Yale University CPSC745 - S2017


Advanced Topics in Machine Learning & Data Mining

Spring 2018

Instructors:Guy Wolf&Smita Krishnaswamy

TA: David van Dijk (david.vandijk@yale.edu)

Use this link to return to the main page

Recommended papers

The following is a tentative list of recommended papers for student presentations:

1.   Ronald R Coifman, Yoel Shkolnisky, Fred J Sigworth, and Amit Singer. Graph laplacian tomography from unknown random projections. IEEE Transactions on Image Processing, 17(10):1891–1899, 2008.

2.   Rui Xu, Steven Damelin, Boaz Nadler, and Donald C Wunsch. Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps. Artificial intelligence in medicine, 48(2):91–98, 2010.

3.   Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, and Yuval Kluger. A deep learning approach to unsupervised ensemble learning. In International Conference on Machine Learning, pages 30–39, 2016.

4.   Uri Shaham, Kelly P Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth Montgomery, and Yuval Kluger. Removal of batch effects using distribution-matching residual networks. Bioinformatics, page btx196, 2017.

5.   Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, and Yuval Kluger. Spectralnet: Spectral clustering using deep neural networks. arXiv preprint arXiv:1801.01587, 2018.

6.   Gal Mishne, Uri Shaham, Alexander Cloninger, and Israel Cohen. Diffusion nets. Applied and Computational Harmonic Analysis, 2017.

7.   Gautam Pai, Ronen Talmon, and Ron Kimmel. Parametric manifold learning via sparse multidimensional scaling. arXiv preprint arXiv:1711.06011, 2017.

8.   Hau-tieng Wu, Ronen Talmon, and Yu-Lun Lo. Assess sleep stage by modern signal processing techniques. IEEE Transactions on Biomedical Engineering, 62(4):1159–1168, 2015.

9.   Carmeline J Dsilva, Ronen Talmon, Ronald R Coifman, and Ioannis G Kevrekidis. Parsimonious representation of nonlinear dynamical systems through manifold learning: A chemotaxis case study. Applied and Computational Harmonic Analysis, 2015.

10.   Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten. Classification of covariance matrices using a riemannian-based kernel for bci applications. Neurocomputing, 112:172–178, 2013.

11.   Laurent Bonnet, Fabien Lotte, and Anatole Lécuyer. Two brains, one game: design and evaluation of a multiuser bci video game based on motor imagery. IEEE Transactions on Computational Intelligence and AI in games, 5(2):185–198, 2013.

12.   Steven Lemm, Benjamin Blankertz, Thorsten Dickhaus, and Klaus-Robert Müller. Introduction to machine learning for brain imaging. Neuroimage, 56(2):387–399, 2011.

13.   Alex Van Esbroeck, Landon Smith, Zeeshan Syed, Satinder Singh, and Zahi Karam. Multi-task seizure detection: addressing intra-patient variation in seizure morphologies. Machine Learning, 102(3):309–321, 2016.

14.   Dominique Duncan, Ronen Talmon, Hitten P Zaveri, and Ronald R Coifman. Identifying preseizure state in intracranial EEG data using diffusion kernels. Mathematical Biosciences and Engineering, 10(3):579–590, 2013.

15.   Polina Mamoshina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov. Applications of deep learning in biomedicine. Molecular Pharmaceutics, 13(5):1445–1454, 2016.

16.   Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

17.   Carl Doersch. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908, 2016.

18.   Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.

19.   Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026, 2013.

20.   Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111–3119, 2013.

21.   Ronen Talmon and Ronald R Coifman. Empirical intrinsic geometry for nonlinear modeling and time series filtering. Proceedings of the National Academy of Sciences, 110(31):12535–12540, 2013.

22.   Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. Deep survival: A deep cox proportional hazards network. arXiv preprint arXiv:1606.00931, 2016.

23.   Uri Shalit, Fredrik Johansson, and David Sontag. Estimating individual treatment effect: generalization bounds and algorithms. arXiv preprint arXiv:1606.03976, 2016.

24.   Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6, 2016.

25.   Smita Krishnaswamy, Matthew H Spitzer, Michael Mingueneau, Sean C Bendall, Oren Litvin, Erica Stone, Dana Peer, and Garry P Nolan. Conditional density-based analysis of t cell signaling in single-cell data. Science, 346(6213):1250689, 2014.

26.   Jacob H Levine, Erin F Simonds, Sean C Bendall, Kara L Davis, D Amir El-ad, Michelle D Tadmor, Oren Litvin, Harris G Fienberg, Astraea Jager, Eli R Zunder, et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell, 162(1):184–197, 2015.

27.   Laleh Haghverdi, Maren Buettner, F Alexander Wolf, Florian Buettner, and Fabian J Theis. Diffusion pseudotime robustly reconstructs lineage branching. bioRxiv, page 041384, 2016.

28.   Manu Setty, Michelle D Tadmor, Shlomit Reich-Zeliger, Omer Angel, Tomer Meir Salame, Pooja Kathail, Kristy Choi, Sean Bendall, Nir Friedman, and Dana Pe’er. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature biotechnology, 34(6):637–645, 2016.

29.   El-ad David Amir, Kara L Davis, Michelle D Tadmor, Erin F Simonds, Jacob H Levine, Sean C Bendall, Daniel K Shenfeld, Smita Krishnaswamy, Garry P Nolan, and Dana Pe’er. visne enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature biotechnology, 31(6):545–552, 2013.

30.   Laurens Van Der Maaten. Accelerating t-sne using tree-based algorithms. Journal of machine learning research, 15(1):3221–3245, 2014.

31.   Ran Kafri, Jason Levy, Miriam B Ginzberg, Seungeun Oh, Galit Lahav, and Marc W Kirschner. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature, 494(7438):480–483, 2013.

32.   Andrea Ocone, Laleh Haghverdi, Nikola S Mueller, and Fabian J Theis. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics, 31(12):i89–i96, 2015.

33.   Tatsunori Hashimoto, David Gifford, and Tommi Jaakkola. Learning population-level diffusions with generative rnns. In Proceedings of The 33rd International Conference on Machine Learning, pages 2417–2426, 2016.

34.   Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey. Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nature biotechnology, 2015.

35.   Pablo G Camara, Daniel IS Rosenbloom, Kevin J Emmett, Arnold J Levine, and Raul Rabadan. Topological data analysis generates high-resolution, genome-wide maps of human recombination. Cell Systems, 3(1):83–94, 2016.

36.   Roy R Lederman and Ronen Talmon. Learning the geometry of common latent variables using alternating-diffusion. Applied and Computational Harmonic Analysis, 2015.

37.   Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543, 2014.

38.   Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani. Training generative neural networks via maximum mean discrepancy optimization. arXiv preprint arXiv:1505.03906, 2015.

39.   Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680, 2014.

40.   Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski. Deep generative stochastic networks trainable by backprop. In International Conference on Machine Learning, pages 226–234, 2014.

41.   David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 30(3):83–98, 2013.

Use this link to return to the main page