Invariance through inference
… We introduce a general approach, called Invariance through Inference, for improving the test…
producing invariant visual features through interpolation, invariance through inference turns …
producing invariant visual features through interpolation, invariance through inference turns …
Environment inference for invariant learning
E Creager, JH Jacobsen… - … Conference on Machine …, 2021 - proceedings.mlr.press
… for domain-invariant learning that incorporates Environment Inference to directly infer … for
downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on …
downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on …
Complexity and information in invariant inference
… However, the practical success of inference algorithms calls for a more refined complexity
… -based invariant inference through the decision problem of polynomial length inference in …
… -based invariant inference through the decision problem of polynomial length inference in …
[КНИГА][B] Group invariance in statistical inference
NC Giri - 1996 - books.google.com
… we use the term invariance to indicate group invariance. In statistics the term invariance is
used in … In our treatment in this monograph we treat invariance in the framework of statistical …
used in … In our treatment in this monograph we treat invariance in the framework of statistical …
When does measurement invariance matter?
D Borsboom - Medical care, 2006 - journals.lww.com
… Through this work, it has become clear … to infer group differences in theoretical attributes
(eg, general intelligence) unless the test scores accord with a particular set of model invariance …
(eg, general intelligence) unless the test scores accord with a particular set of model invariance …
Learning invariance through imitation
… In this section we present two methods for learning an invariant mapping that can exploit
the graded similarities obtained through imitation. The first is a probabilistic approach that is …
the graded similarities obtained through imitation. The first is a probabilistic approach that is …
Invariance
JO Berger, JO Berger - Statistical Decision Theory: Foundations, Concepts …, 1980 - Springer
… discussion of fiducial inference and structural inference. Many … invariance and its relationship
to the use of noninformative priors. The basic idea of invariance is best conveyed through …
to the use of noninformative priors. The basic idea of invariance is best conveyed through …
Loop Invariant Inference through SMT Solving Enhanced Reinforcement Learning
S Yu, T Wang, J Wang - Proceedings of the 32nd ACM SIGSOFT …, 2023 - dl.acm.org
… However, the problem of loop invariant inference is proven undecidable, and even practical
… These methods infer the correct loop invariant by iteratively proposing possible invariant …
… These methods infer the correct loop invariant by iteratively proposing possible invariant …
Learning priors for invariance
E Nalisnick, P Smyth - International Conference on Artificial …, 2018 - proceedings.mlr.press
… However, employing these tricks is difficult to reconcile with principled probabilistic inference…
exhibit invariance properties. The proposed method is akin to posterior variational inference…
exhibit invariance properties. The proposed method is akin to posterior variational inference…
Causal discovery using model invariance through knockoff interventions
… We propose to exploit model invariance through interventions on the predictors to infer
causality in nonlinear multivariate systems of time series. We model nonlinear interactions in time …
causality in nonlinear multivariate systems of time series. We model nonlinear interactions in time …