Interactive Submodular Set Cover

We introduce a natural generalization of submodular set cover and exact active learning with a finite hypothesis class (query learning). We call this new problem interactive submodular set cover. Applications include advertising in social networks with hidden information. We give an approximation guarantee for a novel greedy algorithm and give a hardness of approximation result which matches up to constant factors. We also discuss negative results for simpler approaches and present encouraging early experimental results.Comment: 15 pages, 1 figur

Phylogenetic relationships and systematics of the Amazonian poison frog genus Ameerega using ultraconserved genomic elements

The Amazonian poison frog genus Ameerega is one of the largest yet most understudied of the brightly colored genera in the anuran family Dendrobatidae, with 30 described species ranging throughout tropical South America. Phylogenetic analyses of Ameerega are highly discordant, lacking consistency due to variation in data types and methods, and often with limited coverage of species diversity in the genus. Here, we present a comprehensive phylogenomic reconstruction of Ameerega, utilizing state-of-the-art sequence capture techniques and phylogenetic methods. We sequenced thousands of ultraconserved elements from over 100 tissue samples, representing almost every described Ameerega species, as well as undescribed cryptic diversity. We generated topologies using maximum likelihood and coalescent methods and compared the use of maximum likelihood and Bayesian methods for estimating divergence times. Our phylogenetic inference diverged strongly from those of previous studies, and we recommend steps to bring Ameerega taxonomy in line with the new phylogeny. We place several species in a phylogeny for the first time, as well as provide evidence for six potential candidate species. We estimate that Ameerega experienced a rapid radiation approximately 7–11 million years ago and that the ancestor of all Ameerega was likely an aposematic, montane species. This study underscores the utility of phylogenomic data in improving our understanding of the phylogeny of understudied clades and making novel inferences about their evolution

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201

Active Learning and Submodular Functions

Thesis (Ph.D.)--University of Washington, 2012Active learning is a machine learning setting where the learning algorithm decides what data is labeled. Submodular functions are a class of set functions for which many optimization problems have efficient exact or approximate algorithms. We examine their connections. 1. We propose a new class of interactive submodular optimization problems which connect and generalize submodular optimization and active learning over a finite query set. We derive greedy algorithms with approximately optimal worst-case cost. These analyses apply to exact learning, approximate learning, learning in the presence of adversarial noise, and applications that mix learning and covering. 2. We consider active learning in a batch, transductive setting where the learning algorithm selects a set of examples to be labeled at once. In this setting we derive new error bounds which use symmetric submodular functions for regularization, and we give algorithms which approximately minimize these bounds. 3. We consider a repeated active learning setting where the learning algorithm solves a sequence of related learning problems. We propose an approach to this problem based on a new online prediction version of submodular set cover. A common theme in these results is the use of tools from submodular optimization to extend the breadth and depth of learning theory with an emphasis on non-stochastic settings

Online Submodular Set Cover, Ranking, and Repeated Active Learning

We propose an online prediction version of submodular set cover with connections to ranking and repeated active learning. In each round, the learning algorithm chooses a sequence of items. The algorithm then receives a monotone submodular function and suffers loss equal to the cover time of the function: the number of items needed, when items are selected in order of the chosen sequence, to achieve a coverage constraint. We develop an online learning algorithm whose loss converges to approximately that of the best sequence in hindsight. Our proposed algorithm is readily extended to a setting where multiple functions are revealed at each round and to bandit and contextual bandit settings.