From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Nonlinear dimensionality reduction by locally linear embedding.

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

Users of Internet services are increasingly intolerant of delays and outages, while demanding a consistent online experience. A website that is down or misbehaving is reported within seconds, often with an embarrassing screenshot that spreads through the news like wildfire. Among these failures, the most notorious are the ones that manifest arbitrary behavior, such as returning the wrong content to users or accidentally deleting their data. Unfortunately, protecting against such failures—whether due to misconfigurations, bugs, or even malice—is prohibitively expensive, because most existing solutions do not scale beyond a single server's performance. As a result, these solutions are not used for customer-facing services, where scalability is required to cope with large user populations. 
This thesis describes new systems and algorithms for tolerating arbitrary failures in Internet services, inspired by real-world debacles. Unlike prior work, our solutions are highly scalable. Our approach integrates theoretical innovations into the later stages of system design, giving robust guarantees that are also practical. We begin with a real failure that occurred in the indexing technique used by a certain database provider, and explain theoretically why the technique failed. We remedy the technique by introducing a new class of tree data structures, called relaxed trees, with provably good properties. Our analysis of relaxed trees makes use of exponential potential functions. 
Then, we describe a general system for tolerating arbitrary failures, called Prophecy, that delivers scalable performance on read-mostly workloads. With a modest trust assumption, Prophecy is practical for modern Internet services, as our evaluation confirms. Finally, we devise two techniques to scale this fault tolerance to very large-scale systems and general workloads. The first is an algorithm for securely composing many small replica groups, subject to an adversary that can coordinate faulty nodes across the groups dynamically. The second is a technique for improving the fault tolerance within each replica group, by adding small, trusted broadcast channels that mitigate the impact of faulty nodes.

New systems and algorithms for scalable fault tolerance

Let X, $Y \subset \{ 0,1 \}^* $ We say Y codes X if every $x \in X$ can be obtained by applying a short program to some $y \in Y$ We are interested in sets Y that code X robustly in the sense that even if we delete an arbitrary subset $Y' \subset Y$ of size k, say, the remaining set of strings $Y\backslash Y'$ still codes X In general, this can be achieved only by making in some sense more than k copies of each $x \in X$ and distributing these copies on different strings Y Thus if the strings in X and Y have the same length, then $# \,Y\geqq ( k + 1 )# X$ If we allow coding of X by Y in a way that every $x \in X$ is obtained from strings x, $z \in Y$ by application of a short program, then we can do betterLet $Y = \{ \oplus _{x \in S} x |S \subset X \}$ where $ \oplus $ denotes bitwise sum mod 2 Then $# Y = 2^{ # X} $ Yet Y codes X robustly for $k = 2^{ # X - 1} - 1$ This paper explores the limitations of coding schemes of this nature

Coding strings by pairs of strings

We consider the problem of maintaining persistent lists subject to concatenation and to insertions and deletions at both ends. Updates to a persistent data structure are nondestructive-each operation produces a new list incorporating the change while keeping intact the list or lists to which it applies. Although general techniques exist for making data structures persistent, these techniques fail for structures that are subject to operations, such as catenation, that combine two or more versions. In this paper we develop a simple implementation of persistent double-ended queues with catenation that supports all deque operations in constant amortized time.

Simple Confluently Persistent Catenable Lists (Extended Abstract)

A new algorithm for finding weak components

We introduce the zip tree, a form of randomized binary search tree that integrates previous ideas into one practical, performant, and pleasant-to-implement package. A zip tree is a binary search tree in which each node has a numeric rank and the tree is (max)-heap-ordered with respect to ranks, with rank ties broken in favor of smaller keys. Zip trees are essentially treaps (Seidel and Aragon 1996), except that ranks are drawn from a geometric distribution instead of a uniform distribution, and we allow rank ties. These changes enable us to use fewer random bits per node. We perform insertions and deletions by unmerging and merging paths ("unzipping" and "zipping") rather than by doing rotations, which avoids some pointer changes and improves efficiency. The methods of zipping and unzipping take inspiration from previous top-down approaches to insertion and deletion (Stephenson 1980; Mart\'inez and Roura 1998; Sprugnoli 1980). From a theoretical standpoint, this work provides two main results. First, zip trees require only $O(\log \log n)$ bits (with high probability) to represent the largest rank in an $n$-node binary search tree; previous data structures require $O(\log n)$ bits for the largest rank. Second, zip trees are naturally isomorphic to skip lists (Pugh 1990), and simplify the mapping of (Dean and Jones 2007) between skip lists and binary search trees.

Robert E. Tarjan

Papers

New systems and algorithms for scalable fault tolerance

Coding strings by pairs of strings

Simple Confluently Persistent Catenable Lists (Extended Abstract)

A new algorithm for finding weak components

Zip Trees