Presentations

Presentations
The posters are designed and presented by groups of two students. Each group should email the following information to the TA by Friday 8 February 4pm:

• Names and student numbers
• 5 papers in decreasing order of preference
• Preferred date of the presentation

Please note that we cannot guarantee that we can accommodate everyone's preferences. You can use Piazza to find team-mates. If you would like to do the poster presentation alone rather than in a group, please check with the TA. Please note that individual presentations will only be possible if time slots are available.

• If the presentation is given by two students, both have to contribute equally to the poster and its presentation. Both presenters will receive the same grade.
• You should prepare a poster in A0 landscape format. You can use your favourite tool to create the poster (e.g. LaTeX, LibreOffice Impress, Adobe Illustrator, Powerpoint, ...). For LaTeX, there are templates. For example posters, have a look at the NeurIPS conference.

• Research papers typically make a scientific contribution, which means that they propose or claim something that holds and that matters. The overall goal of the presentation is to convey the contribution made in the paper. For that purpose, the presentations should cover:

  1. Very briefly, what is the paper generally about?
  2. Background and/or brief recap of the relevant material from the lecture.
  3. What is proposed or claimed in the paper?
  4. What supporting evidence is provided?
  5. Why does the proposal/claim matter?

  Please send a PDF of your poster for printing to the TA by Monday 25 February 9am. We cannot print any posters that we receiver later than this, so if you miss this deadline then you would have to print the poster yourself (without reimbursement).

• Aim for a short (10 minutes) interactive presentation. Do only include as much mathematics as needed to convey the key message of the paper.
• Feel free to use diagrams and equations from the paper in your poster (with proper acknowledgement).

Please feel free to propose papers yourself. Check with the lecturer about suitability.

PCA and its extensions

• Independent Component Analysis: Algorithms and Applications
  A. Hyvarinen
  Neural Networks 2000
• Robust Principal Component Analysis
  E. Candes, X. Li, Y. Ma, and J. Wright
  Journal of ACM 2009
• Heterogeneous Component Analysis
  S. Oba, M. Kawanabe, et al
  Advances in Neural Information Processing Systems 21, 2008
• Optimal Sparse Linear Encoders and Sparse PCA
  M. Magdon-Ismail and C. Boutsidis
  Advances in Neural Information Processing Systems 29, 2016
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
  I.M. Johnstone and A.Y. Lu
  Journal of the American Statistical Association 2009
• Single Pass PCA of Matrix Products
  S. Wu, S. Bhojanapalli, et al
  Advances in Neural Information Processing Systems 29, 2016
• Provable Non-convex Robust PCA
  P. Netrapalli, U.N. Niranjan, et al
  Advances in Neural Information Processing Systems 27, 2014
• A Generalization of Principal Components Analysis to the Exponential Family
  M. Collins, S. Dasgupta, and R. E. Schapire
  Advances in Neural Information Processing Systems 15, 2002
• Semi-parametric Exponential Family PCA
  S. Sajama and A. Orlitsky
  Advances in Neural Information Processing Systems 18, 2005
• Demixed Principal Component Analysis
  W. Brendel, R. Romo, and C. K. Machens
  Advances in Neural Information Processing Systems 24, 2011
• Memory Limited, Streaming PCA
  I. Mitliagkas, C. Caramanis, and P. Jain
  Advances in Neural Information Processing Systems 26, 2013
• Iterative Supervised Principal Components
  J. Piironen and A. Vehtari
  Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (PMLR), 2018
• Random Consensus Robust PCA
  D. Pimentel-Alarcon and R. Nowak
  Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (PMLR), 2017

Dimensionality reduction and data visualisation

• Reducing the Dimensionality of Data with Neural Networks
  G. Hinton and R. Salakhutdinov
  Science 2006
• On a Connection between Kernel PCA and Metric Multidimensional Scaling
  C. Williams
  Advances in Neural Information Processing Systems 14, 2001
• Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
  M. Radovanovic et al
  Journal of Machine Learning Research 2010
• Nonlinear Dimensionality Reduction by Locally Linear Embedding (longer version)
  L. Saul and S. Roweis
  Science 2000
• Visualizing Data using t-SNE
  L. van der Maaten and G. Hinton
  Journal of Machine Learning Research 2008
• The Self-Organizing Map
  T. Kohononen
  Neurocomputing 1998
• Learning the Parts of Objects by Non-negative Matrix Factorization
  D.D. Lee and H.S. Seung
  Nature 1999
• Static and Dynamic Source Separation using Nonnegative Factorizations: A Unified View
  P. Smaragdis, C. Fevotte, et al
  IEEE Signal Processing Magazine 31, 2014
• PARAFAC. Tutorial and Applications
  R. Bro
  Chemometrics and Intelligent Laboratory Systems 1997
• Dimensionality Reduction for Data in Multiple Feature Representations
  Y.Y. Lin, T.L. Liu, and C.S. Fuh
  Advances in Neural Information Processing Systems 22, 2009
• Denoising and Dimension Reduction in Feature Space
  M.L. Braun, K.R. Müller, and J.M. Buhmann
  Advances in Neural Information Processing Systems 20, 2007
• Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
  H.F. Yu, N. Rao, and I.S. Dhillon
  Advances in Neural Information Processing Systems 29, 2016
• Dimensionality Reduction of Massive Sparse Datasets Using Coresets
  D. Feldman, M. Volkov, and D. Rus
  Advances in Neural Information Processing Systems 29, 2016
• Isomap Out-of-sample Extension for Noisy Time Series Data
  H. Dadkhahi, M. F. Duarte, and Benjamin Marlin
  IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015
• Dimensionality Reduction Using the Sparse Linear Model
  I. A. Gkioulekas and T. Zickler
  Advances in Neural Information Processing Systems 24, 2011

