A call for better unit testing for invariant risk minimisation

06/06/2021
by   Chunyang Xiao, et al.
0

In this paper we present a controlled study on the linearized IRM framework (IRMv1) introduced in Arjovsky et al. (2020). We show that IRMv1 (and its variants) framework can be potentially unstable under small changes to the optimal regressor. This can, notably, lead to worse generalisation to new environments, even compared with ERM which converges simply to the global minimum for all training environments mixed up all together. We also highlight the isseus of scaling in the the IRMv1 setup. These observations highlight the importance of rigorous evaluation and importance of unit-testing for measuring progress towards IRM.

READ FULL TEXT
research
01/04/2021

Does Invariant Risk Minimization Capture Invariance?

We show that the Invariant Risk Minimization (IRM) formulation of Arjovs...
research
04/10/2020

An Empirical Study of Invariant Risk Minimization

Invariant risk minimization (IRM; Arjovsky et al., 2019) is a recently p...
research
01/30/2022

Provable Domain Generalization via Invariant-Feature Subspace Recovery

Domain generalization asks for models trained on a set of training envir...
research
04/29/2012

Generalising unit-refutation completeness and SLUR via nested input resolution

We introduce two hierarchies of clause-sets, SLUR_k and UC_k, based on t...
research
05/25/2019

Evacuating Two Robots from a Disk: A Second Cut

We present an improved algorithm for the problem of evacuating two robot...
research
10/09/2020

The Unit-Gompertz Distribution: Characterizations and Properties

In a recent paper, Mazucheli et al. (2019) introduced the unit-Gompertz ...
research
10/05/2020

Why Older Adults (Don't) Use Password Managers

Password managers (PMs) are considered highly effective tools for increa...

Please sign up or login with your details

Forgot password? Click here to reset