Fetching the paper…
Reading the bibliography…
Empirical Risk Minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on data obtained from out-of-distribution (OOD).
Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks
Lee, H. B., Lee, H., Na, D., Kim, S., Park, M., Yang, E., and Hwang, S. J · 1905
Earlier work this paper cites.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Bengio, Y., Bengio, S., and Cloutier, J · 1990
Earlier work this paper cites.
Learning to learn: Introduction and overview
Thrun, S. and Pratt, L · 1998
Earlier work this paper cites.
Empirical or invariant risk minimization? a sample complexity perspective
Ahuja, K., Wang, J., Dhurandhar, A., Shanmugam, K., and Varshney, K. R · 2010
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
Cited alongside, same era.
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
Cited alongside, same era.
Invariant causal prediction for nonlinear models
Heinze-Deml, C., Peters, J., and Meinshausen, N · 2018
Cited alongside, same era.
Deep bilevel learning
Jenni, S. and Favaro, P · 2018
Cited alongside, same era.
Insights on representational similarity in neural networks with canonical correlation
Morcos, A. S., Raghu, M., and Bengio, S · 2018
Cited alongside, same era.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O · 2019
Later among the works it cites.
Fast context adaptation via meta-learning
Zintgraf, L., Shiarli, K., Kurin, V., Hofmann, K., and Whiteson, S · 2019
Later among the works it cites.
An empirical study of invariant risk minimization
Choe, Y. J., Ham, J., and Park, K · 2020
Later among the works it cites.
Ood-maml: Meta-learning for few-shot out-of-distribution detection and classification
Jeong, T. and Kim, H · 2020
Later among the works it cites.
Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Priol, R. L., and Courville, A · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Causal confusion in imitation learning
de Haan, P., Jayaraman, D., and Levine, S · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
Invariant risk minimization games
Ahuja, K., Shanmugam, K., Varshney, K., and Dhurandhar, A
Cited in the paper.
Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S
Cited in the paper.
Later among the works it cites.
The risks of invariant risk minimization
Rosenfeld, E., Ravikumar, P., and Risteski, A · 2020
Later among the works it cites.
Regularizing meta-learning via gradient dropout
Tseng, H.-Y., Chen, Y.-W., Tsai, Y.-H., Liu, S., Lin, Y.-Y., and Yang, M.-H · 2020
Later among the works it cites.
Does invariant risk minimization capture invariance?
Kamath, P., Tangella, A., Sutherland, D. J., and Srebro, N · 2021
Closest in time.