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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

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

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NeurIPS 2018 Langevin Monte Carlo and JKO splitting COLT 2018 Large Margin Deep Networks for Classification NeurIPS 2018 Large Margin Neural Language Model EMNLP 2018 Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport NeurIPS 2018 Large-Scale Cox Process Inference using Variational Fourier Features ICML 2018 Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method AISTATS 2018 Large Scale Optimal Transport and Mapping Estimation ICLR 2018 Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs NAACL 2018 Large-Scale Stochastic Sampling from the Probability Simplex NeurIPS 2018 Latent Space Oddity: on the Curvature of Deep Generative Models ICLR 2018 Lattice-free State-level Minimum Bayes Risk Training of Acoustic Models Interspeech 2018 Layer Trajectory LSTM Interspeech 2018 Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond AISTATS 2018 LDMNet: Low Dimensional Manifold Regularized Neural Networks CVPR 2018 LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration ICML 2018 Learners that Use Little Information ALT 2018 Learning a Mixture of Two Multinomial Logits ICML 2018 Learning and Testing Causal Models with Interventions NeurIPS 2018 Learning an Embedding Space for Transferable Robot Skills ICLR 2018 Learning Bayesian Networks by Branching on Constraints PGM 2018 Learning Beam Search Policies via Imitation Learning NeurIPS 2018 Learning Binary Latent Variable Models: A Tensor Eigenpair Approach ICML 2018 Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NeurIPS 2018 Learning Compact Neural Networks with Regularization ICML 2018