A Theory of Multi-objective Machine Learning
Explore multi-objective learning as a unifying paradigm for robustness, collaboration, and fairness in machine learning, with technical tools and empirical evidence.
When and Why Do Efficient Algorithms Exist for Constraint Satisfaction and Beyond
Explore the mathematical structures behind efficient algorithms for constraint satisfaction problems and beyond, focusing on polymorphisms and their role in computational complexity.
Modelling Human Syntax by Means of Probabilistic Dependency Grammars - 2007
Explore probabilistic dependency grammars for modeling human syntax, addressing linguistic phenomena beyond context-free grammars and discussing generative models and parsing algorithms.
Tutorial on Cellular Automata - Lecture 2
Explore advanced concepts in cellular automata, including complex patterns and computational models, in this in-depth mathematical lecture.
An Introduction to Walnut - Lecture 2
Explore Walnut, a tool for automata-based proofs in number theory and combinatorics. Delve into advanced techniques and applications in this comprehensive mathematical lecture.
An Introduction to Walnut - Lecture 1
Explore Walnut, a tool for automata-based decision procedures in number theory and combinatorics on words. Learn its applications and fundamental concepts.
Derandomization and Meta-complexity - Part 2
Explore meta-complexity in derandomization through an advanced tutorial. Delve into cutting-edge mathematical concepts and their applications in randomness and complexity theory.
A Unifying Theory of Distance to Calibration
Explore a rigorous framework for analyzing calibration measures in probabilistic predictors, examining consistent measures and establishing fundamental bounds on measuring calibration distance.
Concentration on HDX: Derandomization Beyond Chernoff - Lecture
Explore advanced derandomization techniques beyond Chernoff's bound, focusing on high-dimensional expanders and their applications in theoretical computer science and complexity theory.