research-article Share on Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Authors: Yao Wu Simon Fraser University, Burnaby, BC, Canada Simon Fraser University, Burnaby, BC, CanadaView Profile , Christopher DuBois Dato Inc., Seattle, WA, USA Dato Inc., Seattle, WA, USAView Profile , Alice X. Zheng Dato Inc., Seattle, WA, USA Dato Inc., Seattle, WA, USAView Profile , Martin Ester Simon Fraser University, Burnaby, BC, Canada Simon Fraser University, Burnaby, BC, CanadaView Profile Authors Info & Claims WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data MiningFebruary 2016Pages 153–162https://doi.org/10.1145/2835776.2835837Published:08 February 2016Publication History 551citation3,811DownloadsMetricsTotal Citations551Total Downloads3,811Last 12 Months393Last 6 weeks24 Get Citation Alerts

A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which satisfies all four of these requirements. The algorithm is flexible, scalable, and surprisingly straight-forward to implement as it is based on a modification of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and show that it matches or outperforms other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information.

Automatic image annotation is a difficult and highly relevant machine learning task. Recent advances have significantly improved the state-of-the-art in retrieval accuracy with algorithms based on nearest neighbor classification in carefully learned metric spaces. But this comes at a price of increased computational complexity during training and testing. We propose FastTag, a novel algorithm that achieves comparable results with two simple linear mappings that are co-regularized in a joint convex loss function. The loss function can be efficiently optimized in closed form updates, which allows us to incorporate a large number of image descriptors cheaply. On several standard real-world benchmark data sets, we demonstrate that FastTag matches the current state-of-the-art in tagging quality, yet reduces the training and testing times by several orders of magnitude and has lower asymptotic complexity.

In this paper, we propose a novel method to efficiently compute the top-K most similar items given a query item, where similarity is defined by the set of items that have the highest vector inner products with the query. The task is related to the classical k-Nearest-Neighbor problem, and is widely applicable in a number of domains such as information retrieval, online advertising and collaborative filtering. Our method assumes an in-memory representation of the dataset and is designed to scale to query lengths of 100,000s of terms. Our algorithm uses a generalized Holder's inequality to upper bound the inner product with the norms of the constituent vectors. We also propose a novel compression scheme that computes bounds for groups of candidate items, thereby speeding up computation and minimizing memory requirements per query. We conduct extensive experiments on the publicly available Wikipedia dataset, and demonstrate that, with a memory overhead of 21%, our method can provide 1-3 orders of magnitude improvement in query run-time compared to naive methods and state of the art competing methods. Our median top-10 word query time is 25 us on 7.5 million words and 2.3 million documents.

Perhaps surprisingly, no practical performance models exist for popular (and complex) client applications such as Adobe's Creative Suite, Microsoft's Office and Visual Studio, Mozilla, Halo 3, etc. There is currently no tool that automatically answers program developers', IT administrators' and end-users' simple what-if questions like "what happens to the performance of my favorite application X if I upgrade from Windows Vista to Windows 7?". This paper describes our approach towards constructing practical, versatile performance models to address this problem. The goal is to have these models be useful for application developers to help expand application testing coverage and for IT administrators to assist with understanding the performance consequences of a software, hardware or configuration change.

As computers have become ever more interconnected, the complexity of security configuration has exploded. Management tools have not kept pace, and we show that this has made identity snowball attacks into a critical danger. Identity snowball attacks leverage the users logged in to a first compromised host to launch additional attacks with those users' privileges on other hosts. To combat such attacks, we present Heat-ray, a system that combines machine learning, combinatorial optimization and attack graphs to scalably manage security configuration. Through evaluation on an organization with several hundred thousand users and machines, we show that Heat-ray allows IT administrators to reduce by 96% the number of machines that can be used to launch a large-scale identity snowball attack.

Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance differences between a pair of devices. There are two primary advantages to this approach. First, because are lative fitness model is constructed for a device pair, the application-device feedback of a closed workload can be captured (e.g., how the I/O arrival rate changes as the workload moves from device A to device B). Second, a relative fitness model allows performance and resource utilization to be used in place of workload characteristics. This is beneficial when workload characteristics are difficult to obtain or concisely express (e.g., rather than describe the spatio-temporal characteristics of a workload, one could use the observed cache behavior of device A to help predict the performance of B.

We describe a statistical approach to software debugging in the presence of multiple bugs. Due to sparse sampling issues and complex interaction between program predicates, many generic off-the-shelf algorithms fail to select useful bug predictors. Taking inspiration from bi-clustering algorithms, we propose an iterative collective voting scheme for the program runs and predicates. We demonstrate successful debugging results on several real world programs and a large debugging benchmark suite.

We present a statistical debugging algorithm that isolates bugs in programs containing multiple undiagnosed bugs. Earlier statistical algorithms that focus solely on identifying predictors that correlate with program failure perform poorly when there are multiple bugs. Our new technique separates the effects of different bugs and identifies predictors that are associated with individual bugs. These predictors reveal both the circumstances under which bugs occur as well as the frequencies of failure modes, making it easier to prioritize debugging efforts. Our algorithm is validated using several case studies, including examples in which the algorithm identified previously unknown, significant crashing bugs in widely used systems.

We present a novel strategy for automatically debugging programs given sampled data from thousands of actual user runs. Our goal is to pinpoint those features that are most correlated with crashes. This is accomplished by maximizing an appropriately defined utility function. It has analogies with intuitive debugging heuristics, and, as we demonstrate, is able to deal with various types of bugs that occur in real programs.

We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program's user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error.

The Kleinberg HITS and the Google PageRank algorithms are eigenvector methods for identifying ``authoritative'' or ``influential'' articles, given hyperlink or citation information. That such algorithms should give reliable or consistent answers is surely a desideratum, and in~\cite{ijcaiPaper}, we analyzed when they can be expected to give stable rankings under small perturbations to the linkage patterns. In this paper, we extend the analysis and show how it gives insight into ways of designing stable link analysis methods. This in turn motivates two new algorithms, whose performance we study empirically using citation data and web hyperlink data.