The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related to non-verbal human behavior. This framework can be used by researchers in the field who are interested in processing data from smartphones and Wearable devices.

We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are $\alpha$ strongly convex and $\beta$ smooth, to achieve the state-of-the-art dynamic regret, the previous work requires $O(\kappa)$ with $\kappa = \frac{\beta}{\alpha}$ queries of gradients at every iteration. But, our framework shows that the query complexity can be improved to be $O(1)$, which does not depend on $\kappa$. The improvement is significant for ill-conditioned problems because that their objective function usually has a large $\kappa$.

This book was either the assigned textbook or recommended reading in every Masters program I researched. Due to its advanced nature, you'll find that book #5 in this list -- An Introduction to Statistical Learning with Applications in R (ISLR) -- was written as a more accessible version, and even includes exercises in R. It's usually recommended for beginners in data science to master the content in ISLR before moving to ESL, where you'll get a more theoretical background. Just mastering ISLR is often enough for data analyst roles. Overall, ESL takes an applied, frequentist approach, as opposed to a Bayesian approach like in the next book. Exercises in this book are not only challenging, but also very useful for individuals generally interested in machine learning research.

Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique.

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Optical imaging techniques are important tools extensively used for probing a number of materials properties [1]. These imaging techniques are non-destructive and particularly convenient for dealing with biological and other complex materials [2]. Liquid crystals are among these materials widely studied via optical and image processing methods [3]. This occurs because liquid crystals are birefringent materials, and as such, simple polarized optical microscope imaging already access some of their important properties, including birefringence and sample thickness [4]. Moreover, this technique estimates the local ordering properties (for instance, the director distribution) across a sample when coupled with variable retarders and different algorithms for fast and sensitive measurements [5]. This approach is known as LC-PolScope [6] and has been used for fine imaging of defect cores in lyotropic liquid crystals [7] and can describe the orientational order of active nematics [8]. Despite the extensive use of optical imaging approaches in the study of liquid crystals [9-12], much less attention has been paid to the problem of extracting physical parameters directly from images of these materials. This is an important issue since several physical parameters of liquid crystals are only obtained by adjusting theoretical models to cumbersome and time demanding experimental results. Examples include the microscopic order parameter, from which several other parameters characterizing the nematic phase are dependent [3], and the pitch length of cholesteric liquid crystals.

Nowadays, CAPTCHAs are computer generated tests that human can pass but current computer systems can not. They have common usage in various web services in order to be able to detect a human from computer programs autonomously. In this way, owners can protect their web services from bots. In addition to visual CAPTCHAs which consist of distorted images, mostly test images, that a user must write some description about that image, there are a significant amount of audio CAPTCHAs as well. Briefly, audio CAPTCHAs are sound files which consist of human sound under heavy noise where the speaker pronounces a bunch of digits consecutively. Generally, in those sound files, there are some periodic and non-periodic noises to get difficult to recognize them with a program but not for a human listener. We gathered numerous randomly collected audio file to train and then test them using our SVM algorithm to be able to extract digits out of each conversation.

Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items. For this reason, they have gained prominence in many machine learning applications that rely on subset selection. However, sampling from a DPP over a ground set of size $N$ is a costly operation, requiring in general an $O(N^3)$ preprocessing cost and an $O(Nk^3)$ sampling cost for subsets of size $k$. We approach this problem by introducing DPPNets: generative deep models that produce DPP-like samples for arbitrary ground sets. We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors. We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPPs so powerful and unique. Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.

We consider a canonical revenue maximization problem where customers arrive sequentially; each customer is interested in buying one product, and the customer purchases the product if her valuation for it exceeds the price set by the seller. The valuations of customers are not observed by the seller; however, the seller can leverage contextual information available to her in the form of noisy covariate vectors describing the customer's history and the product's type to set prices. The seller can learn the relationship between covariates and customer valuations by experimenting with prices and observing transaction outcomes. We consider a semi-parametric model where the relationship between the expectation of the log of valuation and the covariates is linear (hence parametric) and the residual uncertainty distribution, i.e., the noise distribution, is non-parametric. We develop a pricing policy, DEEP-C, which learns this relationship with minimal exploration and in turn achieves optimal regret asymptotically in the time horizon.

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical proportions are closely connected to kernel functions on pairs of objects. Based on this result, we introduce the analogy kernel, which can be seen as a measure of how strongly four objects are in analogical relationship. As an application, we consider the problem of object ranking in the realm of preference learning, for which we develop a new method based on support vector machines trained with the analogy kernel. Our first experimental results for data sets from different domains (sports, education, tourism, etc.) are promising and suggest that our approach is competitive to state-of-the-art algorithms in terms of predictive accuracy.