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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)

Self-Normalizing Neural Networks NIPS 2017
Asynchronous Parallel Coordinate Minimization for MAP Inference NIPS 2017
Coded Distributed Computing for Inverse Problems NIPS 2017
Generalization Properties of Learning with Random Features NIPS 2017
Gaussian Quadrature for Kernel Features NIPS 2017
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees NIPS 2017
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks NIPS 2017
Acceleration and Averaging in Stochastic Descent Dynamics NIPS 2017
LightGBM: A Highly Efficient Gradient Boosting Decision Tree NIPS 2017
Straggler Mitigation in Distributed Optimization Through Data Encoding NIPS 2017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results NIPS 2017
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning NIPS 2017
Bayesian Compression for Deep Learning NIPS 2017
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
Learning Overcomplete HMMs 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
Learning with Average Top-k Loss NIPS 2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference NIPS 2017
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding NIPS 2017
Discriminative State Space Models NIPS 2017
Linearly constrained Gaussian processes NIPS 2017
A Minimax Optimal Algorithm for Crowdsourcing NIPS 2017