Pogodin, Roman; Deka, Namrata; Li, Yazhe; Sutherland, Danica J; Veitch, Victor; Gretton, Arthur; (2023) Efficient Conditionally Invariant Representation Learning. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. p. 4723). International Conference on Learning Representations: Kigali, Rwanda. (In press).

Preview

Text 4723_efficient_conditionally_invari.pdf - Accepted Version Download (559kB) | Preview

Abstract

We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regularizer in settings where we wish to learn neural features φ(X) of data X to estimate a target Y , while being conditionally independent of a distractor Z given Y . Both Z and Y are assumed to be continuous-valued but relatively low dimensional, whereas X and its features may be complex and high dimensional. Relevant settings include domain-invariant learning, fairness, and causal learning. The procedure requires just a single ridge regression from Y to kernelized features of Z, which can be done in advance. It is then only necessary to enforce independence of φ(X) from residuals of this regression, which is possible with attractive estimation properties and consistency guarantees. By contrast, earlier measures of conditional feature dependence require multiple regressions for each step of feature learning, resulting in more severe bias and variance, and greater computational cost. When sufficiently rich features are used, we establish that CIRCE is zero if and only if φ(X) ⊥⊥ Z | Y . In experiments, we show superior performance to previous methods on challenging benchmarks, including learning conditionally invariant image features.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as CSVJSONXML

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item