Abstract: I will survey recent results describing the application of modern non-convex optimization methods to the problems of reconstruction attacks on private datasets (the “poison”), and the algorithmic generation of […]
Abstract: Artificial intelligence holds enormous promise to automate our life. At the same time, AI systems can be incredibly brittle, unreliable, and biased. In this talk, I will present a few […]
Abstract: The efficient sharing of AI infrastructures is becoming increasingly important in both public and private data centers. This demand is driven by two key factors: the proliferation of specialized […]
Abstract: Modern machine learning models—such as LLMs and recommender systems—interact with humans, companies, and other models in a broader ecosystem. However, these multi-agent interactions often induce unintended ecosystem-level outcomes such […]
Abstract: Large Language Models (LMs) have demonstrated remarkable capabilities by scaling up training data and model sizes. However, they continue to face critical challenges, including hallucinations and outdated knowledge, which […]
Abstract: Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However, due to their scale and complexity, we lack a fundamental scientific understanding of much […]
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Abstract: Extracting insights from imaging data used to be straightforward: every component of imaging systems was engineered by humans, the analysis and interpretation of the collected data was driven by […]
Abstract: Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them […]
Abstract: Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing […]
