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 Robustness of Neural Semantic Parsers
EACL 2021
On Robust Optimal Transport: Computational Complexity and Barycenter Computation
NeurIPS 2021
On Sampled Metrics for Item Recommendation (Extended Abstract)
IJCAI 2021
On Sampling-Based Training Criteria for Neural Language Modeling
Interspeech 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
ICML 2021
On Smoother Attributions using Neural Stochastic Differential Equations
IJCAI 2021
On Statistical Bias In Active Learning: How and When to Fix It
ICLR 2021
On the Absence of Spurious Local Minima in Nonlinear Low-Rank Matrix Recovery Problems
AISTATS 2021
On the Adequacy of Untuned Warmup for Adaptive Optimization
AAAI 2021
On the Algorithmic Stability of Adversarial Training
NeurIPS 2021
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
NeurIPS 2021
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search
EACL 2021
On the Complexity of Finding Justifications for Collective Decisions
AAAI 2021
On the Consistency of Metric and Non-Metric K-Medoids
AISTATS 2021
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
NeurIPS 2021
On the Convergence of Communication-Efficient Local SGD for Federated Learning
AAAI 2021
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
ICML 2021
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness
COLT 2021
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
NeurIPS 2021
On the Convergence of Step Decay Step-Size for Stochastic Optimization
NeurIPS 2021
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method
IJCAI 2021
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions
AISTATS 2021
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
NeurIPS 2021
On the Cryptographic Hardness of Learning Single Periodic Neurons
NeurIPS 2021
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
ICLR 2021