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- Ooi, Wei TsangSuggested from co-authorship
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Works10 from public data
- Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees23
This work proposes SARD, a convex learning algorithm built on a family of surrogate losses that are provably Bayes-consistent and $(\mathcal{R}, \mathcal{G})$-consistent, and demonstrates that these guarantees hold across classification, regression, and multi-task settings.
- Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer9
This work proposes a decoupled surrogate: a softmax classifier head and an independent sigmoid head per expert, mirroring the two natural objects of the problem, and proves an excess-risk bound with calibration constant, the first multi-expert L2D guarantee whose constant does not grow with the expert pool when the per-expert weight is held fixed.
- Online Learning-to-Defer with Varying Experts7
An online multiclass L2D algorithm that combines queried-action bandit feedback with a dynamically varying pool of experts is introduced that achieves expected true-deferral regret under a concentrated-score condition.
- Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models7
L2D-SLDS proves sublinear regret against a changing conditional-risk oracle without exploration, when the candidate models are accurate and either the archive separates them or live feedback reveals cost differences.
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- Towards Robust Human–AI Decision-Making via Learning-to-Defer1
A unified and robust framework for L2D is developed that characterizes Bayes-optimal routing policies, establishes surrogate-consistency guarantees, and introduces a unified adversarial framework for attacking and defending L2D with Bayes-optimal robustness.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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