TILOS Seminar Series

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  • TILOS Seminar: Foundational Methods for Foundation Models for Scientific Machine Learning

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Michael W. Mahoney, ICSI, LBNL, and Department of Statistics, UC Berkeley Abstract: The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable, when built on the rich, diverse, multimodal data that […]

  • TILOS Seminar: Amplifying human performance in combinatorial competitive programming

    Virtual

    Petar Veličković, Google DeepMind Abstract: Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk, […]

  • TILOS Seminar: Optimal Quantization for LLMs and Matrix Multiplication

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Yury Polyanskiy, MIT Abstract: The main building block of large language models is matrix multiplication, which is often bottlenecked by the speed of loading these matrices from memory. A number of recent quantization algorithms (SmoothQuant, GPTQ, QuIP, SpinQuant etc) address this issue by storing matrices in lower precision. We derive optimal asymptotic information-theoretic tradeoff between […]

  • TILOS-HDSI Seminar with Adam Klivans (UT Austin): A New Paradigm for Learning with Distribution Shift

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Adam Klivans, The University of Texas at Austin Abstract: We revisit the fundamental problem of learning with distribution shift, where a learner is given labeled samples from training distribution D, unlabeled samples from test distribution D′ and is asked to output a classifier with low test error. The standard approach in this setting is to […]

  • Optimization for ML and AI Seminar with Courtney Paquette (McGill University): High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws

    CSE 1242 and Virtual 3235 Voigt Dr, La Jolla, CA, United States

    Courtney Paquette, McGill University Abstract: Given the massive scale of modern ML models, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead, we need to understand how these models scale, allowing us to experiment with smaller problems and then apply those […]

  • TILOS-SDSU Seminar with Jeremy Schwartz (Zoox)

    SDSU and Virtual

    Title and abstract TBA... Jeremy Schwartz is a robotics engineer at Zoox with expertise in a wide variety of areas of mechanical and electrical engineering and computer science. His primary professional expertise is in autonomy and behavioral algorithms, and he has worked in the aerospace industry as well as ground robotics, specializing in autonomous systems […]

  • Optimization for AI and ML Seminar with Volkan Cevher (EPFL)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park from 2006-2007 and […]

  • Optimization for ML and AI Seminar with Frank E. Curtis (Lehigh University)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been employed since 2009. He received a bachelor’s degree from the College of William and Mary in 2003 with a double major in Computer Science and Mathematics, received a master’s degree […]

  • TILOS-HDSI Seminar with Yi Wu (Tsinghua University)

    CSE 4140 and Virtual 9500 Gilman Dr, La Jolla, CA, United States

    Title and abstract TBA... Yi Wu is an Assistant Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences (IIIS). He received his Ph.D. from the University of California, Berkeley, advised by Professor Stuart Russell. Before joining IIIS, Dr. Wu was a researcher at OpenAI. His research interest include deep reinforcement learning, multi-agent learning, natural language […]

  • Optimization for ML and AI Seminar with Joel Tropp (Caltech)

    HDSI 123 and Virtual 3234 Matthews Ln, La Jolla, CA, United States

    Title and abstract TBA... Joel A. Tropp is Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology. His research centers on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory. Some of his best-known contributions include matching pursuit algorithms, randomized SVD algorithms, matrix concentration inequalities, and statistical phase transitions. Prof. Tropp attained the […]