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An Analysis of the Adaptation Speed of Causal Models
v1v2 (latest)

An Analysis of the Adaptation Speed of Causal Models

18 May 2020
Rémi Le Priol
Reza Babanezhad Harikandeh
Yoshua Bengio
Damien Scieur
    CML
ArXiv (abs)PDFHTMLGithub (15★)

Papers citing "An Analysis of the Adaptation Speed of Causal Models"

10 / 10 papers shown
Softmax is $1/2$-Lipschitz: A tight bound across all $\ell_p$ norms
Softmax is 1/21/21/2-Lipschitz: A tight bound across all ℓp\ell_pℓp​ norms
Pravin Nair
350
3
0
27 Oct 2025
Adaptation Speed Analysis for Fairness-aware Causal Models
Adaptation Speed Analysis for Fairness-aware Causal ModelsInternational Conference on Information and Knowledge Management (CIKM), 2023
Yujie Lin
Chen Zhao
Minglai Shao
Xujiang Zhao
Haifeng Chen
162
6
0
31 Aug 2023
Modularity Trumps Invariance for Compositional Robustness
Modularity Trumps Invariance for Compositional Robustness
I. Mason
Anirban Sarkar
Tomotake Sasaki
Xavier Boix
OOD
315
1
0
15 Jun 2023
On the Generalization and Adaption Performance of Causal Models
On the Generalization and Adaption Performance of Causal Models
Nino Scherrer
Anirudh Goyal
Stefan Bauer
Yoshua Bengio
Nan Rosemary Ke
CMLOODBDLTTA
239
10
0
09 Jun 2022
CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning
  of Large Language Models
CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language ModelsInternational Conference on Language Resources and Evaluation (LREC), 2021
Jorg Frohberg
Frank Binder
SLR
406
45
0
22 Dec 2021
Convergence Rates for the MAP of an Exponential Family and Stochastic
  Mirror Descent -- an Open Problem
Convergence Rates for the MAP of an Exponential Family and Stochastic Mirror Descent -- an Open Problem
Rémi Le Priol
Frederik Kunstner
Damien Scieur
Damien Scieur
217
1
0
12 Nov 2021
Prequential MDL for Causal Structure Learning with Neural Networks
Prequential MDL for Causal Structure Learning with Neural Networks
J. Bornschein
Silvia Chiappa
Alan Malek
Rosemary Nan Ke
CML
290
3
0
02 Jul 2021
Can Subnetwork Structure be the Key to Out-of-Distribution
  Generalization?
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?International Conference on Machine Learning (ICML), 2021
Dinghuai Zhang
Kartik Ahuja
Yilun Xu
Yisen Wang
Aaron Courville
OOD
359
107
0
05 Jun 2021
Towards Causal Representation Learning
Towards Causal Representation Learning
Bernhard Schölkopf
Francesco Locatello
Stefan Bauer
Nan Rosemary Ke
Nal Kalchbrenner
Anirudh Goyal
Yoshua Bengio
OODCMLAI4CE
424
352
0
22 Feb 2021
Inductive Biases for Deep Learning of Higher-Level Cognition
Inductive Biases for Deep Learning of Higher-Level CognitionProceedings of the Royal Society A (Proc. R. Soc. A), 2020
Anirudh Goyal
Yoshua Bengio
AI4CE
618
430
0
30 Nov 2020
1
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