Presentation Abstract: As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that […]
Presentation Abstract: From robotics to autonomous vehicles, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In […]
Presentation Abstract: The rapid advance of AI driven by Large Language Models (LLMs), like ChatGPT, has led to impressive results across a range of different use cases. This has included […]
Presentation Abstract: Despite ML models' impressive performance, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk, I overview my work […]
Presentation Abstract: Despite the widespread proliferation of neural networks, the mechanisms through which they operate so successfully are not well understood. In this talk, we will first explore empirical and […]
Presentation Abstract: The growing complexity and heterogeneity of networked systems have spurred a plethora of machine learning (ML) solutions, each promising a tantalizing improvement in performance. However, their path to […]
Presentation Abstract: Artificial Intelligence is being increasingly relied on in safety-critical domains. But the predictive models underlying these systems are notoriously brittle, and trustworthy deployment remains a significant challenge. In […]
Presentation Abstract: "Trustworthy Machine Learning" has become a buzz-word in recent years. But what exactly are the semantics of the promise that we are supposed to trust? In this talk we will make a proposal, through the […]
Presentation Abstract: The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, […]
Presentation Abstract: Language models work well, but they are far from trustworthy. Major open questions remain on high-stakes issues such as detecting benchmark contamination, identifying LM-generated text, and reliably generating […]
