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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)

Byzantine-Tolerant Methods for Distributed Variational Inequalities NeurIPS 2023 Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs NeurIPS 2023 Calibrating a Deep Neural Network with Its Predecessors IJCAI 2023 Calibrating “Cheap Signals” in Peer Review without a Prior NeurIPS 2023 Calibrating Deep Neural Networks Using Explicit Regularisation and Dynamic Data Pruning WACV 2023 Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability NeurIPS 2023 Calibrating the Rigged Lottery: Making All Tickets Reliable ICLR 2023 Calibrating Transformers via Sparse Gaussian Processes ICLR 2023 Calibrating Uncertainty for Semi-Supervised Crowd Counting ICCV 2023 Calibration by Distribution Matching: Trainable Kernel Calibration Metrics NeurIPS 2023 Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems ICLR 2023 Calibration techniques for node classification using graph neural networks on medical image data MIDL 2023 CAME: Confidence-guided Adaptive Memory Efficient Optimization ACL 2023 CAME: Contrastive Automated Model Evaluation ICCV 2023 Can 5th Generation Local Training Methods Support Client Sampling? Yes! AISTATS 2023 Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! ACL 2023 Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC 2023 Can Forward Gradient Match Backpropagation? ICML 2023 Can Graph Neural Networks Learn to Solve the MaxSAT Problem? (Student Abstract) AAAI 2023 Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning? ACML 2023 Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics CoNLL 2023 Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning L4DC 2023 Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization EMNLP 2023 Can Neural Network Memorization Be Localized? ICML 2023 Can Pretrained Language Models (Yet) Reason Deductively? EACL 2023