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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)

LCA: Loss Change Allocation for Neural Network Training NeurIPS 2019 L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise NeurIPS 2019 Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NeurIPS 2019 Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization IJCAI 2019 Learnability for the Information Bottleneck UAI 2019 Learnability of Solutions to Conjunctive Queries JMLR 2019 Learnable Embedding Space for Efficient Neural Architecture Compression ICLR 2019 Learning Adaptive Random Features AAAI 2019 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers NeurIPS 2019 Learning and Inference for Structured Prediction: A Unifying Perspective IJCAI 2019 Learning and the Unknown: Surveying Steps toward Open World Recognition AAAI 2019 Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing AAAI 2019 Learning a SAT Solver from Single-Bit Supervision ICLR 2019 Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models JMLR 2019 Learning-Based Low-Rank Approximations NeurIPS 2019 Learning beamforming in ultrasound imaging MIDL 2019 Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms AISTATS 2019 Learning deep kernels for exponential family densities ICML 2019 Learning Deep Priors for Image Dehazing ICCV 2019 Learning Deep Stochastic Optimal Control Policies Using Forward-Backward SDEs RSS 2019 Learning Deep Transformer Models for Machine Translation ACL 2019 Learning Dependency Structures for Weak Supervision Models ICML 2019 Learning Deterministic Weighted Automata with Queries and Counterexamples NeurIPS 2019 Learning Distributions Generated by One-Layer ReLU Networks NeurIPS 2019 Learning Diverse Bayesian Networks AAAI 2019