• Week 1 (Jan. 23):
  • NMF lecture (Hongchang Gao)
• Week 2 (Jan. 30):
  • SVM Optimization (Xiaoqian Wang)
• Week 3 (Feb. 6):
  • No class. Instructor will attend AAAI conference
• Week 4 (Feb. 13):
  • Deep Learning (De Wang)
• Week 5 (Feb. 20):
  • Deep Learning (Kamran Ghasedi)
• Week 6 (Feb. 27):
  • Michail Theofanidis, Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
  • Michail Theofanidis, Deep networks for motor control functions
• Week 7 (Mar. 6):
  • Ashwin Ramesh Babu, Sketch Tokens: A learned Mid-level Representation or Contour and Object Detection
  • Zhifei Deng, Scaling Distributed Machine Learning with the Parameter Server
• Week 8 (Mar. 13):
  • Spring Break
• Week 9 (Mar. 20):
  • Jianjin Deng, Efficient Mini-batch Training for Stochastic Optimization
  • Qicheng Wang, Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
• Week 10 (Mar. 27):
  • Ashwin RameshBabu
  • Xin Miao, Generative Adversarial Nets
  • Lin Yan, Two-Manifold Problems with Applications to Nonlinear System Identification
• Week 11 (Apr. 3):
  • Sanika Gupta, Online Instrumental Variable Regression with Applications to Online Linear System Identification
  • Zhifei Deng, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  • Jianjin Deng, Efficient Mini-batch Training for Stochastic Optimization
• Week 12 (Apr. 10):
  • Sanika Gupta, Deep Visual Analogy-Making
  • Xin Miao, Fast and Robust Parallel SGD Matrix Factorization
  • Lin Yan, Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery
• Week 13 (Apr. 17):
  • Jianjin Deng
  • Qicheng Wang, Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
  • Neelabh Pant, Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data
• Week 14 (Apr. 24):
  • Rodrigo Linhares, Action-Conditional Video Prediction using Deep Networks in Atari Games
  • Rodrigo Linhares, Multiview Triplet Embedding: Learning Attributes in Multiple Maps
  • Neelabh Pant, Traffic Flow Prediction With Big Data: A Deep Learning Approach
• Week 15 (May 1):
  • Shirin Shirvani, GraphChi: Large-Scale Graph computation on Just a PC
  • Shirin Shirvani, Collaborative Deep Learning for Recommender Systems
  • Qicheng Wang, Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

Paper List

• Computer Vision Applications
  • Sparse Subspace Clustering with Missing Entries
    
  • Deep Visual Analogy-Making (Sanika Gupta)
    
  • Action-Conditional Video Prediction using Deep Networks in Atari Games (Rodrigo Linhares)
    
  • Latent Semantic Representation Learning for Scene Classification
    
• Clustering
  • Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization
    
  • Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup
    
  • Parallel Correlation Clustering on Big Graphs
    
  • Fast Distributed k-Center Clustering with Outliers on Massive Data
    
  • Clustering and Projected Clustering with Adaptive Neighbors
    
  • Differentially Private Subspace Clustering
    
• Feature Selection
  • Multiview Triplet Embedding: Learning Attributes in Multiple Maps (Rodrigo Linhares)
    
  • Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction
    
• Supervised Learning
  • Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery(Lin Yan)
    
  • Optimal Action Extraction for Random Forests and Boosted Trees
    
  • Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings
    
  • Generative Adversarial Nets (Xin Miao)
    
• System
  • Scaling Distributed Machine Learning with the Parameter Server (Zhifei Deng)
    
  • GraphChi: Large-Scale Graph computation on Just a PC (Shirin Shirvani)
    
  • PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
    
• Stochastic Optimization
  • Efficient Mini-batch Training for Stochastic Optimization (Jianjin Deng)
    
  • Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
    
  • Scaling Up Stochastic Dual Coordinate Ascent
    
• SVM Optimization
  • Pegasos: Primal Estimated sub-GrAdient SOlver for SVM (Qicheng Wang)
    
  • Trust Region Newton Method for Large-Scale Logistic Regression
    
  • Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
    
• Artificial Intelligence
  • Online Instrumental Variable Regression with Applications to Online Linear System Identification(Sanika Gupta)
    
  • A Spectral Learning Approach to Range-Only SLAM
    
  • Two-Manifold Problems with Applications to Nonlinear System Identification (Lin Yan)
    
• Recommender Systems
  • Matrix Completion with Queries
    
  • Fast and Robust Parallel SGD Matrix Factorization (Xin Miao)
    
  • Collaborative Deep Learning for Recommender Systems (Shirin Shirvani)
    

• Distributed Optimization
  • PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
    
  • Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM
    
  • Adding vs. Averaging in Distributed Primal-Dual Optimization
    
• Distributed Learning
  • Communication Efficient Distributed Machine Learning with the Parameter Server
    
• Stochastic Algorithm
  • Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (Qicheng Wang)
    
  • Stochastic Dual Coordinate Ascent with Adaptive Probabilities (Zhifei Deng)
    
  • Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
    
  • Variance Reduced Stochastic Gradient Descent with Neighbors
    
  • Beyond Convexity: Stochastic Quasi-Convex Optimization
    
• Classification:
  • Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
    
  • Sparse Local Embeddings for Extreme Multi-label Classification
    
  • On the Optimality of Classifier Chain for Multi-label Classification
    
  • The Coherent Loss Function for Classification
    
• Feature Learning:
  • Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
    
  • Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
    
  • On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection
    
• Submodular
  • Structured sparsity-inducing norms through submodular functions
    
  • Parallel Double Greedy Submodular Maximization