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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
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD NeurIPS 2021 Deterministic Finite-Memory Bias Estimation COLT 2021 Deterministic Mini-batch Sequencing for Training Deep Neural Networks AAAI 2021 Development of a Psychoacoustic Loss Function for the Deep Neural Network (DNN)-Based Speech Coder Interspeech 2021 Device-Based LTE Latency Reduction at the Application Layer NSDI 2021 DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts ACL 2021 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning ICML 2021 DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs ICML 2021 Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI EMNLP 2021 Diagnostics for conditional density models and Bayesian inference algorithms UAI 2021 Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain AISTATS 2021 DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities ICCV 2021 DiBS: Differentiable Bayesian Structure Learning NeurIPS 2021 DIBS: Diversity Inducing Information Bottleneck in Model Ensembles AAAI 2021 Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge ACL 2021 Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NeurIPS 2021 Differentiable Causal Discovery Under Unmeasured Confounding AISTATS 2021 Differentiable Diffusion for Dense Depth Estimation From Multi-View Images CVPR 2021 Differentiable Divergences Between Time Series AISTATS 2021 Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution ICML 2021 Differentiable Dynamic Wirings for Neural Networks ICCV 2021 Differentiable Fluids with Solid Coupling for Learning and Control AAAI 2021 Differentiable Greedy Algorithm for Monotone Submodular Maximization: Guarantees, Gradient Estimators, and Applications AISTATS 2021 Differentiable Learning Under Triage NeurIPS 2021 Differentiable Multiple Shooting Layers NeurIPS 2021