Search Results for author:
Found 8 papers, 2 papers with code
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
However, these gains in click prediction do not translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in this benchmark.
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
In this work, we propose to model the deployment of a DSS as causal domain shift and provide novel cross-domain identification results for the conditional expectation $E[Y | X]$, allowing for pre- and post-hoc assessment of the deployment of the DSS, and for retraining of a model that assesses the risk under a baseline policy where the DSS is not deployed.
When Do Off-Policy and On-Policy Policy Gradient Methods Align?
A well-established off-policy objective is the excursion objective.
Modeling Latent Selection with Structural Causal Models
Selection bias is ubiquitous in real-world data, and can lead to misleading results if not dealt with properly.
Fair Grading Algorithms for Randomized Exams
In a randomized exam, each student is asked a small number of random questions from a large question bank.
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS).
Sparse Bayesian Multi-Task Learning
We propose a new sparse Bayesian model for multi-task regression and classification.
