Slides/Presentations/Lectures
Node Embedding with Adaptive Similarities
- SVD-based unsupervised node embeddings based on self-tuned multi-scale similarities.
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Learning over graphs (with Adaptive Diffusions)
- Semi-supervised classification of graph nodes, using graph- and class-adaptive
truncated random walks. Tests on real multicalss and multilabel graphs.
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Active Learning
- Fundamental AL principles and algorithms based on Burr Settles' tutorial.
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- Development of AL framework for graph-based active node classification.
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Graphical models
- Introduction to inference with discrete and continuous undirected graphical models (MRFs).
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Markov Chain Monte Carlo (MCMC) and Importance Sampling
- Basic principles and algorithms for Monte Carlo-based Bayesian inference.
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