Time Series and Machine Learning Reading Group
Oct 2023-Feb 2024, University of Southampton
In this semester we will read a series of papers on deep nerual network (DNN) theories and applications, nonparametric statistics, and casual inference (CI).
This reading group is hybrid –- we meet weekly on Friday 14:00-15:30 (UK time), both at B54/7031 and via MS Teams. Feel free to choose your preferred method to join in.
Please check this website regularly for the most up-to-date arrangement.
Materials
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Week 1. Nonparametric Regression Using Deep Neural Networks with ReLU Activation Function (part 1)
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Week 2. Nonparametric Regression Using Deep Neural Networks with ReLU Activation Function (part 2)
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Week 3. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (part 1)
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Week 4. Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
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Week 5. On Deep Learning as a Remedy for the Curse of Dimensionality in Nonparametric Regression
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Week 6. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (part 2)
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Week 7. Learning without Concentration for General Loss Functions (part 1)
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Week 8. Learning without Concentration for General Loss Functions (part 2)
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Week 9. Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank Scores
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Week 10. Anomaly Detection of Time Series with Smoothness-inducing Sequential Variational Auto-encoder
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Week 11. Mixtures of large-scale dynamic functional brain network modes (part 1)
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Week 12. Mixtures of large-scale dynamic functional brain network modes (part 2)
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Week 13. Deep nonparametric regression on approximate manifolds: non-asymptotic error bounds with polynomial prefactors (part 1)
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Week 14. Deep nonparametric regression on approximate manifolds: non-asymptotic error bounds with polynomial prefactors (part 2)
Group Members
Please contact Chao if want to join in the group
Supplementary References
- Introduction to Nonparametric Estimation.
Alexandre Tsybakov, Springer, 2009.
- Neural Network Learning—Theoretical Fundations
Martin Anthony and Peter L. Bartlett, Cambridge University Press, 1999
- A First Course in Casual Inferences.
Peng Ding, 2023.
- A Distribution-Free Theory of Nonparametric Regression.
László Györfi, Michael Kohler, Adam Krzyżak, and Harro Walk, Springer, 2002.
Roles of Presenter and Discussant
If it is your first time attend a reading group, you might find the reading group tips by Lester Mackey and Percy Liang helpful.
Every time we will have one people (presenter) present the main contents and another people (discussant) raise questions and lead the discussion.
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As a presenter: you should have an in-depth reading and develop a solid understanding of all the details in the assigned topic. You should prepare well, and make sure you deliver a logically clear and technically accessible presentation. In short words, it is your job to have everyone in the meeting understand the main ideas of the reading.
-
As a discussant: you should be more familiar with the content than if you were simply in the group. You don’t need to know everything. You can pause the presentation, ask questions (to the presenter or to the audience), and facilitate discussions. It is your job to help the presenter to have everyone (yourself included!) in the meeting understand the main ideas of the reading and having learned something.
Before each session, although not compulsory I would recommend following amount of time spent on reading:
- Presenter: > 10 hours;
- Discussant: 5 hours;
- General audience: 2 hours.
If you encounter any problem during your reading, feel free to discuss with me or other staff members.
Past Reading Groups:
Webpage maintained by Chao Zheng. Last updated on 18/11/2022
Time Series and Machine Learning Reading Group
Oct 2023-Feb 2024, University of Southampton
In this semester we will read a series of papers on deep nerual network (DNN) theories and applications, nonparametric statistics, and casual inference (CI).
This reading group is hybrid –- we meet weekly on Friday 14:00-15:30 (UK time), both at B54/7031 and via MS Teams. Feel free to choose your preferred method to join in.
Timetable (provisional)
Please check this website regularly for the most up-to-date arrangement.
Materials
Week 1. Nonparametric Regression Using Deep Neural Networks with ReLU Activation Function (part 1)
Week 2. Nonparametric Regression Using Deep Neural Networks with ReLU Activation Function (part 2)
Week 3. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (part 1)
Week 4. Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks
Week 5. On Deep Learning as a Remedy for the Curse of Dimensionality in Nonparametric Regression
Week 6. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (part 2)
Week 7. Learning without Concentration for General Loss Functions (part 1)
Week 8. Learning without Concentration for General Loss Functions (part 2)
Week 9. Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank Scores
Week 10. Anomaly Detection of Time Series with Smoothness-inducing Sequential Variational Auto-encoder
Week 11. Mixtures of large-scale dynamic functional brain network modes (part 1)
Week 12. Mixtures of large-scale dynamic functional brain network modes (part 2)
Week 13. Deep nonparametric regression on approximate manifolds: non-asymptotic error bounds with polynomial prefactors (part 1)
Week 14. Deep nonparametric regression on approximate manifolds: non-asymptotic error bounds with polynomial prefactors (part 2)
Group Members
Please contact Chao if want to join in the group
Supplementary References
Alexandre Tsybakov, Springer, 2009.
Martin Anthony and Peter L. Bartlett, Cambridge University Press, 1999
Peng Ding, 2023.
László Györfi, Michael Kohler, Adam Krzyżak, and Harro Walk, Springer, 2002.
Roles of Presenter and Discussant
If it is your first time attend a reading group, you might find the reading group tips by Lester Mackey and Percy Liang helpful.
Every time we will have one people (presenter) present the main contents and another people (discussant) raise questions and lead the discussion.
As a presenter: you should have an in-depth reading and develop a solid understanding of all the details in the assigned topic. You should prepare well, and make sure you deliver a logically clear and technically accessible presentation. In short words, it is your job to have everyone in the meeting understand the main ideas of the reading.
As a discussant: you should be more familiar with the content than if you were simply in the group. You don’t need to know everything. You can pause the presentation, ask questions (to the presenter or to the audience), and facilitate discussions. It is your job to help the presenter to have everyone (yourself included!) in the meeting understand the main ideas of the reading and having learned something.
Before each session, although not compulsory I would recommend following amount of time spent on reading:
Past Reading Groups:
Webpage maintained by Chao Zheng. Last updated on 18/11/2022