Skip to content
conftrace_

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
'5
'10
'15
'20
'25

Papers (including subtopics)

On Testing of Uniform Samplers AAAI 2019 On the Calibration of Multiclass Classification with Rejection NeurIPS 2019 On The Classification-Distortion-Perception Tradeoff NeurIPS 2019 On the Computational Power of Online Gradient Descent COLT 2019 On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization ICML 2019 On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition AISTATS 2019 On the Convergence and Robustness of Adversarial Training ICML 2019 On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 On the convergence of single-call stochastic extra-gradient methods NeurIPS 2019 On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019 On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization IJCAI 2019 On the Convergence Rate of Training Recurrent Neural Networks NeurIPS 2019 On the Correctness and Sample Complexity of Inverse Reinforcement Learning NeurIPS 2019 On the Curved Geometry of Accelerated Optimization NeurIPS 2019 On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method ICCV 2019 On the Distortion Value of the Elections with Abstention AAAI 2019 On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study ACL 2019 On the Dynamics of Gradient Descent for Autoencoders AISTATS 2019 On the equivalence between graph isomorphism testing and function approximation with GNNs NeurIPS 2019 On the Expressive Power of Deep Polynomial Neural Networks NeurIPS 2019 On the Generalization Gap in Reparameterizable Reinforcement Learning ICML 2019 On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NeurIPS 2019 On the Global Optima of Kernelized Adversarial Representation Learning ICCV 2019 On the Hardness of Probabilistic Inference Relaxations AAAI 2019 On the Hardness of Robust Classification NeurIPS 2019