Multi-Directional Recurrent Neural Network (M-RNN)
Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different – and often irregular – times. Accurate estimation of those missing measurements is critical for many reasons, including diagnosis, prognosis and treatment. Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data). We propose a new approach, based on a novel deep learning architecture that we call a Multi-directional Recurrent Neural Network (M-RNN) that interpolates within data streams and imputes across data streams. We demonstrate the power of our approach by applying it to five real-world medical datasets. We show that it provides dramatically improved estimation of missing measurements in comparison to 11 state-of-the-art benchmarks (including Spline and Cubic Interpolations, MICE, MissForest, matrix completion and several RNN methods); typical improvements in Root Mean Square Error are between 35% – 50%. Additional experiments based on the same five datasets demonstrate that the improvements provided by our method are extremely robust. …

StackExchange
Existing keyphrase generation studies suffer from the problems of generating duplicate phrases and deficient evaluation based on a fixed number of predicted phrases. We propose a recurrent generative model that generates multiple keyphrases sequentially from a text, with specific modules that promote generation diversity. We further propose two new metrics that consider a variable number of phrases. With both existing and proposed evaluation setups, our model demonstrates superior performance to baselines on three types of keyphrase generation datasets, including two newly introduced in this work: StackExchange and TextWorld ACG. In contrast to previous keyphrase generation approaches, our model generates sets of diverse keyphrases of a variable number. …

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. …

Maler
In this paper, we study adaptive online convex optimization, and aim to design a universal algorithm that achieves optimal regret bounds for multiple common types of loss functions. Existing universal methods are limited in the sense that they are optimal for only a subclass of loss functions. To address this limitation, we propose a novel online method, namely Maler, which enjoys the optimal $O(\sqrt{T})$, $O(d\log T)$ and $O(\log T)$ regret bounds for general convex, exponentially concave, and strongly convex functions respectively. The essential idea is to run multiple types of learning algorithms with different learning rates in parallel, and utilize a meta algorithm to track the best one on the fly. Empirical results demonstrate the effectiveness of our method. …