On the Limitations of General Purpose Domain Generalisation Methods

Rademacher and gaussian complexities: Risk bounds and structural results

P. L. Bartlett and S. Mendelson · 2002

Earlier work this paper cites.

A theory of learning from different domains

S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010

Earlier work this paper cites.

Generalizing from several related classification tasks to a new unlabeled sample

G. Blanchard, G. Lee, and C. Scott · 2011

Earlier work this paper cites.

Learning From An Optimization Viewpoint

K. Sridharan · 2011

Earlier work this paper cites.

Domain generalization via invariant feature representation

K. Muandet, D. Balduzzi, and B. Schölkopf · 2013

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.

Domain Adaptation in Computer Vision Applications

G. Csurka · 2017

Earlier work this paper cites.

Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning

S. Kpotufe · 2017

Earlier work this paper cites.

Foundations of machine learning

M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018

Cited alongside, same era.

Invariant risk minimization

M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019

Cited alongside, same era.

Causal regularization

D. Janzing · 2019

Cited alongside, same era.

Bridging Theory and Algorithm for Domain Adaptation

Y. Zhang, T. Liu, M. Long, and M. Jordan · 2019

Cited alongside, same era.

Generalizing to unseen domains via distribution matching

Original

I. Albuquerque, J. Monteiro, M. Darvishi, T. H. Falk, and I. Mitliagkas · 2020

Cited alongside, same era.

Marginal Singularity, and the Benefits of Labels in Covariate-Shift, June 2020

S. Kpotufe and G. Martinet · 2020

Cited alongside, same era.

Domain generalization by marginal transfer learning

G. Blanchard, A. A. Deshmukh, Ü. Dogan, G. Lee, and C. Scott · 2021

Later among the works it cites.

Geometric deep learning: Grids, groups, graphs, geodesics, and gauges

Original

M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković · 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.

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.

Domain generalization: A survey, 2021

K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy · 2021

Later among the works it cites.