SPSC recovers drifting low-rank subspaces from scalar rewards under known noise variance, bounded coupling, and full probe support, then achieves dynamic regret scaling as r sqrt(T) plus lower-order terms instead of d sqrt(T).
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A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
ℓ₂-Boosting exhibits benign overfitting with logarithmic excess variance decay Θ(σ²/log(p/n)) under isotropic noise due to ℓ₁ bias, and a subdifferential early stopping rule recovers minimax-optimal ℓ₁ rates.
AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
citing papers explorer
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Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity
SPSC recovers drifting low-rank subspaces from scalar rewards under known noise variance, bounded coupling, and full probe support, then achieves dynamic regret scaling as r sqrt(T) plus lower-order terms instead of d sqrt(T).
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Causal Algorithmic Recourse: Foundations and Methods
A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
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When Does $\ell_2$-Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the $\ell_1$ Implicit Bias
ℓ₂-Boosting exhibits benign overfitting with logarithmic excess variance decay Θ(σ²/log(p/n)) under isotropic noise due to ℓ₁ bias, and a subdifferential early stopping rule recovers minimax-optimal ℓ₁ rates.
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Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
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A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
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Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.