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Found 11 papers, 5 papers with code
Off-Policy Evaluation for Large Action Spaces via Policy Convolution
To address these challenges, we introduce the Policy Convolution (PC) family of estimators.
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
We consider offline policy optimization (OPO) in contextual bandits, where one is given a fixed dataset of logged interactions.
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
To this end, motivated by recent advances in uncertainty quantification, we propose two threshold-policy selection rules that can provide distribution-free and finite-sample guarantees on fairness in first-stage recommenders.
Improving Screening Processes via Calibrated Subset Selection
Many selection processes such as finding patients qualifying for a medical trial or retrieval pipelines in search engines consist of multiple stages, where an initial screening stage focuses the resources on shortlisting the most promising candidates.
Improving Expert Predictions with Conformal Prediction
In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance.
Fairness of Exposure in Stochastic Bandits
Contextual bandit algorithms have become widely used for recommendation in online systems (e. g. marketplaces, music streaming, news), where they now wield substantial influence on which items get exposed to the users.
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally.
Integrated Triaging for Fast Reading Comprehension
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator.
Resource Aware Person Re-identification across Multiple Resolutions
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.
Ranked #12 on
Person Re-Identification
on CUHK03 detected
