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Found 9 papers, 6 papers with code
Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
In such cases, marginally valid conformal prediction intervals may not provide valid coverage for the focal unit(s) due to selection bias.
Iterative Approximate Cross-Validation
Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models.
Policy learning "without'' overlap: Pessimism and generalized empirical Bernstein's inequality
Existing policy learning methods rely on a uniform overlap assumption, i. e., the propensities of exploring all actions for all individual characteristics are lower bounded in the offline dataset.
Policy Learning with Adaptively Collected Data
Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
Online Multi-Armed Bandits with Adaptive Inference
During online decision making in Multi-Armed Bandits (MAB), one needs to conduct inference on the true mean reward of each arm based on data collected so far at each step.
Derandomizing Knockoffs
Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives.
Feature Importance
Variable Selection
Methodology
Applications
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
We study the problem of dynamic batch learning in high-dimensional sparse linear contextual bandits, where a decision maker, under a given maximum-number-of-batch constraint and only able to observe rewards at the end of each batch, can dynamically decide how many individuals to include in the next batch (at the end of the current batch) and what personalized action-selection scheme to adopt within each batch.
Batched Multi-armed Bandits Problem
While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open.
