Speaker:   Hao Geng
Title:   It From ETH Part II
Abstract:   In this talk, I will recap what we discussed in the workshop and finish the rest of it. I'll explain how the Cardy boundary enriched large-c CFT ensemble enbles a derivation of the multi-intervel entanglement entropy in AdS3/CFT2 and provides a natural way to see the emergence of the replica wormhole in the calculation of the Page curve of the black hole radiation. I'll also discuss some open problems in quantum gravity inspired by our work.

Speaker:   Masamichi Miyaji
Title:   Perturbative Hilbert space of JT closed universes
Abstract:   We consider gravitational path-integral of JT gravity of closed universe in AdS and dS spacetimes, for fixed extrinsic curvature boundary conditions where the spacetime boundary is rigid rather than random. In the absence of topology change, we calculate the amplitude and identify the perturbative Hilbert space. We find the Hilbert space is given by the infinite-dimensional co-invariant Hilbert space, which can be obtained from a space of boundary conditions modded by constraints. We also propose a finite N regularization of the model, whose Hilbert space dimension is non-trivial.

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TITLE: Information Lattice Learning

ABSTRACT: Drawing on group-theoretic and information-theoretic foundations, we propose information lattice learning (ILL) as a general framework to learn rules of a signal (e.g., an image or a probability distribution). In our definition, a rule is a coarsened signal used to help us gain one interpretable insight about the original signal. To make full sense of what might govern the signal’s intrinsic structure, we seek multiple disentangled rules arranged in a hierarchy, called a lattice. Compared to representation/rule-learning models optimized for a specific task (e.g., classification), ILL focuses on explainability: it is designed to mimic human experiential learning and discover rules akin to those humans can distill and comprehend. We will detail the mathematical foundations and algorithms of ILL, and illustrate how it addresses the fundamental question “what makes X an X” by creating rule-based explanations designed to help humans understand. Our focus is on explaining X rather than (re)generating it. We show ILL’s efficacy and interpretability on benchmarks and assessments, as well as a demonstration of ILL-enhanced classifiers achieving human-level digit recognition using only one or a few MNIST training examples (1–10 per class).  We present applications in knowledge discovery, using ILL to distill music theory from scores and chemical laws from molecules and further revealing connections between them. We close with some early work on understanding the principles that govern scattering amplitudes in Super Yang-Mills theory, rather than just predicting them.

BIOGRAPHY:
Lav R. Varshney is the Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute at Stony Brook University. He is co-founder and CEO of Kocree, Inc., a startup company building novel human-controllable AI for discovery and creativity, and chief scientist of Ensaras, Inc., a startup company focused on AI and wastewater treatment. He holds appointments at RAND Corporation and at Brookhaven National Laboratory. He was previously on the faculty of the University of Illinois Urbana-Champaign, a visiting scholar at Northwestern's Kellogg School of Management, a principal research scientist at Salesforce Research AI, and a research staff member at IBM Research. He is a former White House staffer, having served on the National Security Council staff as a White House Fellow, where he contributed to national AI and wireless communications policy. His research interests include information theory and artificial intelligence. He received his B.S. degree from Cornell University and his S.M. and Ph.D. degrees from the Massachusetts Institute of Technology.