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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)

Scalable Gaussian Process Variational Autoencoders AISTATS 2021 Scalable gradient matching based on state space Gaussian Processes ACML 2021 Scalable Graph Networks for Particle Simulations AAAI 2021 Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation NeurIPS 2021 Scalable Inference of Sparsely-changing Gaussian Markov Random Fields NeurIPS 2021 Scalable Intervention Target Estimation in Linear Models NeurIPS 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021 Scalable Normalizing Flows for Permutation Invariant Densities ICML 2021 Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More ICML 2021 Scalable Thompson Sampling using Sparse Gaussian Process Models NeurIPS 2021 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition ICML 2021 Scalable Verification of Quantized Neural Networks AAAI 2021 Scalars are universal: Equivariant machine learning, structured like classical physics NeurIPS 2021 Scale-Aware Automatic Augmentation for Object Detection CVPR 2021 Scaled-YOLOv4: Scaling Cross Stage Partial Network CVPR 2021 Scaling Distributed Machine Learning with In-Network Aggregation NSDI 2021 Scaling Gaussian Processes with Derivative Information Using Variational Inference NeurIPS 2021 Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting UAI 2021 Scaling Laws for Acoustic Models Interspeech 2021 Scaling Neural Tangent Kernels via Sketching and Random Features NeurIPS 2021 Scaling Properties of Deep Residual Networks ICML 2021 Scaling Sparsemax Based Channel Selection for Speech Recognition with ad-hoc Microphone Arrays Interspeech 2021 Scaling the Convex Barrier with Active Sets ICLR 2021 Scaling Up Exact Neural Network Compression by ReLU Stability NeurIPS 2021 Scaling-Up Robust Gradient Descent Techniques AAAI 2021