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Optimization & Theory

36,874 papers (3 classified directly here)

Subtopics

Optimization (14,983) Bayesian Inference (5,566) Learning Theory (5,334) Theory (4,955) Statistical Learning (4,117) Neural Network Optimization (3,649) Stochastic Processes (2,667) Loss Functions (1,162) Distributed Learning (1,135) Stochastic Methods (1,118) Online Algorithms (806) Statistics (684) Information Theory (318) Generalization (220) Sparse Optimization (158) Sample Complexity (99) Game Theory (30) Robust Optimization (17) Optimal Transport (14) Approximation Algorithms (10) Distributed Optimization (8) Convex Optimization (8)

Papers per year

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Papers (including subtopics)

Tractability in Structured Probability Spaces NIPS 2017
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search NIPS 2017
Multi-Information Source Optimization NIPS 2017
Differentially private Bayesian learning on distributed data NIPS 2017
MMD GAN: Towards Deeper Understanding of Moment Matching Network NIPS 2017
Convergence of Gradient EM on Multi-component Mixture of Gaussians NIPS 2017
Efficient and Flexible Inference for Stochastic Systems NIPS 2017
Learning ReLUs via Gradient Descent NIPS 2017
Collapsed variational Bayes for Markov jump processes NIPS 2017
Is the Bellman residual a bad proxy? NIPS 2017
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems NIPS 2017
Robust Estimation of Neural Signals in Calcium Imaging NIPS 2017
Stochastic Mirror Descent in Variationally Coherent Optimization Problems NIPS 2017
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication NIPS 2017
From Bayesian Sparsity to Gated Recurrent Nets NIPS 2017
Consistent Robust Regression NIPS 2017
Scalable Variational Inference for Dynamical Systems NIPS 2017
Hierarchical Clustering Beyond the Worst-Case NIPS 2017
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NIPS 2017
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization NIPS 2017
A Learning Error Analysis for Structured Prediction with Approximate Inference NIPS 2017
Simple strategies for recovering inner products from coarsely quantized random projections NIPS 2017
Trimmed Density Ratio Estimation NIPS 2017
Adaptive Batch Size for Safe Policy Gradients NIPS 2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NIPS 2017