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Optimization

1638 directly classified papers

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Papers

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NIPS 2018
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms NIPS 2018
Provably Correct Automatic Sub-Differentiation for Qualified Programs NIPS 2018
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NIPS 2018
A Bridging Framework for Model Optimization and Deep Propagation NIPS 2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training NIPS 2018
The Price of Privacy for Low-rank Factorization NIPS 2018
The Convergence of Sparsified Gradient Methods NIPS 2018
Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis NIPS 2018
Neural Proximal Gradient Descent for Compressive Imaging NIPS 2018
Adaptive Methods for Nonconvex Optimization NIPS 2018
Training Deep Models Faster with Robust, Approximate Importance Sampling NIPS 2018
L4: Practical loss-based stepsize adaptation for deep learning NIPS 2018
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD NIPS 2018
Norm matters: efficient and accurate normalization schemes in deep networks NIPS 2018
Byzantine Stochastic Gradient Descent NIPS 2018
Variance-Reduced Stochastic Gradient Descent on Streaming Data NIPS 2018
NEON2: Finding Local Minima via First-Order Oracles NIPS 2018
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization NIPS 2018
On the Local Minima of the Empirical Risk NIPS 2018
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization NIPS 2018
Stochastic Cubic Regularization for Fast Nonconvex Optimization NIPS 2018
Approximating Real-Time Recurrent Learning with Random Kronecker Factors NIPS 2018
A Greedy Approach for Budgeted Maximum Inner Product Search NIPS 2017
Population Matching Discrepancy and Applications in Deep Learning NIPS 2017