ECE Illinois 543: Statistical Learning Theory Spring 2017 Project Papers

• Charbel Sakr Learning in Discrete Spaces with Discrete Neural Networks
• Colin Graber An Overview of Structured Prediction Theory
• Du Su Regret minimization fo News Dissemination
• Furen Zhuang Bounds on Agnostic Online Learning
• Haohua Wan Gradient Descent Boosting: Convergence and Algorithm
• Hussein Sibai Verification of Neural Nets: Towards Deterministic Guarantees
• Jianhao Peng Binary Classification Approach to Ordinal Regression
• Jin Kim Inductive Bias and Performance Bounds for the Case of Multi-Task Learning
• Mayank Baranwal Weighted-Kernal Deterministic Annealing Algorithm to Shape Clustering
• Menglong Li A Simple Practical Accelerated Method for Finite Sums: Point-SAGA
• Michael Livesay Chaining Method to Improve Rademacher Bound
• Puoya Tabaghi Fat-Shattering and Learnability of Real-Valued Functions
• Ravi Kiran Raman Principle of Maximum Conditional Entropy
• Shu Chen Noise Reduction and Acceleration of Stochastic Gradient Descent for Least Squares Regression Problem
• Soham Dan PAC Bounds
• Surya Sankagiri Gradient Estimation for High-Dimensional Machine Learning
• Toros Arikan Improving the ERM Algorithms Through Density Ratio Estimation
• Vaishnavi Subramanian Ensemble methods and random forests
• Wenxuan Zhou Analysis of Pegasos Algorithm
• Zheng Liu Stability of Clustering Algorithms Under the ERM Scheme with Infinite Large Dataset
• Zhili Feng Methods of Moments for Recovering Spectrum from Samples