Marco Mondelli, Institute of Science and Technology Austria Abstract: A recent line of work has analyzed the properties of deep over-parameterized neural networks through the lens of the Neural Tangent Kernel (NTK). In this talk, I will show how concentration bounds on the NTK (and, specifically, on its smallest eigenvalue) provide insights on (i) the […]
UC San Diego Professor of Data Science and Philosophy and TILOS affiliate David Danks will present an introduction to the U.S. Government’s Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence for TILOS members. David Danks currently serves on the National AI Advisory Committee (NAIAC), which is tasked with advising the President and the National […]
Aldo Pacchiano, Assistant Professor, Boston University Center for Computing and Data Sciences Abstract: The principle of Optimism in the Face of Uncertainty (OFU) is one of the foundational algorithmic design choices in Reinforcement Learning and Bandits. Optimistic algorithms balance exploration and exploitation by deploying data collection strategies that maximize expected rewards in plausible models. This […]
Max Welling, Research Chair in Machine Learning, University of Amsterdam Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC, Belief Propagation, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed, with new tools developed in the […]
Please join Dr. Nisheeth Vishnoi from Yale and Dr. David Danks from UC San Diego who will discuss their Research in AI Ethics. Professor Danks develops practical frameworks and methods to incorporate ethical and policy considerations throughout the AI lifecycle, including different ways to include them in optimization steps. Bias and fairness have been a […]
The UC San Diego HDSI-TILOS “LLM Meets Theory” Workshop aims to bring together students and faculty to discuss the future of mathematical and scientific theory and large language models (LLMs). LLMs are like a miracle—not one that breaks the laws of nature (that would be impossible, of course), but something that defied all expectations and […]
Roy Fox, Assistant Professor and Director of the Intelligent Dynamics Lab, UC Irvine Abstract: If learning from data is valuable, can learning from big data be very valuable? So far, it has been so in vision and language, for which foundation models can be trained on web-scale data to support a plethora of downstream tasks; […]
Xiang Cheng, TILOS Postdoctoral Scholar, MIT Abstract: Motivated by the in-context learning phenomenon, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent, which enables it to learn linear functions in-context. More generally, we show that a non-linear […]
Nicklas Hansen, UC San Diego Abstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However, teaching AI agents to reliably interact with our physical world has proven challenging, which is in part due to a lack of large and sufficiently diverse robot datasets. In this talk, […]
TILOS (The NSF National AI Institute for Learning-enabled Optimization at Scale) will hold its 3rd Annual Industry Day on June 18, 2024, at the Halıcıoğlu Data Science Institute at UC San Diego, which is the campus hub for Data Science. Our first two Industry Days have attracted more than 100 participants, each featuring (1) talks from invited Industry […]
