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Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
Foundations models are presented as generalists that often perform well over a myriad of tasks.
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Sampling biases in training data are a major source of algorithmic biases in machine learning systems.
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
We define the $\varepsilon$-contaminated stochastic bandit problem and use our robust mean estimators to give two variants of a robust Upper Confidence Bound (UCB) algorithm, crUCB.
Fair Pipelines
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions.
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