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 Intrinsic Differential Privacy of Bagging
IJCAI 2021
On the Learnability of Knowledge in Multi-Agent Logics
IJCAI 2021
On the Limits of Minimal Pairs in Contrastive Evaluation
EMNLP 2021
On the Limits of Pseudo Ground Truth in Visual Camera Re-Localisation
ICCV 2021
On the Linear Convergence of Policy Gradient Methods for Finite MDPs
AISTATS 2021
On the mapping between Hopfield networks and Restricted Boltzmann Machines
ICLR 2021
On the Memory Mechanism of Tensor-Power Recurrent Models
AISTATS 2021
On the Minimal Error of Empirical Risk Minimization
COLT 2021
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
AISTATS 2021
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
IJCAI 2021
On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity
AISTATS 2021
On the Optimal Efficiency of A* with Dominance Pruning
AAAI 2021
On the Optimality of Batch Policy Optimization Algorithms
ICML 2021
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
JMLR 2021
On the Origin of Implicit Regularization in Stochastic Gradient Descent
ICLR 2021
On the Out-of-distribution Generalization of Probabilistic Image Modelling
NeurIPS 2021
On the Parameterized Complexity of Polytree Learning
IJCAI 2021
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
NeurIPS 2021
On the Power of Differentiable Learning versus PAC and SQ Learning
NeurIPS 2021
On the Power of Edge Independent Graph Models
NeurIPS 2021
On the Predictability of Pruning Across Scales
ICML 2021
On the Privacy Properties of GAN-generated Samples
AISTATS 2021
On the Problem of Underranking in Group-Fair Ranking
ICML 2021
On the proliferation of support vectors in high dimensions
AISTATS 2021
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
ICML 2021