Reading List: ICML Papers 2016
• PaperList
To make a habit of reading more papers, I have decided to write comments, may be some quick or sometime detail, on the papers which I find interesting and related to my research area. Hopefully, this will come handy in solving my own research problems.
Starting with ICML 2016 Papers:
- Revisiting Semi-Supervised Learning with Graph Embeddings ICML 2016
- Why Regularized Auto-Encoders learn Sparse Representation? ICML 2016
- On the Consistency of Feature Selection With Lasso for Non-linear Targets. ICML 2016
- Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML 2016
- The Variational Nystrom method for large-scale spectral problems. ICML 2016
- A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation. ICML 2016 (Possibly a new way to measure difference in probability distribution other than most common – KL divergence )
- Low-Rank Matrix Approximation with Stability. ICML 2016
- Unsupervised Deep Embedding for Clustering Analysis. ICML 2016
- Online Low-Rank Subspace Clustering by Explicit Basis Modeling. ICML 2016
- Community Recovery in Graphs with Locality. ICML 2016
- Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. ICML 2016
- Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. ICML 2016
- Compressive Spectral Clustering ICML. 2016
- Variance-Reduced and Projection-Free Stochastic Optimization. ICML 2016
- Learning Convolutional Neural Networks for Graphs. ICML 2016
- Discrete Deep Feature Extraction: A Theory and New Architectures. ICML 2016