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)
Online sampling from log-concave distributions
NeurIPS 2019
Online Variance Reduction with Mixtures
ICML 2019
Online-Within-Online Meta-Learning
NeurIPS 2019
On Making Stochastic Classifiers Deterministic
NeurIPS 2019
On Mean Estimation for General Norms with Statistical Queries
COLT 2019
On Medians of (Randomized) Pairwise Means
ICML 2019
On Model Stability as a Function of Random Seed
CoNLL 2019
On Network Design Spaces for Visual Recognition
ICCV 2019
On NMT Search Errors and Model Errors: Cat Got Your Tongue?
EMNLP 2019
On Nonlinear Spatial Filtering in Multichannel Speech Enhancement
Interspeech 2019
On Privacy Protection of Latent Dirichlet Allocation Model Training
IJCAI 2019
On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training
ICLR 2019
On Relating Explanations and Adversarial Examples
NeurIPS 2019
On Robustness of Principal Component Regression
NeurIPS 2019
On Robustness to Adversarial Examples and Polynomial Optimization
NeurIPS 2019
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
NeurIPS 2019
On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem
AAAI 2019
On Scalable and Efficient Computation of Large Scale Optimal Transport
ICML 2019
On Single Source Robustness in Deep Fusion Models
NeurIPS 2019
On Sparse Linear Regression in the Local Differential Privacy Model
ICML 2019
On Strength Adjustment for MCTS-Based Programs
AAAI 2019
On Structure Priors for Learning Bayesian Networks
AISTATS 2019
On Succinct Encodings for the Tournament Fixing Problem
IJCAI 2019
On Symmetric Losses for Learning from Corrupted Labels
ICML 2019
On Testing for Biases in Peer Review
NeurIPS 2019