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

Distribution-Independent PAC Learning of Halfspaces with Massart Noise NeurIPS 2019 Distribution-Interpolation Trade off in Generative Models ICLR 2019 Divergence Prior and Vessel-Tree Reconstruction CVPR 2019 Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model CVPR 2019 Diverse Image Synthesis From Semantic Layouts via Conditional IMLE ICCV 2019 Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation NeurIPS 2019 Does Data Augmentation Lead to Positive Margin? ICML 2019 Does data interpolation contradict statistical optimality? AISTATS 2019 Does My Rebuttal Matter? Insights from a Major NLP Conference NAACL 2019 Do NLP Models Know Numbers? Probing Numeracy in Embeddings EMNLP 2019 Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse NeurIPS 2019 Don't take it lightly: Phasing optical random projections with unknown operators NeurIPS 2019 DoubleLex Revisited and Beyond IJCAI 2019 Double Quantization for Communication-Efficient Distributed Optimization NeurIPS 2019 DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression ICML 2019 Doubly-Competitive Distribution Estimation ICML 2019 Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives ICLR 2019 Doubly-Robust Lasso Bandit NeurIPS 2019 Doubly Semi-Implicit Variational Inference AISTATS 2019 Douglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution CVPR 2019 DPPy: DPP Sampling with Python JMLR 2019 DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs NAACL 2019 Dropout as a Structured Shrinkage Prior ICML 2019 DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization JMLR 2019 DTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification NAACL 2019