Artificial Intelligence
Explore artificial intelligence fundamentals, algorithms, and applications. Learn about search techniques, game theory, planning, expert systems, logic, and constraint satisfaction problems.
Artificial Intelligence
Comprehensive exploration of AI concepts, from search algorithms and logic to planning, expert systems, uncertainty reasoning, fuzzy logic, machine learning, and natural language processing.
Artificial Intelligence
Comprehensive exploration of AI concepts, algorithms, and techniques, covering search strategies, optimization methods, game playing, planning, constraint satisfaction, and knowledge-based systems.
An Introduction to Artificial Intelligence
Comprehensive exploration of AI fundamentals, covering search algorithms, game theory, logic, uncertainty, Bayesian networks, and reinforcement learning, with historical context and practical applications.
Introduction to the Dynamics of Disordered Systems: Equilibrium and Gradient Descent - Lecture 1
Explore disordered systems dynamics, covering glasses, jamming transitions, and optimization problems. Gain insights into metastable states and algorithmic applications in various fields.
A Quick and Partial Survey on the Complexity of Query Answering
Explore the complexity of query answering in this survey, covering key concepts and recent developments in discrete mathematics and logic.
Goemans-Williamson: Rounding the Max-Cut SDP - Lecture 20a of CS Theory Toolkit
Efficient algorithm for approximating maximum graph cuts using semidefinite programming and randomized rounding, with guaranteed performance ratio of 0.878.
Improved Streaming Algorithms for Max-DICUT via Local Snapshots
Explore advanced streaming algorithms for Max-DICUT problem, focusing on local graph snapshots to surpass the 4/9 approximation in sublinear space.
Sparsification: Graphs, CSPs and Codes - Sublinear Graph Simplification
Explore graph sparsification, CSP compression, and code sparsification techniques. Learn about cutting-edge research in sublinear graph simplification and its applications to various domains.
Why Do Simple Models Work? Partial Answers from Information Geometry - Lecture 1
Explores information geometry to understand why simple models effectively describe complex systems, focusing on statistical mechanics, renormalization, and model reduction in physics and biology.
When and How are Promise Constraint Satisfaction Problems Efficiently Solvable
Delve into the mathematical foundations of Constraint Satisfaction Problems (CSP), exploring algorithmic efficiency, polymorphisms, and promise CSP applications in graph coloring and discrepancy minimization.
Constraint Satisfaction Problems: Consistency Algorithms - Part 6
Explore consistency algorithms for constraint satisfaction problems in AI, covering formulation, solving methods, and domain splitting.
Constraint Satisfaction Problem - What Makes the Problem Easy
Explore constraint satisfaction problems, their complexity, and variants. Learn about polynomial-time solvable cases and NP-complete instances in this mathematical deep dive.
Trees and Series-Parallel Graphs - Lecture 22a of CS Theory Toolkit
Explore treewidth, CSPs, and graph theory fundamentals for theoretical computer science research, focusing on trees and series-parallel graphs.
Constraint Satisfaction Problems - Lecture 20b of CS Theory Toolkit
Explore Constraint Satisfaction Problems, their formal definition, examples, and algorithms. Learn about the Dichotomy Theorem and its implications for theoretical computer science research.
Analysis of Boolean Functions at CMU - Constraint Satisfaction Problems
Explore constraint satisfaction problems in boolean functions, covering topics like Max3SAT, Max3Coloring, and approximation algorithms for complex computational challenges.
Sketching Algorithms for Max-DICUT and Other CSPs
Explore algorithms for Maximum Directed Cut problem in graph theory, focusing on sketching techniques, space complexity, and applications to Constraint Satisfaction Problems.
Tea - A High-level Language and Runtime System for Automating Statistical Analysis
Introduces Tea, a declarative language for automating statistical analysis. Simplifies hypothesis testing by determining valid tests based on study design and assumptions, lowering barriers to accurate statistical research.
Approximating Constraint Satisfaction Problems on High Dimensional Expanders
Explore high-dimensional expanders and their role in approximating constraint satisfaction problems, with insights on natural collections and general expanders.
Optimal Streaming Approximations for All Boolean Max 2-CSPs and Max k-SAT
Explore streaming approximations for Boolean Max 2-CSPs and Max k-SAT, covering algorithms, lower bounds, and communication complexity in constraint satisfaction problems.
How Efficiently Can We Solve Unique Games? - Lecture
Explore the computational complexity of Unique Games and their efficient solutions in this insightful talk by Yotam Dikstein.