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
14
29
44
31
52
189
195
212
235
352
382
457
787
816
828
1,066
1,391
1,913
3,012
3,646
4,303
4,450
5,357
6,155
5,854
1,521
2000
'5
'10
'15
'20
'25
Papers (including subtopics)
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
JMLR 2020
On the consistency of top-k surrogate losses
ICML 2020
On the Convergence of FedAvg on Non-IID Data
ICLR 2020
On the Convergence of Model Free Learning in Mean Field Games
AAAI 2020
On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings
ICML 2020
On the Convergence of SARAH and Beyond
AISTATS 2020
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
NeurIPS 2020
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems
COLT 2020
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
AISTATS 2020
On the Decidability of Intuitionistic Tense Logic without Disjunction
IJCAI 2020
On the Discrepancy between Density Estimation and Sequence Generation
EMNLP 2020
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
AAAI 2020
On the distance between two neural networks and the stability of learning
NeurIPS 2020
On the Equivalence between Online and Private Learnability beyond Binary Classification
NeurIPS 2020
On the Equivalence between Positional Node Embeddings and Structural Graph Representations
ICLR 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
NeurIPS 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
NeurIPS 2020
On the Generalization Benefit of Noise in Stochastic Gradient Descent
ICML 2020
On the Global Convergence of Training Deep Linear ResNets
ICLR 2020
On the Global Convergence Rates of Softmax Policy Gradient Methods
ICML 2020
On the Global Optimality of Model-Agnostic Meta-Learning
ICML 2020
On the Interplay Between Fine-tuning and Sentence-Level Probing for Linguistic Knowledge in Pre-Trained Transformers
EMNLP 2020
On the interplay between noise and curvature and its effect on optimization and generalization
AISTATS 2020
On the Iteration Complexity of Hypergradient Computation
ICML 2020
On the Learnability of Possibilistic Theories
IJCAI 2020