Presentations

Presentations I have prepared for different courses and lab seminar throughout my PhD

Deep Learning

Regularization: Dropout Method

Check here

References:

• Gareth,J., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer: Chicago.
• Hastie, T., Tibshirani, R. , & Friedman, J. . (2011) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer: New York
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 19291958.
• Wan, L., Zeiler, M., Zhang, S., LeCun, Y., & Fergus, R. (2013). Regularization of neural networks using dropconnect. ICML, (1), 109111.
• Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent Neural Network Regularization, (2013), 18.

Variational Autoencoders with Correlated Latent Features

Check here

References:

• Courville - Variational Autoencoder and Extensions(http://videolectures.net/deeplearning2015_courville_autoenco der_extension/)
• Wang - Deep Generative Models (http://www.cs.toronto.edu/~slwang/generative_model.pdf)
• Way et. al. - Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders (2018)
• Kingma et al - Auto-Encoding Variational Bayes (2014)
• Rezende et al - Stochastic back-propagation and variational inference in deep latent Gaussian models (2014)
• Casale et al – Gaussian Process Prior Variational Autoencoders (2018)

Functional Data Analysis:

Bootstrap for Functional Regression

Check here

References:

• Ferraty et al (2010) - On the Validity of the Bootstrap in Non-Parametric Functional Regression

Extending Correlation and Regression from Multivariate to Functional Data

Check here

References:

• Conway – A Course in Functional Analysis, 2nd edition , 1990, Chapter 5* - The Diagonalization of Compact Self-Adjoint Operators
• He, Mueller & Wang - Extending correlation and regression from multivariate to functional data , 2000.

Machine Learning and Data Science

Kernel Methods for Machine Learning: Kernel Mean Matching for Covariate Shift

Check here

References:

• Quinonero – Dataset Shift in Machine Learning (2012)

Case Study: NYC Open Data Service Requests

Check here

Graphical Models:

Sparse Matrix Graphical Models

Check here

References:

• Chenlei Leng & Cheng Yong Tang - Sparse Matrix Graphical Models (2012)

Doubly Functional Graphical Models in High Dimensions

Check here

References:

• Qiao, Qian & James - Doubly Functional Graphical Models in High Dimensions (2019)

Selected Papers on Multilevel Graphical Models

Check here

References:

• Pircalabelu et al (2020) - Zoom-in–out joint graphical lasso for different coarseness scales
• Zhang et al (2019) - A random covariance model for bi-level graphical modeling with application to resting-state fMRI data