Attractor decompositions in games and automata @SimonsInstituteTOC

Simons Institute | Attractor decompositions in games and automata @SimonsInstituteTOC | Uploaded 2 months ago | Updated 15 hours ago
Marcin Jurdzinski (University of Warwick)
https://simons.berkeley.edu/talks/marcin-jurdzinski-university-warwick-2024-07-19
Games and Equilibria in System Design and Analysis

An attractor decomposition is a decomposition of a graph that satisfies the parity condition, and its “shape” can be described by an ordered tree. We argue that attractor decompositions and their trees can be used to measure the structural complexity of a winning strategy in a parity game. We illustrate this on two examples: relating Lehtinen’s register number of a parity game to the Strahler number of its attractor decomposition tree, and a quasi-polynomial translation from alternating parity automata on words to alternating weak automata. [Joint work with Laure Daviaud, Karoliina Lehtinen, and Thejaswini K.S.]

Debugging genomic profiling experiments and predictive models with interpretation tools

A Theory of Multi-objective Machine Learning

$Large Scale Private Learning on Data Streams, and the BLTs Abhradeep Guha Thakurta (Google DeepMind) https://simons.berkeley.edu/talks/abhradeep-guha-thakurta-google-deepmind-2024-06-18 Extroverted Sublinear Algorithms In recent years, Differentially Private Follow the Regularized Leader (DP-FTRL) style algorithms have become increasingly popular for training large models over data streams, e.g., in training DP models for Gboard next-word prediction (https://research.google/blog/federated-learning-with-formal-differentia… and https://arxiv.org/abs/2305.18465). At the heart of these algorithms is a correlated noise addition mechanism that factorizes a square lower triangular matrix of all ones (A=BC) and adds noise BZ (with Z ~ N(0,σ^2)^{training steps x dimensions}) to the prefix sum of the gradients observed during training. Prior work required generating and storing the noise matrix in advance, which is prohibitively expensive for large-scale model training. In this talk, we introduce a new algorithmic construction called the Buffered Linear Toeplitz operator (BLT) that allows generating the noise required for DP in a streaming fashion while requiring a buffer size of approximately log^2(training steps) x dimensions. Furthermore, compared to the class of all Lower Triangular Toeplitz (LTT) factorizations (i.e., B and C being LTT), BLTs are only off by an additive constant in terms of utility under standard error metrics. The talk will draw ideas from rational function approximations and constant recurrence sequences for the construction of BLTs. The talk will also demonstrate the practicality of this work through simulation experiments. The talk is primarily based on https://arxiv.org/pdf/2404.16706, and is a joint work with Krishnamurthy (Dj) Dvijotham, Brendan McMahan, Krishna Pillutla, and Thomas Steinke.$

Space is a latent sequence: A theory of hippocampus and PFC

Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree

O(log log n) Passes is Optimal for Semi-Streaming Maximal Independent Set

Towards Understanding Generalization Properties of Score-Based Losses

Prediction, Generalization, Complexity: Revisiting the Classical View from Statistics Part 1