In this chapter we will introduce submodularity and some of its generalizations, illustrate how it arises in various applications, and discuss algorithms for optimizing submodular functions. Submodularity is a property of set functions with deep theoretical consequences and far-reaching applications. At first glance it seems very similar to concavity, in other ways it resembles convexity. It appears in a wide variety of applications: in Computer Science it has recently been identified and utilized in domains such as viral marketing [39], information gathering [44], image segmentation [10, 40, 36], document summarization [56], and speeding up satisfiability solvers [73]. Our emphasis in this chapter is on maximization; there are many important results and applications related to minimizing submodular functions that we do not cover. As a concrete running example, we will consider the problem of deploying sensors in a drinking water distribution network (see Figure 3.1) in order to detect contamination. In this domain, we may have a model of how contaminants, accidentally or maliciously introduced into the network, spread over time. Such a model then allows to quantify the benefit f(A) of deploying sensors at a particular set A of locations (junctions or pipes in the network) in terms of the detection performance (such as average time to detection). Based on this notion of utility, we then wish to find an optimal subset A ⊆ V of locations maximizing the utility, max A f(A) , subject to some constraints (such as bounded cost). This application requires solving a difficult real-world optimization problem, that can be handled with the techniques discussed in this chapter (Krause et al. [49] show in detail how submodular optimization can be applied in this domain.)

/pdf/submodular-function-maximization-1dos5dw1tz.pdf

Submodular Function Maximization

Many problems in artificial intelligence require adaptively making a sequence of decisions with uncertain outcomes under partial observability. Solving such stochastic optimization problems is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse AI applications including management of sensing resources, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.

/pdf/adaptive-submodularity-theory-and-applications-in-active-bxo0rq4zkk.pdf

Adaptive submodularity: theory and applications in active learning and stochastic optimization

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse applications including sensor placement, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.

/pdf/adaptive-submodularity-theory-and-applications-in-active-31e6lh9z78.pdf

Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally. We show that if the observations are noisy, perhaps surprisingly, GBS can perform very poorly. We develop EC2, a novel, greedy active learning algorithm and prove that it is competitive with the optimal policy, thus obtaining the first competitiveness guarantees for Bayesian active learning with noisy observations. Our bounds rely on a recently discovered diminishing returns property called adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. Our results hold even if the tests have non-uniform cost and their noise is correlated. We also propose EFFECX-TIVE, a particularly fast approximation of EC2, and evaluate it on a Bayesian experimental design problem involving human subjects, intended to tease apart competing economic theories of how people make decisions under uncertainty.

/pdf/near-optimal-bayesian-active-learning-with-noisy-57z537yc17.pdf

Near-Optimal Bayesian Active Learning with Noisy Observations

We address the problem of designing data structures in the presence of faults that may arbitrarily corrupt memory locations. More precisely, we assume that an adaptive adversary can arbitrarily overwrite the content of up to δ memory locations, that corrupted locations cannot be detected, and that only O(1) memory locations are safe. In this framework, we call a data structure resilient if it is able to operate correctly (at least) on the set of uncorrupted values. We present a resilient dictionary, implementing search, insert, and delete operations. Our dictionary has O(log n + δ) expected amortized time per operation, and O(n) space complexity, where n denotes the current number of keys in the dictionary. We also describe a deterministic resilient dictionary, with the same amortized cost per operation over a sequence of at least δϵ operations, where ϵ > 0 is an arbitrary constant. Finally, we show that any resilient comparison-based dictionary must take Ω(log n + δ) expected time per search. Our results are achieved by means of simple, new techniques which might be of independent interest for the design of other resilient algorithms.

/pdf/resilient-dictionaries-1t5t8chr6k.pdf

Resilient dictionaries

We introduce and study a problem that we refer to as the optimal split tree problem. The problem generalizes a number of problems including two classical tree construction problems including the Huffman tree problem and the optimal alphabetic tree. We show that the general split tree problem is NP-complete and analyze a greedy algorithm for its solution. We show that a simple modification of the greedy algorithm guarantees O(log n) approximation ratio. We construct an example for which this algorithm achieves Ω(log n/log log n) approximation ratio. We show that if all weights are equal and the optimal split tree is of depth O(log n). then the greedy algorithm guarantees O(log n/log log n) approximation ratio. We also extend our approximation algorithm to the construction of a search tree for partially ordered sets.

On an Optimal Split Tree Problem

The classic problem of searching an ordered list becomes more complicated when the answers to queries are not reliable. In the prefix-bounded error model, the number of incorrect responses at no point exceeds r times the number of questions, where r is a fixed positive fraction strictly less than ~. We present efficient algorithms for searching in this error model, and we also establish nontrivial lower bounds for sorting, maxima finding, and related problems.

Comparison-based search in the presence of errors

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On an Optimal Split Tree Problem

Comparison-based search in the presence of errors