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Found 9 papers, 2 papers with code
Partial identification of kernel based two sample tests with mismeasured data
We show that under $\epsilon$-contamination, the typical estimate of the MMD is unreliable.
Multi-Similarity Contrastive Learning
Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart.
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
We study the theoretical properties of our approach, identifying scenarios where it is guaranteed to lead to zero bias when used to approximate the Q-function.
Offline Policy Evaluation and Optimization under Confounding
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning.
Fairness and robustness in anti-causal prediction
Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models.
Causally motivated Shortcut Removal Using Auxiliary Labels
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
Exploiting structured data for learning contagious diseases under incomplete testing
In this work we ask: can we build reliable infection prediction models when the observed data is collected under limited, and biased testing that prioritizes testing symptomatic individuals?
Estimation of Bounds on Potential Outcomes For Decision Making
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics.
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data
Risk prediction is central to both clinical medicine and public health.
