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Optimization

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Papers

A Progressive Batching L-BFGS Method for Machine Learning ICML 2018
Prediction Rule Reshaping ICML 2018
Improved large-scale graph learning through ridge spectral sparsification ICML 2018
Conditional Noise-Contrastive Estimation of Unnormalised Models ICML 2018
Stability and Generalization of Learning Algorithms that Converge to Global Optima ICML 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? ICML 2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity ICML 2018
Scalable Bilinear Pi Learning Using State and Action Features ICML 2018
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients ICML 2018
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization ICML 2018
Stochastic Training of Graph Convolutional Networks with Variance Reduction ICML 2018
Path Consistency Learning in Tsallis Entropy Regularized MDPs ICML 2018
On Acceleration with Noise-Corrupted Gradients ICML 2018
Online Linear Quadratic Control ICML 2018
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation ICML 2018
Escaping Saddles with Stochastic Gradients ICML 2018
Modeling Sparse Deviations for Compressed Sensing using Generative Models ICML 2018
Alternating Randomized Block Coordinate Descent ICML 2018
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms ICML 2018
Noisin: Unbiased Regularization for Recurrent Neural Networks ICML 2018
Randomized Block Cubic Newton Method ICML 2018
Essentially No Barriers in Neural Network Energy Landscape ICML 2018
On the Power of Over-parametrization in Neural Networks with Quadratic Activation ICML 2018