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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

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2000
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Papers (including subtopics)

Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures NeurIPS 2021 Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NeurIPS 2021 Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning NeurIPS 2021 Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation ICML 2021 Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning NeurIPS 2021 Bringing UMAP Closer to the Speed of Light with GPU Acceleration AAAI 2021 BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization ICLR 2021 Budget-feasible Maximum Nash Social Welfare is Almost Envy-free IJCAI 2021 Budget-feasible Mechanisms for Representing Groups of Agents Proportionally IJCAI 2021 Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation CVPR 2021 BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining NeurIPS 2021 Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification ICLR 2021 Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data ICML 2021 Byzantine-Resilient Non-Convex Stochastic Gradient Descent ICLR 2021 CADA: Communication-Adaptive Distributed Adam AISTATS 2021 calibrated adversarial training ACML 2021 Calibrate your listeners! Robust communication-based training for pragmatic speakers EMNLP 2021 Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration NeurIPS 2021 Calibration and Consistency of Adversarial Surrogate Losses NeurIPS 2021 Calibration of Neural Networks using Splines ICLR 2021 Calibration tests beyond classification ICLR 2021 CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression NeurIPS 2021 Can Selfless Learning Improve Accuracy of a Single Classification Task? WACV 2021 Can Transformer Language Models Predict Psychometric Properties? ACL 2021 Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression NeurIPS 2021