Conditional Probability Estimation

An online journal club on the topics Conditional Probability Estimation. Our cover topics on all sorts of probabilistic approach, such as VAE, normalizing, graph neural network, probabilistic time series forecasting.

Introduction: Conditional Probability Estimation

References for Probability Estimation Club

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Summary: A list of references for our online discussions.
Pages: 42

Inferring causal impact using Bayesian structural time-series models

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Summary: Our topic for this session is Inferring causal impact using Bayesian structural time-series models (arXiv:1506.00356). Abstract Abstract of Inferring causal impact using Bayesian structural time-series models (arXiv:1506.00356): An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response in a synthetic control that would have occurred had no intervention taken place.
Pages: 42

40 M Competition

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Summary: We will discuss the M competition. @小紫花: M5: 2020 年的一个比赛，预测沃尔玛在米国 3 个州、 10 个店、3000 多个产品的销售，要求预测 28 天。两个比赛：预测一个中值，或者预测一个分布（9 个数）。今年有 M6 官网，指引 PDF https://mofc.unic.ac.cy/m5-competition/ 中值 https://www.kaggle.com/competitions/m5-forecasting-accuracy/ 分布 https://www.kaggle.com/competitions/m5-forecasting-uncertainty 比赛背景、组织、运营总结 https://www.sciencedirect.com/science/article/pii/S0169207021001187 中值预测总结 https://www.sciencedirect.com/science/article/pii/S0169207021001874 分布预测总结（我比较感兴趣） https://www.sciencedirect.com/science/article/pii/S0169207021001722 一篇评论文章 https://www.sciencedirect.com/science/article/abs/pii/S016920702100128X 对讨论的回复 https://www.sciencedirect.com/science/article/abs/pii/S0169207022000644 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

39 Data Augmentation for Time Series

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Summary: Wen Q, Sun L, Yang F, Song X, Gao J, Wang X, et al. Time Series Data Augmentation for Deep Learning: A Survey. arXiv [cs.LG]. 2020. Available: http://arxiv.org/abs/2002.12478 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

38 Temporal Fusion Transformer

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Summary: Lim B, Arik SO, Loeff N, Pfister T. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. In: arXiv.org [Internet]. 19 Dec 2019 [cited 9 Jul 2022]. Available: https://arxiv.org/abs/1912.09363 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

37 DeepAR

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Summary: Topic: DeepAR. Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

36 Evaluating time series forecasting models

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Summary: Our topic for this session is Cerqueira V, Torgo L, Mozetic I. Evaluating time series forecasting models: An empirical study on performance estimation methods. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1905.11744 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

32 Conformal Time Series Forecasting

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Summary: We start our new journey on time series by sharing and discussing two review papers: Lim B, Zohren S. Time Series Forecasting With Deep Learning: A Survey. arXiv [stat.ML]. 2020. Available: http://arxiv.org/abs/2004.13408 Gneiting T, Katzfuss M. Probabilistic Forecasting. Annu Rev Stat Appl. 2014;1: 125–151. doi:10.1146/annurev-statistics-062713-085831 (pdf) Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

32 Review of Time Series Forecasting

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Summary: Lim B, Zohren S. Time Series Forecasting With Deep Learning: A Survey. arXiv [stat.ML]. 2020. Available: http://arxiv.org/abs/2004.13408 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

32 Counterfactual Explanation in Multivariate Time Series

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Summary: Ates E, Aksar B, Leung VJ, Coskun AK. Counterfactual Explanations for Machine Learning on Multivariate Time Series Data. arXiv [cs.LG]. 2020. Available: http://arxiv.org/abs/2008.10781 Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

32 Causal Inference

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Summary: Alexa will lead a discussion on causal inference. Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.
Pages: 42

31 Uncertainty in Deep Learning

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Summary: Topic: uncertainty in deep learning References: Gawlikowski, J. et al. A Survey of Uncertainty in Deep Neural Networks. Arxiv (2021). Jospin, L. V., Buntine, W., Boussaid, F., Laga, H. & Bennamoun, M. Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users. Arxiv (2020). Gal, Yarin. “Uncertainty in deep learning.” (2016): 3. Use the following timezone tool or click on the “Add to Calendar” button on the sidebar.
Pages: 42