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1. Wasserstein Learning of Deep Generative Point Process Models

   Xiao, Shuai ; Farajtabar, Mehrdad ; Ye, Xiaojing ; Yan, Junchi ; Song, Le ; Zha, Hongyuan ( January 2017 , Advances in neural information processing systems)

   Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena. Currently, they are often characterized via intensity function which limits model’s expressiveness due to unrealistic assumptions on its parametric form used in practice. Furthermore, they are learned via maximum likelihood approach which is prone to failure in multi-modal distributions of sequences. In this paper, we propose an intensity-free approach for point processes modeling that transforms nuisance processes to a target one. Furthermore, we train the model using a likelihood-free leveraging Wasserstein distance between pointmore »processes. Experiments on various synthetic and real-world data substantiate the superiority of the proposed point process model over conventional ones.« less
2. Learning Registered Point Processes from Idiosyncratic Observations

   Xu, Hongteng ; Carin, Lawrence ; Zha, Hongyuan ( January 2018 , Proceedings of Machine Learning Research)

   A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects. An alternating optimization method is proposed to learn a “registered” point process that accounts for shared structure, as well as “warping” functions that characterize idiosyncratic aspects of each observed sequence. Under reasonable constraints, in each iteration we update the sample-specific warping functions by solving a set of constrained nonlinear programming problems in parallel, and update the model by maximum likelihood estimation. The justifiability, complexity and robustness of the proposed method are investigated in detail, andmore »the influence of sequence stitching on the learning results is discussed empirically. Experiments on both synthetic and real-world data demonstrate that the method yields explainable point process models, achieving encouraging results compared to state-of-the-art methods.« less
3. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence

   https://doi.org/10.1093/jamia/ocaa322  

   Gao, Junyi ; Sharma, Rakshith ; Qian, Cheng ; Glass, Lucas M. ; Spaeder, Jeffrey ; Romberg, Justin ; Sun, Jimeng ; Xiao, Cao ( January 2021 , Journal of the American Medical Informatics Association)

   We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients’ claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

   We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of daysmore »into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties.

   STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model.

   By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization.

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4. Transformer Hawkes Process

   Zuo, Simiao ; Jiang, Haoming ; Li, Zichong ; Zhao, Tuo ; Zha, Hongyuan. ( July 2020 , International Conference on Machine Learning)

   Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets. These data often ex- hibit complicated short-term and long-term temporal dependencies. However, most of the ex- isting recurrent neural network-based point process models fail to capture such dependencies, and yield unreliable prediction performance. To address this issue, we propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long- term dependencies and meanwhile enjoys computational efficiency. Numerical experiments on various datasets show that THP outperforms existing models in terms of both likelihood and eventmore »prediction accuracy by a notable margin. Moreover, THP is quite general and can incorpo- rate additional structural knowledge. We provide a concrete example, where THP achieves im- proved prediction performance for learning multiple point processes when incorporating their relational information.« less
5. Deep Reinforcement Learning for Sequence-to-Sequence Models

   https://doi.org/10.1109/TNNLS.2019.2929141  

   Keneshloo, Yaser ; Shi, Tian ; Ramakrishnan, Naren ; Reddy, Chandan K. ( July 2020 , IEEE Transactions on Neural Networks and Learning Systems)

   In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide stateof-the-art performance in a wide variety of tasks, such as machine translation, headline generation, text summarization, speech-to-text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder–decoder models produce competitive results, many researchers have proposed additional improvements over these seq2seq models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistencymore »between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with seq2seq models that enable remembering long-term memories. We present some of the most recent frameworks that combine the concepts from RL and deep neural networks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization and provide some targeted experiments for these RL models, both in terms of performance and training time.« less