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)
Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates
AISTATS 2017
Online Learning for Multivariate Hawkes Processes
NeurIPS 2017
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
NeurIPS 2017
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm
EACL 2017
Online Learning to Rank in Stochastic Click Models
ICML 2017
Online Learning to Rank with Top-k Feedback
JMLR 2017
Online Learning with a Hint
NeurIPS 2017
Online Learning with Local Permutations and Delayed Feedback
ICML 2017
Online Learning Without Prior Information
COLT 2017
Online Learning with Transductive Regret
NeurIPS 2017
Online multiclass boosting
NeurIPS 2017
Online Nonnegative Matrix Factorization with General Divergences
AISTATS 2017
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
ICML 2017
Online Prediction with Selfish Experts
NeurIPS 2017
Online Reputation Fraud Campaign Detection in User Ratings
IJCAI 2017
Online Robust Low-Rank Tensor Learning
IJCAI 2017
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
NeurIPS 2017
On Markov chain Monte Carlo methods for tall data
JMLR 2017
On Optimal Generalizability in Parametric Learning
NeurIPS 2017
On orthogonality and learning recurrent networks with long term dependencies
ICML 2017
On Perturbed Proximal Gradient Algorithms
JMLR 2017
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
NeurIPS 2017
On Querying Incomplete Information in Databases under Bag Semantics
IJCAI 2017
On Relaxing Determinism in Arithmetic Circuits
ICML 2017
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
NeurIPS 2017