Machine Learning

1. Computation Method

1. A scalable bootstrap for massive data

2. Crowdsourcing

1. Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks
2. Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing
3. Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
4. Repeated labeling using multiple noisy labelers
5. Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers

3. Dimension Reduction

1. Visualizing Data using t-SNE)
2. Umap: Uniform manifold approximation and projection for dimension reduction

4. Learning to Rank

1. A Short Introduction to Learning to Rank
2. DeepRank: A New Architecture for Relevance Ranking in Information Retrieval

5. Privacy

1. Formal Privacy for Functional Data with Gaussian Perturbations
2. Differential Privacy for Functions and Functional Data
3. White-box vs Black-box- Bayes Optimal Strategies for Membership Inference
4. Privacy Risk in Machine Learning- Analyzing the Connection to Overfitting
5. Sublinear Space Private Algorithms Under the Sliding Window Model

6. Subspace Clustering

1. Mixtures of Probabilistic Principal Component Analysers
2. Robust Recovery of Subspace Structures by Low-Rank Representation
3. A tutorial on subspace clustering

7. Test of Goodness of Fit

1. A Kernelized Stein Discrepancy for Goodness-of-fit Tests
2. A Linear-Time Kernel Goodness-of-Fit Test

8. Traditional Learning Theory

1. Boosting the Margin_A New Explanation for the Effectiveness of Voting Methods
2. The Weighted Majority Algorithm
3. The Strength of Weak Learnability
4. The Multiplicative Weights Update Method- A Meta-Algorithm and Applications
5. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples