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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References:
- Trevor Hastie, Robert Tibshirani, J. F. (2004). The Elements of Statistical Learning (Vol. 99, Issue 466). Springer Science & Business Media.
- Christpher M. Bishop. (2006). Pattern Recognition and Machine Learning.
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
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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
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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
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Pages: 42
37 DeepAR
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Summary: Topic: DeepAR.
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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
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Pages: 42
32 Conformal Time Series Forecasting
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References:
- 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
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.
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Pages: 42
32 Review of Time Series Forecasting
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References:
- 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
Summary: Lim B, Zohren S. Time Series Forecasting With Deep Learning: A Survey. arXiv [stat.ML]. 2020. Available: http://arxiv.org/abs/2004.13408
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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
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Pages: 42
32 Causal Inference
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Summary: Alexa will lead a discussion on causal inference.
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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