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 Expressivity of Recurrent Neural Cascades
AAAI 2024
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
ICLR 2024
On the Faithfulness of Vision Transformer Explanations
CVPR 2024
On-the-fly Denoising for Data Augmentation in Natural Language Understanding
EACL 2024
On the Foundations of Shortcut Learning
ICLR 2024
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
ICLR 2024
On the generalization capacity of neural networks during generic multimodal reasoning
ICLR 2024
On the Generalization of Equivariant Graph Neural Networks
ICML 2024
On the Generalization of Stochastic Gradient Descent with Momentum
JMLR 2024
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
COLT 2024
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
ICLR 2024
On the hardness of learning under symmetries
ICLR 2024
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
ICLR 2024
On the Hardness of Probabilistic Neurosymbolic Learning
ICML 2024
On the Hyperparameters in Stochastic Gradient Descent with Momentum
JMLR 2024
On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
NeurIPS 2024
On the Identifiability of Quantized Factors
CLeaR 2024
On the Impact of Calibration Data in Post-training Quantization and Pruning
ACL 2024
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
JMLR 2024
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions
AISTATS 2024
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory
NeurIPS 2024
On the Implicit Bias of Adam
ICML 2024
On the In-context Generation of Language Models
EMNLP 2024
On the Inductive Bias of Stacking Towards Improving Reasoning
NeurIPS 2024
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
AISTATS 2024