Deep plAckeTt-luce modEL wIth uNcertainty mEasurements (DATELINE)
The aggregation of k-ary preferences is a historical and important problem, since it has many real-world applications, such as peer grading, presidential elections and restaurant ranking. Meanwhile, variants of Plackett-Luce model has been applied to aggregate k-ary preferences. However, there are two urgent issues still existing in the current variants. First, most of them ignore feature information. Namely, they consider k-ary preferences instead of instance-dependent k-ary preferences. Second, these variants barely consider the uncertainty in k-ary preferences provided by agnostic crowds. In this paper, we propose Deep plAckeTt-luce modEL wIth uNcertainty mEasurements (DATELINE), which can address both issues simultaneously. To address the first issue, we employ deep neural networks mapping each instance into its ranking score in Plackett-Luce model. Then, we present a weighted Plackett-Luce model to solve the second issue, where the weight is a dynamic uncertainty vector measuring the worker quality. More importantly, we provide theoretical guarantees for DATELINE to justify its robustness. …

CODED
A powerful approach to detecting erroneous data is to check which potentially dirty data records are incompatible with a user’s domain knowledge. Previous approaches allow the user to specify domain knowledge in the form of logical constraints (e.g., functional dependency and denial constraints). We extend the constraint-based approach by introducing a novel class of statistical constraints (SCs). An SC treats each column as a random variable, and enforces an independence or dependence relationship between two (or a few) random variables. Statistical constraints are expressive, allowing the user to specify a wide range of domain knowledge, beyond traditional integrity constraints. Furthermore, they work harmoniously with downstream statistical modeling. We develop CODED, an SC-Oriented Data Error Detection system that supports three key tasks: (1) Checking whether an SC is violated or not on a given dataset, (2) Identify the top-k records that contribute the most to the violation of an SC, and (3) Checking whether a set of input SCs have conflicts or not. We present effective solutions for each task. Experiments on synthetic and real-world data illustrate how SCs apply to error detection, and provide evidence that CODED performs better than state-of-the-art approaches. …

DEFRAG
Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels. This task is faced with the dual challenge of high precision and scalability, with millisecond level prediction times being a benchmark. We propose DEFRAG, an adaptive feature agglomeration technique to accelerate extreme classification algorithms. Despite past works on feature clustering and selection, DEFRAG distinguishes itself in being able to scale to millions of features, and is especially beneficial when feature sets are sparse, which is typical of recommendation and multi-label datasets. The method comes with provable performance guarantees and performs efficient task-driven agglomeration to reduce feature dimensionalities by an order of magnitude or more. Experiments show that DEFRAG can not only reduce training and prediction times of several leading extreme classification algorithms by as much as 40%, but also be used for feature reconstruction to address the problem of missing features, as well as offer superior coverage on rare labels. …

ReSet
Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectures have a fixed structure and apply the same transformation to all input images. In this work, we develop a ResNet-based model that dynamically selects Computational Units (CU) for each input object from a learned set of transformations. Dynamic selection allows the network to learn a sequence of useful transformations and apply only required units to predict the image label. We compare our model to ResNet-38 architecture and achieve better results than the original ResNet on CIFAR-10.1 test set. While examining the produced paths, we discovered that the network learned different routes for images from different classes and similar routes for similar images. …