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Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift
Methodology · Submitted 28 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.
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The abstract, sentence by sentence
Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. We allow for partially non-overlapping support between the source and target populations. We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Statistical machine learning methods often face the challenge of limited data available from the population of interest. | background 1.00 | 0.04 | 0.01 | asserted 0.93 |
| s1 | One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. | background 1.00 | 0.04 | 0.02 | asserted 0.77 |
| s2 | Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. | gap 1.00 | 0.04 | 0.04 | asserted 1.00 |
| s4 | In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. | contribution 1.00 | 0.84 | 0.13 | asserted 0.93 |
| s5 | We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. | contribution 0.60 | 0.73 | 0.04 | asserted 1.00 |
| s6 | We allow for partially non-overlapping support between the source and target populations. | method 0.48 | 0.56 | 0.03 | asserted 0.85 |
| s7 | We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. | contribution 0.89 | 0.97 | 0.25 | asserted 1.00 |
| s8 | We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. | contribution 0.95 | 0.96 | 0.08 | asserted 1.00 |
| s9 | Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions. | result 1.00 | 0.93 | 0.32 | asserted 0.98 |
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The paper
Original abstract
Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{dataset shift} conditions are known as \emph{domain adaptation} or \emph{transfer learning}. Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population. In this paper, we study the general problem of efficiently estimating target population risk under various dataset shift conditions, leveraging semiparametric efficiency theory. We consider a general class of dataset shift conditions, which includes three popular conditions -- covariate, label and concept shift -- as special cases. We allow for partially non-overlapping support between the source and target populations. We develop efficient and multiply robust estimators along with a straightforward specification test of these dataset shift conditions. We also derive efficiency bounds for two other dataset shift conditions, posterior drift and location-scale shift. Simulation studies support the efficiency gains due to leveraging plausible dataset shift conditions.
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References
How the literature uses it
3 arXiv papers cite this in 6 sentences
Papers citing it · 3 sentences without a confident label
Builds on it 1 paper
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Here we consider the scenario where the target and source populations differ under the so-called “Covariate Shift Posterior Drift” (CSPD) mechanism introduced in [ref] , building upon [ref] .
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation · Transfer Learning: Average Loss Estimation under Dataset Shift · 0.85
Uses its method or tool 1 paper
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[ref] proposed the following statistically optimal (i.e. semiparametric efficient) estimator $β̂$ of $β^∗$: [equation] We now unpack the above definition: here $v (x) [ℓ(X, Y) | X = x, A = 0]$ is the average conditional squared error loss in the target population with $v̂$ being its estimate, $𝒮(x) {x': r̂_0 (x') = r̂_0 (x)}$ is a subset in the sample space of the covariate $X$, and further define [equation] We finally turn to the definitions of $ĝ_1$ and $ĝ_2$: first let $𝒜 (x) ∫_x' ∈𝒮(x) K (x') 𝖻(x') x'$, and eventually define [equation] Derivations of the above estimator can also be found in Appendix [ref] for the sake of completeness.
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation · Transfer Learning: Average Loss Estimation under Dataset Shift · 0.87
Cites it as background 1 paper
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The related literature on multi-source domain adaptation [ref] usually focuses on risk estimation using samples from multiple source populations that perform well in a target population or are robust to distributional shifts between the source populations and the target population.
Interpretable meta-analysis of model or marker performance · Introduction · 0.99
What it cites · 8 sentences without a confident label
Cites as background 8 papers
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Two popular guarantees are marginal coverage and training-set conditional—or probably approximately correct (PAC)—coverage [ref] .
PAC Confidence Predictions for Deep Neural Network Classifiers · Problem setup · 0.99
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[ref] proposed testing for various forms of dataset shift.
A unified framework for dataset shift diagnostics · Background · 0.87
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Other conditions and methods have also been studied for more specific problems; examples include (generalized, penalized) linear models [ref] , binary classification [ref] , graphical models [ref] , and location-scale shifts [ref] , among others.
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features · Background · 0.95
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