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Optimization

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Papers

Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? NIPS 2017
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks NIPS 2017
Convolutional Phase Retrieval NIPS 2017
Stochastic and Adversarial Online Learning without Hyperparameters NIPS 2017
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding NIPS 2017
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization NIPS 2017
Learning with Feature Evolvable Streams NIPS 2017
Online Dynamic Programming NIPS 2017
Training Deep Networks without Learning Rates Through Coin Betting NIPS 2017
Gradient descent GAN optimization is locally stable NIPS 2017
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers NIPS 2017
Non-convex Finite-Sum Optimization Via SCSG Methods NIPS 2017
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization NIPS 2017
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure NIPS 2017
Reinforcement Learning under Model Mismatch NIPS 2017
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem NIPS 2017
Learned D-AMP: Principled Neural Network based Compressive Image Recovery NIPS 2017
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations NIPS 2017
Revenue Optimization with Approximate Bid Predictions NIPS 2017
Improved Training of Wasserstein GANs NIPS 2017
Adaptive stimulus selection for optimizing neural population responses NIPS 2017
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls NIPS 2017
Ranking Data with Continuous Labels through Oriented Recursive Partitions NIPS 2017
Scalable Log Determinants for Gaussian Process Kernel Learning NIPS 2017
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains NIPS 2017