ASSET

Presentation Abstract: Faced with unlabeled data in deployment that is sampled from a different distribution than that which generated the training data, all bets are off. Moreover, while numerous heuristics have been proposed for this vague setting, it remains unclear when any among them are applicable. One way to render these problems identifiable is to […]

Presentation Abstract: Huge strides have made in the widespread adoption of autonomous and human-in-the-loop cyber-physical systems (CPS), partly fueled by dramatic improvements in learning-based techniques. An important aspect of such CPS applications is that they are safety-critical: any undesirable behavior by such systems can cause serious harm to human lives or property. The formal methods […]

ABSTRACT: I'll present research on using large language models (LLMs) to build explainable classifiers.   I will show off work from my PhD students and collaborators on several recent research directions: Image classification with explainable features  (https://arxiv.org/abs/2211.11158) Text classification with explainable features (work in progress) The importance of faithfulness in explanations (https://arxiv.org/abs/2209.11326) (Time permitting) A […]

Abstract: Machine learning algorithms are increasingly used in conjunction with optimization to guide decision making. A key challenge is aligning the machine learning loss with the decision-making loss. Existing solutions have limited flexibility and/or scale poorly to large datasets. We propose a principled decision-aware learning algorithm that uses a Taylor expansion of the optimal decision […]