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1. A Stein-Papangelou Goodness-of-Fit Test for Point Processes.

   Yang, J; Rao, V; Neville, J. (January 2019, Artificial Intelligence and Statistics (AISTATS 2019))

   Point processes provide a powerful framework for modeling the distribution and interactions of events in time or space. Their flexibility has given rise to a variety of sophisticated models in statistics and machine learning, yet model diagnostic and criticism techniques re- main underdeveloped. In this work, we pro- pose a general Stein operator for point pro- cesses based on the Papangelou conditional intensity function. We then establish a kernel goodness-of-fit test by defining a Stein dis- crepancy measure for general point processes. Notably, our test also applies to non-Poisson point processes whose intensity functions con- tain intractable normalization constants due to the presence of complex interactions among points. We apply our proposed test to sev- eral point process models, and show that it outperforms a two-sample test based on the maximum mean discrepancy. 

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2. Semi-parametric Learning of Structured Temporal Point Processes

   Xu, G.; Wang, M.; Bian, J.; Huang, H.; Burch, T.; Andrade, S.; Zhang, J.; Guan, Y. (January 2020, Journal of machine learning research)

   We propose a general framework of using a multi-level log-Gaussian Cox process to model repeatedly observed point processes with complex structures; such type of data have become increasingly available in various areas including medical research, social sciences, economics, and finance due to technological advances. A novel nonparametric approach is developed to efficiently and consistently estimate the covariance functions of the latent Gaussian processes at all levels. To predict the functional principal component scores, we propose a consistent estimation procedure by maximizing the conditional likelihood of super-positions of point processes. We further extend our procedure to the bivariate point process case in which potential correlations between the processes can be assessed. Asymptotic properties of the proposed estimators are investigated, and the effectiveness of our procedures is illustrated through a simulation study and an application to a stock trading dataset. 

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3. Semi-parametric Learning of Structured Temporal Point Processes

   Xu, Ganggang; Wang, Ming; Bian, Jiangze; Huang, Hui; Burch, Timothy; Andrade, Sandro; Zhang, Jingfei; Guan, Yongtao (January 2020, Journal of machine learning research)

   null (Ed.)

   We propose a general framework of using a multi-level log-Gaussian Cox process to model repeatedly observed point processes with complex structures; such type of data has become increasingly available in various areas including medical research, social sciences, economics, and finance due to technological advances. A novel nonparametric approach is developed to efficiently and consistently estimate the covariance functions of the latent Gaussian processes at all levels. To predict the functional principal component scores, we propose a consistent estimation procedure by maximizing the conditional likelihood of super-positions of point processes. We further extend our procedure to the bivariate point process case in which potential correlations between the processes can be assessed. Asymptotic properties of the proposed estimators are investigated, and the effectiveness of our procedures is illustrated through a simulation study and an application to a stock trading dataset. 

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4. Peer Feedback Processes in the Game Industry

   https://doi.org/10.1145/3311350.3347176  

   Seering, Joseph; Mayol, Ray; Harpstead, Erik; Chen, Tianying; Cook, Amy; Hammer, Jessica (October 2019, Proceedings of the Annual Symposium on Computer-Human Interaction in Play)

   null (Ed.)

   A wide variety of design strategies, tools, and processes are used across the game industry. Prior work has shown that these processes are often collaborative, with experts in different domains contributing to different parts of the whole. However, the ways in which these professionals give and receive peer feedback have not yet been studied in depth. In this paper we present results from interviews with industry professionals at two game studios, describing the ways they give feedback. We propose a new, six step process that describes the full feedback cycle from making plans to receive feedback to reflecting and acting upon that feedback. This process serves as a starting point for researchers studying peer feedback in games, and allows for comparison of processes across different types of studios. It will also help studios formalize their understanding of their own processes and consider alternative processes that might better fit their needs. 

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5. Influence-aware attention for multivariate temporal point processes

   Shou, Xiao; Gao, Tian; Subramanian, Dharmashankar; Bhattacharjya, Debarun; Bennett, Kristin (April 2024, Conference on Causal Learning and Reasoning)

   Identifying the subset of events that influence events of interest from continuous time datasets is of great interest in various applications. Existing methods however often fail to produce accurate and interpretable results in a time-efficient manner. In this paper, we propose a neural model – Influence-Aware Attention for Multivariate Temporal Point Processes (IAA-MTPPs) – which leverages the powerful attention mechanism in transformers to capture temporal dynamics between event types, which is different from existing instance-to-instance attentions, using variational inference while maintaining interpretability. Given event sequences and a prior influence matrix, IAA-MTPP efficiently learns an approximate posterior by an Attention-to-Influence mechanism, and subsequently models the conditional likelihood of the sequences given a sampled influence through an Influence-to-Attention formulation. Both steps are completed efficiently inside a B-block multi-head self-attention layer, thus our end-to-end training with parallelizable transformer architecture enables faster training compared to sequential models such as RNNs. We demonstrate strong empirical performance compared to existing baselines on multiple synthetic and real benchmarks, including qualitative analysis for an application in decentralized finance. 

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