhou, J. Huang, P. Zhao, and Y. Bian · 2022
Closest in time.
Pre-training molecular graph representation with 3d geometry
S. Liu, H. Wang, W. Liu, J. Lasenby, H. Guo, and J. Tang · 2022
Closest in time.
Interpretable and generalizable graph learning via stochastic attention mechanism
S. Miao, M. Liu, and P. Li · 2022
Closest in time.
Geodiff: A geometric diffusion model for molecular conformation generation
M. Xu, L. Yu, Y. Song, C. Shi, S. Ermon, and J. Tang · 2022
Closest in time.
Finding diverse and predictable subgraphs for graph domain generalization
J. Yu, J. Liang, and R. He · 2022
Closest in time.
Sparse invariant risk minimization
X. Zhou, Y. Lin, W. Zhang, and T. Zhang · 2022
Closest in time.