Search Results for author:
Found 7 papers, 2 papers with code
Auditing Fairness under Unobserved Confounding
A fundamental problem in decision-making systems is the presence of inequity across demographic lines.
Bayesian Neural Networks with Domain Knowledge Priors
Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty.
Understanding prompt engineering may not require rethinking generalization
Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings.
Learning with Explanation Constraints
In this paper, we formalize this notion as learning from explanation constraints and provide a learning theoretic framework to analyze how such explanations can improve the learning of our models.
Improving self-supervised representation learning via sequential adversarial masking
Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision.
Losses over Labels: Weakly Supervised Learning via Direct Loss Construction
Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning.
Label Propagation with Weak Supervision
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications.
