Stock movement prediction has always been a tough but attractive task for researchers in data mining and machine learning. Generally speaking, two challenges for stock time series prediction remain not well-explored. One is the over-fitting of deep learning models due to the limited data availability. The second one is potential domain shifts that may happen during the evolution of the stock time series. In this paper, we present Meta-Adaptive Stock movement prediction with two-StagE Representation learning (MASSER), a framework for stock movement prediction based on self-supervised learning and meta-learning. Specifically, we first design two-stage encoders to learn representations, the first-stage encoder aims to learn unified embeddings, and the second-stage encoder, which is based on the first stage, is used for temporal domain shift detection in the training stage via self-supervised learning. We formalize the problem of stock movement prediction into a standard meta-learning setting. Inspired by importance sampling, we estimate the sampling probability for tasks to balance the domain discrepancy caused by evolving temporal domains. Extensive experiment results on two open source datasets show that our experimental framework with the classical ResNet as backbone achieves improvements of 5% - 9.5% on average accuracy, compared to state-of-the-art baselines. Furthermore, We extend the standard setting of stock movement prediction to a more challenging online paradigm, which is close to the realistic interday trading scenarios. MASSER outperforms baselines in both online setting and backtesting.

4D printing encodes transformability over time, which empowers users to create artifacts by on-demand deformation. The creative process of 4D printing shape-changing artifacts can be challenging because of its discontinuous fabrication steps, such as digital designing, specific path planning, automatic printing and manual triggering. We hypothesize that switching from typical 4D printing reliant on 3D printers to a more “handcrafted” method can allow users to understand and continuously reflect upon the artifact and its transformability. Towards this vision, we introduce 4Doodle, a hybrid craft approach that integrates unique deformation controllability and five techniques for freehand 4D printing, using a 3D pen. To tackle the shape-changing challenges of uncertain hands-on fabrication, we develop a mixed reality system to help novices master the manual skills of 4D printing. We also demonstrate a series of 4D printed artifacts with fully human intervention. Finally, our user study shows that 4Doodle lowers the skill-acquisition barrier associated with handcrafting 4D printed artifacts, and it has great potential for creative production and spatial ability.

The areas of reinforcement learning and multi-armed bandits have recently seen significant innovation, while many application domains, such as e-commerce, are full of problems and challenges to which vanilla RL or MAB methods cannot directly apply. This workshop aims at filling this communication gap by creating a platform for researchers and practitioners from both the method/theory side and application side of the community. Having this platform now instead of at a later time is beneficial to all sides of the community: practitioners and frontline scientists are able to avoid re-inventing existing techniques; theory-oriented researchers can find motivation in industry problems, working within more realistic settings, and making real-world impact. The 2nd Multi-armed Bandits and Reinforcement Learning Workshop was a half day workshop co-located with the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2023) in Long Beach, California.

Contextual decision-making problems have witnessed extensive applications in various fields such as online content recommendation, personalized healthcare, and autonomous vehicles, where a core practical challenge is to select a suitable surrogate model for capturing unknown complicated reward functions. It is often the case that both high approximation accuracy and explicit uncertainty quantification are desired. In this work, we propose a neural network-accompanied Gaussian process (NN-AGP) model, which leverages neural networks to approximate the unknown and potentially complicated reward function regarding the contextual variable, and maintains a Gaussian process surrogate model with respect to the decision variable. Our model is shown to outperform existing approaches by offering better approximation accuracy thanks to the use of neural networks and possessing explicit uncertainty quantification from the Gaussian process. We also analyze the maximum information gain of the NN-AGP model and prove regret bounds for the corresponding algorithms. Moreover, we conduct experiments on both synthetic and practical problems, illustrating the effectiveness of our approach.