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
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'10
'15
'20
'25
Papers (including subtopics)
On the Impact of the Activation function on Deep Neural Networks Training
ICML 2019
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
EMNLP 2019
On the Inducibility of Stackelberg Equilibrium for Security Games
AAAI 2019
On the Inductive Bias of Neural Tangent Kernels
NeurIPS 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
NeurIPS 2019
On the Integration of CP-nets in ASPRIN
IJCAI 2019
On the Interaction Effects Between Prediction and Clustering
AISTATS 2019
On the Limitations of Representing Functions on Sets
ICML 2019
On the Limits of Learning to Actively Learn Semantic Representations
CoNLL 2019
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
ICML 2019
On the loss landscape of a class of deep neural networks with no bad local valleys
ICLR 2019
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
ICLR 2019
On the number of variables to use in principal component regression
NeurIPS 2019
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems
NeurIPS 2019
On the optimality of the Hedge algorithm in the stochastic regime
JMLR 2019
On Theory for BART
AISTATS 2019
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
AAAI 2019
On the Power and Limitations of Random Features for Understanding Neural Networks
NeurIPS 2019
On The Power of Curriculum Learning in Training Deep Networks
ICML 2019
On the Problem of Assigning PhD Grants
IJCAI 2019
On the Realization of Compositionality in Neural Networks
ACL 2019
On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA
COLT 2019
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
ICLR 2019
On the Relationship Between Satisfiability and Markov Decision Processes
UAI 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions
ICLR 2019