We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Classification and diagnosis of diabetes

Matrix Factorization Techniques for Recommender Systems

What is Twitter

We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method's usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method's explanations improved the participants' understanding of the model.

/pdf/explaining-prediction-models-and-individual-predictions-with-351ksyjxjq.pdf

Explaining prediction models and individual predictions with feature contributions

Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then trains a cross-domain mapping function to encode such distance information by exploiting both overlapping users as labeled data and all the items as unlabeled data. In addition, SSCDR adopts an effective inference technique that predicts the latent vectors of cold-start users by aggregating their neighborhood information. Our extensive experiments on different CDR scenarios show that SSCDR outperforms the state-of-the-art methods in terms of CDR accuracy, particularly in the realistic settings that a small portion of users overlap between two domains.

Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users

With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data streams. However, existing LOF-based algorithms for data streams still suffer from two inherent limitations: 1) Large amount of memory space is required. 2) A long sequence of outliers is not detected. In this paper, we propose a new outlier detection algorithm for data streams, called DILOF that effectively overcomes the limitations. To this end, we first develop a novel density-based sampling algorithm to summarize past data and then propose a new strategy for detecting a sequence of outliers. It is worth noting that our proposing algorithms do not require any prior knowledge or assumptions on data distribution. Moreover, we accelerate the execution time of DILOF about 15 times by developing a powerful distance approximation technique. Our comprehensive experiments on real-world datasets demonstrate that DILOF significantly outperforms the state-of-the-art competitors in terms of accuracy and execution time. The source code for the proposed algorithm is available at our website: http://di.postech.ac.kr/DILOF.

DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams

Background
Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques.

/pdf/developing-a-hybrid-dictionary-based-bio-entity-recognition-2c810siotf.pdf

Developing a hybrid dictionary-based bio-entity recognition technique

Active sampling (also called active learning or selective sampling) has been extensively researched for classification and rank learning methods, which is to select the most informative samples from unlabeled data such that, once the samples are labeled, the accuracy of the function learned from the samples is maximized. While active sampling methods require learning a function at each iteration to find the most informative samples, this paper proposes passive sampling techniques for regression, which find the informative samples not based on the learned function but based on the samples' geometric characteristics in the feature space. Passive sampling is more efficient than active sampling, as it does not require, at each iteration, learning and validating the regression functions and evaluating the unlabeled data using the function. For regression, passive sampling is also more effective, Active sampling for regression suffers from serious performance fluctuations in practice, because it selects the samples of highest regression errors and such samples are likely noisy. Passive sampling, on the other hand, shows more stable performance. We observe from our extensive experiments that our passive sampling methods perform even better than the ``omniscient'' active sampling that knows the labels of unlabeled data.

Passive Sampling for Regression

Multi-aspect data appear frequently in many web-related applications. For example, product reviews are quadruplets of (user, product, keyword, timestamp). How can we analyze such web-scale multi-aspect data? Can we analyze them on an off-the-shelf workstation with limited amount of memory? Tucker decomposition has been widely used for discovering patterns in relationships among entities in multi-aspect data, naturally expressed as high-order tensors. However, existing algorithms for Tucker decomposition have limited scalability, and especially, fail to decompose high-order tensors since they explicitly materialize intermediate data, whose size rapidly grows as the order increases (≥ 4). We call this problem M-Bottleneck ("Materialization Bottleneck"). To avoid M-Bottleneck, we propose S-HOT, a scalable high-order tucker decomposition method that employs the on-the-fly computation to minimize the materialized intermediate data. Moreover, S-HOT is designed for handling disk-resident tensors, too large to fit in memory, without loading them all in memory at once. We provide theoretical analysis on the amount of memory space and the number of scans of data required by S-HOT. In our experiments, S-HOT showed better scalability not only with the order but also with the dimensionality and the rank than baseline methods. In particular, S-HOT decomposed tensors 1000× larger than baseline methods in terms dimensionality. S- HOT also successfully analyzed real-world tensors that are both large-scale and high-order on an off-the-shelf workstation with limited amount of memory, while baseline methods failed. The source code of S-HOT is publicly available at http://dm.postech.ac.kr/shot to encourage reproducibility.

Hwanjo Yu

Papers

Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users

DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams

Developing a hybrid dictionary-based bio-entity recognition technique

Passive Sampling for Regression

S-HOT: Scalable High-Order Tucker Decomposition