Learning a high-performance trade execution model via reinforcement learning (RL) requires interaction with the real dynamic market. However, the massive interactions required by direct RL would result in a significant training overhead. In this paper, ...

Financial time series forecasting is challenging due to limited sample size, correlated samples, low signal strengths, among others. Additional information with knowledge graphs (KGs) can allow for improved prediction and decision making. In this work, ...

With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and are characterized with multiple labels, thus exhibiting ...

In financial market, certain types of stochastic events are intrinsically impactful to the prediction of financial times series, such as stock return, while few existing research attempts have been made to incorporate stochastic event modeling to time ...

Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the ...

Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as ...

Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the following challenges, including (C1) how to incorporate explicit relationship ...

Financial time series analysis plays a central role in optimizing investment decision and hedging market risks. This is a challenging task as the problems are always accompanied by dual-level (i.e, data-level and task-level) heterogeneity. For instance, ...

Deep neural network clustering is superior to the conventional clustering methods due to deep feature extraction and nonlinear dimensionality reduction. Nevertheless, deep neural network leads to a rough representation regarding the inherent ...

Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to ...

User behavior modeling is essential in computational advertisement, which builds users' profiles by tracking their online behaviors and then delivers the relevant ads according to each user's interests and needs. Accurate models will lead to higher ...

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. The large amount of data associated with process variables monitored over time form a rich reservoir of information, ...

Big data analytics is the latest spotlight with all the glare of fame ranging from media coverage to booming start-up companies to eye-catching merges and acquisitions. On the contrary, the $336 billion industry of semiconductor was seen as an "old-...

This paper targets the problem of cargo pricing optimization in the air cargo business. Given the features associated with a pair of origination and destination, how can we simultaneously predict both the optimal price for the bid stage and the outcome ...