Monika Henzinger

Online stochastic packing applied to display ad allocation

Inspired by online ad allocation, we study online stochastic packing integer programs from theoretical and practical standpoints. We first present a near-optimal online algorithm for a general class of packing integer programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple dual training-based algorithm achieves a (1-o(1))- approximation guarantee in the random order stochastic model. This is a significant improvement over logarithmic or constant-factor approximations for the adversarial variants of the same problems (e.g. factor 1 - 1/e for online ad allocation, and log(m) for online routing). We then focus on the online display ad allocation problem and study the efficiency and fairness of various training-based and online allocation algorithms on data sets collected from real-life display ad allocation system. Our experimental evaluation confirms the effectiveness of training-based algorithms on real data sets, and also indicates an intrinsic trade-off between fairness and efficiency.

Maintaining Minimum Spanning Forests in Dynamic Graphs

We present the first fully dynamic algorithm for maintaining a minimum spanning forest in time

Purely URL-based topic classification

o(\sqrt n)

Exploring unknown environments

per operation. To be precise, the algorithm uses O(n1/3 log n) amortized time per update operation. The algorithm is fairly simple and deterministic. An immediate consequence is the first fully dynamic deterministic algorithm for maintaining connectivity and bipartiteness in amortized time O(n1/3 log n) per update, with O(1) worst case time per query.

Lower Bounds for Fully Dynamic Connectivity Problems in Graphs

Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classified using their content, but a URL-only classifier is preferable, (i) when speed is crucial, (ii) to enable content filtering before an (objection-able) web page is downloaded, (iii) when a pages content is hidden in images, (iv) to annotate hyperlinks in a personalized web browser, without fetching the target page, and (v) when a focused crawler wants to infer the topic of a target page before devoting bandwidth to download it. We apply a machine learning approach to the topic identification task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classifiers for each topic, we achieve typical F-measure values between 80 and 85, and a typical precision of around 85. We also ran experiments on a small data set of university web pages. For the task of classifying these pages into faculty, student, course and project pages, our methods improve over previous approaches by 13.8 points of F-measure.

Computing Vertex Connectivity

We consider exploration problems where a robot has to construct a complete map of an unknown environment. We assume that the environment is modeled by a directed, strongly connected graph. The robots task is to visit all nodes and edges of the graph using the minimum number R of edge traversals. Deng and Papadimitriou (Proceedings of the 31st Symposium on the Foundations of Computer Science, 1990, pp. 356{361) showed an upper bound for R of d O(d) m and Koutsoupias (reported by Deng and Papadimitriou) gave a lower bound of ›(d2m), where m is the number of edges in the graph and d is the minimum number of edges that have to be added to make the graph Eulerian. We give the rst subexponential algorithm for this exploration problem, which achieves an upper bound of d O(logd) m. We also show a matching lower bound of d ›(logd) m for our algorithm. Additionally, we give lower bounds of 2 ›(d) m, respectively, d ›(logd) m for various other natural exploration algorithms.

Parametric and kinetic minimum spanning trees

Abstract. We prove lower bounds on the complexity of maintaining fully dynamic k -edge or k -vertex connectivity in plane graphs and in (k-1) -vertex connected graphs. We show an amortized lower bound of

Maintaining Minimum Spanning Trees in Dynamic Graphs

\Omega

A Comprehensive Study of Features and Algorithms for URL-Based Topic Classification

(log n / {k (log log n} + log b)) per edge insertion, deletion, or query operation in the cell probe model, where b is the word size of the machine and n is the number of vertices in G . We also show an amortized lower bound of

Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams

\Omega