{"total":24,"rows":[{"id":"2103.12828","title":"Learning to Optimize: A Primer and A Benchmark","created":"2021-03-23","cited_by_count":208,"area":null},{"id":"2209.12892","title":"Learning to Learn with Generative Models of Neural Network Checkpoints","created":"2022-09-26","cited_by_count":86,"area":null},{"id":"2211.09760","title":"VeLO: Training Versatile Learned Optimizers by Scaling Up","created":"2022-11-17","cited_by_count":57,"area":null},{"id":"2201.01212","title":"AutoBalance: Optimized Loss Functions for Imbalanced Data","created":"2022-01-04","cited_by_count":49,"area":null},{"id":"2310.07831","title":"Optimal Linear Decay Learning Rate Schedules and Further Refinements","created":"2023-10-11","cited_by_count":43,"area":null},{"id":"2205.13881","title":"Automated Dynamic Algorithm Configuration","created":"2022-05-27","cited_by_count":39,"area":null},{"id":"2203.11860","title":"Practical tradeoffs between memory, compute, and performance in learned optimizers","created":"2022-03-22","cited_by_count":32,"area":null},{"id":"2209.11208","title":"A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases","created":"2022-09-22","cited_by_count":30,"area":null},{"id":"2106.00958","title":"A Generalizable Approach to Learning Optimizers","created":"2021-06-02","cited_by_count":23,"area":null},{"id":"2503.12811","title":"A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules","created":"2025-03-17","cited_by_count":22,"area":null},{"id":"2104.04192","title":"Reinforced Attention for Few-Shot Learning and Beyond","created":"2021-04-09","cited_by_count":18,"area":null},{"id":"2201.12426","title":"A Simple Guard for Learned Optimizers","created":"2022-01-28","cited_by_count":9,"area":null},{"id":"2408.09310","title":"Narrowing the Focus: Learned Optimizers for Pretrained Models","created":"2024-08-17","cited_by_count":3,"area":null},{"id":"2511.21377","title":"Controlling changes to attention logits","created":"2025-11-26","cited_by_count":3,"area":null},{"id":"2310.14963","title":"Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens","created":"2023-10-23","cited_by_count":2,"area":null},{"id":"2110.14920","title":"Efficient Meta Subspace Optimization","created":"2021-10-28","cited_by_count":1,"area":null},{"id":"2311.18598","title":"Generalisable Agents for Neural Network Optimisation","created":"2023-11-30","cited_by_count":1,"area":null},{"id":"2510.02297","title":"Interactive Training: Feedback-Driven Neural Network Optimization","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2106.08767","title":"To Raise or Not To Raise: The Autonomous Learning Rate Question","created":"2021-06-16","cited_by_count":0,"area":null},{"id":"2305.18445","title":"Intelligent gradient amplification for deep neural networks","created":"2023-05-29","cited_by_count":0,"area":null},{"id":"2404.06679","title":"Neural Optimizer Equation, Decay Function, and Learning Rate Schedule Joint Evolution","created":"2024-04-10","cited_by_count":0,"area":null},{"id":"2509.23052","title":"Optimization as a Dynamical System: Generative Schedules from Latent ODEs","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.15473","title":"POP: Prior-Fitted First-Order Optimization Policies","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2609.34681","title":"SOLAR: A State-Driven Online Learning Rate Scheduler for LLM Pretraining","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}