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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Three-Valued Semantics for Hybrid MKNF Knowledge Bases Revisited (Extended Abstract) IJCAI 2018 Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform NeurIPS 2018 Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NeurIPS 2018 Tight Regret Bounds for Bayesian Optimization in One Dimension ICML 2018 Time-evolving Text Classification with Deep Neural Networks IJCAI 2018 Time-Space Tradeoffs for Learning Finite Functions from Random Evaluations, with Applications to Polynomials COLT 2018 Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data IJCAI 2018 Token-level and sequence-level loss smoothing for RNN language models ACL 2018 Topological mixture estimation ICML 2018 Total stochastic gradient algorithms and applications in reinforcement learning NeurIPS 2018 To Tune or Not to Tune the Number of Trees in Random Forest JMLR 2018 To Understand Deep Learning We Need to Understand Kernel Learning ICML 2018 Toward Automatically Measuring Learner Ability from Human-Machine Dialog Interactions using Novel Psychometric Models NAACL 2018 Towards a Better Metric for Evaluating Question Generation Systems EMNLP 2018 Towards a Mathematical Understanding of the Difficulty in Learning With Feedforward Neural Networks CVPR 2018 Towards Binary-Valued Gates for Robust LSTM Training ICML 2018 Towards Black-box Iterative Machine Teaching ICML 2018 Towards Deep Learning Models Resistant to Adversarial Attacks ICLR 2018 Towards Effective Low-Bitwidth Convolutional Neural Networks CVPR 2018 Towards Fast Computation of Certified Robustness for ReLU Networks ICML 2018 Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization CVPR 2018 Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies COLING 2018 Towards Memory-Friendly Deterministic Incremental Gradient Method AISTATS 2018 Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication ICML 2018 Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation AISTATS 2018