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 Expressive Flexibility of Self-Attention Matrices
AAAI 2023
On the Expressive Power of Geometric Graph Neural Networks
ICML 2023
On the Generalization Ability of Retrieval-Enhanced Transformers
EACL 2023
On the Generalization Error of Stochastic Mirror Descent for Quadratically-Bounded Losses: an Improved Analysis
NeurIPS 2023
On the Generalization Properties of Diffusion Models
NeurIPS 2023
On the geometry of Stein variational gradient descent
JMLR 2023
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
ICML 2023
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures
ICML 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models
ICML 2023
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
NeurIPS 2023
On the Implicit Bias of Linear Equivariant Steerable Networks
NeurIPS 2023
On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data
AISTATS 2023
On the Importance of Denoising When Learning To Compress Images
WACV 2023
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
NeurIPS 2023
On the inconsistency of separable losses for structured prediction
EACL 2023
On the Initialization of Graph Neural Networks
ICML 2023
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
ICML 2023
On the Intersection of Context-Free and Regular Languages
EACL 2023
On the Interventional Kullback-Leibler Divergence
CLeaR 2023
On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails
NeurIPS 2023
On the Learnability of Multilabel Ranking
NeurIPS 2023
On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks
UAI 2023
On the Limitations of Simulating Active Learning
ACL 2023
On the Lower Bound of Minimizing Polyak-Łojasiewicz functions
COLT 2023
On the Minimax Regret for Online Learning with Feedback Graphs
NeurIPS 2023