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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building ...Savoir plus
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size...Savoir plus
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning covers several aspects of the "optimism in the ...Savoir plus
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Optimization is a ubiquitous modeling tool and is often deployed in settings which repeatedly solve similar instances of the same problem. Amortized o...Savoir plus
Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional l...Savoir plus
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solv...Savoir plus
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting w...Savoir plus
Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for stud...Savoir plus
Active learning is a protocol for supervised machine learning in which a learning algorithm sequentially requests the labels of selected data points f...Savoir plus
This monograph presents some new concentration inequalities for Feynman-Kac particle processes. It analyzes different types of stochastic particle mod...Savoir plus
A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researc...Savoir plus
The current availability of powerful computers and huge data sets is creating new opportunities in computational mathematics to bring together concept...Savoir plus
Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling of complex object...Savoir plus
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an ...Savoir plus
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. T...Savoir plus
A multi-armed bandit problem - or, simply, a bandit problem - is a sequential allocation problem defined by a set of actions. At each time step, a uni...Savoir plus
Over the last decade, Approximate Message Passing (AMP) algorithms have become extremely popular in various structured high-dimensional statistical pr...Savoir plus
Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structura...Savoir plus
In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a ...Savoir plus
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast...Savoir plus
Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It...Savoir plus
Automated theorem proving represents a significant and long-standing area of research in computer science, with numerous applications. A large proport...Savoir plus
This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory ...Savoir plus