Performance evaluation, hyperparameter selection

• Image Quality Assessment: From Error Visibility to Structural Similarity
  Z. Wang, A. Bovik, et al
  IEEE Transactions on Image Processing 2004
• BLEU: a Method for Automatic Evaluation of Machine Translation
  K. Papineni, S. Roukos, et al
  Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) 2002
• Random Search for Hyper-Parameter Optimization
  J. Bergstra and Y. Bengio
  Journal of Machine Learning Research
• Practical Bayesian Optimization of Machine Learning Algorithms
  J. Snoek, H. Larochelle, and R. Adams
  Advances in Neural Information Processing Systems 25, 2012
• "Why Should I Trust You?" Explaining the Predictions of Any Classifier
  M.T. Ribeiro, S. Singh, and C. Guestrin
  Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
• Minimax-optimal Semi-supervised Regression on Unknown Manifolds
  A. Moscovich, A. Jaffe, and N. Boaz
  Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (PMLR), 2017

Missing data, outliers, and anomaly detection

• Isolation forest {for anomaly detection}
  Liu et al
  Eighth IEEE International Conference on Data Mining 2008
• Removing Electroencephalographic Artifacts: Comparison between ICA and PCA
  T.P. Jung et al
  Proceedings of the 1998 IEEE Signal Processing Society Workshop 1998
• Recommender Systems: Missing Data and Statistical Model Estimation
  B. Marlin et al
  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011)
• LOF: Identifying Density-Based Local Outliers
  M. Breunig et al
  Proceedings of the ACM SIGMOD International Conference on Management of Data 2000
• Support Vector Data Description
  D. Tax and R. Duin
  Machine Learning 2004
• Anomaly Detection: A Survey
  V. Chandola, A. Banerjee, and V. Kumar
  ACM Computing Surveys (CSUR), 2009
• Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection
  T. Kanamori, S. Hido, and M. Sugiyama
  Advances in Neural Information Processing Systems 22, 2009
• Generalized Outlier Detection with Flexible Kernel Density Estimates
  E. Schubert, A. Zimek, and H. Kriegel
  Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
• Fast Memory Efficient Local Outlier Detection in Data Streams
  M. Salehi, C. Leckie, J. C. Bezdek, T. Vaithianathan, and X. Zhang
  IEEE Transactions on Knowledge and Data Engineering 28, 2016
• Outlier Detection and Trend Detection: Two Sides of the Same Coin
  E. Schubert, M. Weiler, and A. Zimek
  IEEE International Conference on Data Mining Workshop (ICDMW), 2015
• In-network PCA and Anomaly Detection
  L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. Joseph, and N. Taft
  Advances in Neural Information Processing Systems 20, 2007
• Feature Set Embedding for Incomplete Data
  D. Grangier and I. Melvin
  Advances in Neural Information Processing Systems 23, 2010
• A Denoising View of Matrix Completion
  W. Wang, M. A. Carreira-Perpinan, and Z. Lu
  Advances in Neural Information Processing Systems 24, 2011

Miscellaneous

• Private traits and attributes are predictable from digital records of human behavior
  M. Kosinski et al
  Proceedings of the National Academy of Sciences 2013
• Learning Fair Representations
  R. Zemel et al
  Proceedings of the 30th International Conference on Machine Learning 2013
• Data Mining for Internet of Things: A Survey
  C.-W. Tsai, C.-F. Lai, and M.-C. Chiang
  IEEE Communications Surveys & Tutorials, 16(1) 2014
• Meme-tracking and the dynamics of the news cycle
  J. Leskovec, L. Backstrom, and J. Kleinberg
  Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining 2009
• Theory-guided Data Science: A New Paradigm for Scientific Discovery
  A. Karpatne et al
  IEEE Transactions on Knowledge and Data Engineering 29
• Matrix Factorization Techniques for Recommender Systems
  Y. Koren, R. Bell, and C. Volinsky
  Computer, 42(8), 2009
• Mining Internet-Scale Software Repositories
  E. Linstead, P. Rigor, and S. Bajracharya
  Advances in Neural Information Processing Systems 21, 2008
• Human Interaction with Recommendation Systems
  S. Schmit and C. Riquelme
  Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (PMLR), 2018