311 - 320 of 366 results for: CS

This course introduces advanced formal systems and programming languages as well as techniques to reason formally about them. Possible systems of study include: the lambda calculus, System F, the Pi and Spi calculi, simply-typed languages, security type systems for non-interference, robust safety, linear types, ownership types, session types, logical relations and semantic models etc.

A research seminar on computational complexity theory. The focus of this year's offering will be on concrete complexity, a major strand of research in modern complexity theory. We will cover fundamental techniques and major results concerning basic models of computation such as circuits, decision trees, branching problems, and halfspaces.

Introduction to quantum complexity theory. Topics include: the class BQP and its relation to other complexity classes; quantum query and communication complexity; quantum proof systems, Hamiltonian complexity, and the quantum PCP conjecture; the complexity & verification of quantum sampling experiments; and quantum cryptography. Prerequisites: background in quantum computing and computational complexity theory

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles. Prerequisites: some familiarity with probability, programming, and multivariable calculus.

In this course we will explore the current state of research in the field of AI alignment, which seeks to bring increasingly intelligent AI systems in line with human values and interests. As the energy in the AI alignment landscape has been increasingly focused on political considerations, we seek to create a space to discuss which direction we should be pointing in, now that we have a better idea of what AI scaling will look like in the near future. This is a philosophical task, and we will invite several speakers that are philosophical in persuasion, but we also find that several of the most relevant philosophical questions cannot be asked without a strong technical familiarity with the specifics of language models and reinforcement learning. The format will consist of weekly lectures in which speakers present their relationships to the alignment problem and their current research approaches. Before each speaker, we will have some corresponding assigned readings and we will assign some form of active engagement with the material: we will accept a blog post in response to the ideas in the readings, but we will encourage jupyter notebooks that engage with the technical material directly. Therefore this course requires research experience, preferably using mathematical and programming tools (e.g. Python, PyTorch, calculus), and is a graduate level course, open to advanced undergraduates.

An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;algorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.

(Previously numbered CS 369G.) Designing algorithms for efficient processing of large data sets poses unique challenges. This course will discuss algorithmic paradigms that have been developed to efficiently process data sets that are much larger than available memory. We will cover streaming algorithms and sketching methods that produce compact datanstructures, dimension reduction methods that preserve geometric structure, efficient algorithms for numerical linear algebra, graph sparsification methods, as well as impossibility results for these techniques.

Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure. Prerequisite: linear algebra, multivariable calculus, probability, and proofs.