• From Word Prediction to Complex Skills: Data Flywheels for Mathematical Reasoning

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    https://youtu.be/OYOuSAAE7QQ New Technologies in Mathematics Seminar Speaker: Anirudh Goyal (University of Montreal) Title: From Word Prediction to Complex Skills: Data Flywheels for Mathematical Reasoning Abstract: This talk examines how large language models (LLMs) evolve from simple word prediction to complex skills, with a focus on mathematical problem solving. A major driver of AI products today is the […]

  • Topics in Deep Learning Theory

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    Topics in Deep Learning Theory Eli Grigsby

  • Bosonic and fermionic 1-form symmetries and anomaly matching

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    Quantum Field Theory and Physical Mathematics Seminar *via Zoom only* Speaker: Rajath Radhakrishnan (ICTP, Trieste) Title: Bosonic and fermionic 1-form symmetries and anomaly matching Abstract: In this talk, I will consider bosonic and fermionic (non-invertible) 1-form symmetries in 2+1d QFTs. These are 1-form symmetries implemented by topological line operators with real spins. I will present a classification […]

  • Positive mass and rigidity theorems in Riemannian geometry  

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    Member Seminar Speaker: Puskar Mondal Title: Positive mass and rigidity theorems in Riemannian geometry Abstract: Positive mass theorem proved by Schoen-Yau, Witten, Taubes-Parker is one of the most important results in scalar curvature geometry in asymptotically flat settings. Since then several versions have been proven and generalized to other geometries such as asymptotically hyperbolic manifolds. The analogous […]

  • Higher Vapnik–Chervonenkis theory

    CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United States

    Colloquium Speaker: Artem Chernikov, University of Maryland Title: Higher Vapnik–Chervonenkis theory Abstract: Finite VC-dimension, a combinatorial property of families of sets, was discovered simultaneously by Vapnik and Chervonenkis in probabilistic learning theory, and by Shelah in model theory (where it is called NIP). It plays an important role in several areas including machine learning, combinatorics, mathematical […]