{"total":91,"rows":[{"id":"2302.06675","title":"Symbolic Discovery of Optimization Algorithms","created":"2023-02-13","cited_by_count":434,"area":null},{"id":"2106.13281","title":"Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation","created":"2021-06-24","cited_by_count":407,"area":null},{"id":"2103.12828","title":"Learning to Optimize: A Primer and A Benchmark","created":"2021-03-23","cited_by_count":208,"area":null},{"id":"2206.00719","title":"Dataset Distillation using Neural Feature Regression","created":"2022-06-01","cited_by_count":177,"area":null},{"id":"2212.04458","title":"General-Purpose In-Context Learning by Meta-Learning Transformers","created":"2022-12-08","cited_by_count":105,"area":null},{"id":"2202.00817","title":"Do Differentiable Simulators Give Better Policy Gradients?","created":"2022-02-02","cited_by_count":89,"area":null},{"id":"2301.04272","title":"Data Distillation: A Survey","created":"2023-01-11","cited_by_count":87,"area":null},{"id":"2111.05803","title":"Gradients are Not All You Need","created":"2021-11-10","cited_by_count":86,"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":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2602.23413","title":"EvoX: Meta-Evolution for Automated Discovery","created":"2026-02-26","cited_by_count":60,"area":null},{"id":"2403.12143","title":"Graph Neural Networks for Learning Equivariant Representations of Neural Networks","created":"2024-03-18","cited_by_count":59,"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":"2112.13835","title":"Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies","created":"2021-12-27","cited_by_count":52,"area":null},{"id":"2109.04504","title":"Bootstrapped Meta-Learning","created":"2021-09-09","cited_by_count":45,"area":null},{"id":"2211.11260","title":"Discovering Evolution Strategies via Meta-Black-Box Optimization","created":"2022-11-21","cited_by_count":44,"area":null},{"id":"2302.01735","title":"Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective","created":"2023-02-03","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":"2102.02515","title":"HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks","created":"2021-02-04","cited_by_count":38,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2310.11046","title":"Fast Graph Condensation with Structure-based Neural Tangent Kernel","created":"2023-10-17","cited_by_count":36,"area":null},{"id":"2110.05649","title":"Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection","created":"2021-10-11","cited_by_count":34,"area":null},{"id":"2304.03995","title":"Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization","created":"2023-04-08","cited_by_count":33,"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":"2107.10847","title":"Accelerating Quadratic Optimization with Reinforcement Learning","created":"2021-07-22","cited_by_count":30,"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":"2212.14032","title":"On Implicit Bias in Overparameterized Bilevel Optimization","created":"2022-12-28","cited_by_count":30,"area":null},{"id":"2205.03076","title":"Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation","created":"2022-05-06","cited_by_count":26,"area":null},{"id":"2207.11719","title":"Gradient-based Bi-level Optimization for Deep Learning: A Survey","created":"2022-07-24","cited_by_count":26,"area":null},{"id":"2402.05232","title":"Universal Neural Functionals","created":"2024-02-07","cited_by_count":26,"area":null},{"id":"2109.10781","title":"Introducing Symmetries to Black Box Meta Reinforcement Learning","created":"2021-09-22","cited_by_count":25,"area":null},{"id":"2106.00958","title":"A Generalizable Approach to Learning Optimizers","created":"2021-06-02","cited_by_count":23,"area":null},{"id":"2210.01802","title":"Alternating Differentiation for Optimization Layers","created":"2022-10-03","cited_by_count":22,"area":null},{"id":"2310.05674","title":"Making Scalable Meta Learning Practical","created":"2023-10-09","cited_by_count":17,"area":null},{"id":"2203.06578","title":"Symbolic Learning to Optimize: Towards Interpretability and Scalability","created":"2022-03-13","cited_by_count":16,"area":null},{"id":"2304.11787","title":"B2Opt: Learning to Optimize Black-box Optimization with Little Budget","created":"2023-04-24","cited_by_count":15,"area":null},{"id":"2101.07367","title":"Training Learned Optimizers with Randomly Initialized Learned Optimizers","created":"2021-01-14","cited_by_count":13,"area":null},{"id":"2212.01055","title":"Transformer-Based Learned Optimization","created":"2022-12-02","cited_by_count":13,"area":null},{"id":"2305.18577","title":"Towards Constituting Mathematical Structures for Learning to Optimize","created":"2023-05-29","cited_by_count":13,"area":null},{"id":"2402.06184","title":"The boundary of neural network trainability is fractal","created":"2024-02-09","cited_by_count":13,"area":null},{"id":"2502.02854","title":"TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation","created":"2025-02-05","cited_by_count":13,"area":null},{"id":"2105.14900","title":"A unified view of likelihood ratio and reparameterization gradients","created":"2021-05-31","cited_by_count":12,"area":null},{"id":"2107.09661","title":"Learn2Hop: Learned Optimization on Rough Landscapes","created":"2021-07-20","cited_by_count":12,"area":null},{"id":"2302.01470","title":"Learning to Optimize for Reinforcement Learning","created":"2023-02-03","cited_by_count":12,"area":null},{"id":"2401.03244","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","created":"2024-01-06","cited_by_count":11,"area":null},{"id":"2104.04657","title":"Meta-Learning Bidirectional Update Rules","created":"2021-04-10","cited_by_count":10,"area":null},{"id":"2410.06324","title":"Differentiation Through Black-Box Quadratic Programming Solvers","created":"2024-10-08","cited_by_count":10,"area":null},{"id":"2103.05147","title":"Model-free Policy Learning with Reward Gradients","created":"2021-03-09","cited_by_count":9,"area":null},{"id":"2104.10631","title":"MetricOpt: Learning to Optimize Black-Box Evaluation Metrics","created":"2021-04-21","cited_by_count":9,"area":null},{"id":"2302.01128","title":"Mnemosyne: Learning to Train Transformers with Transformers","created":"2023-02-02","cited_by_count":9,"area":null},{"id":"2302.11085","title":"Learning to Generalize Provably in Learning to Optimize","created":"2023-02-22","cited_by_count":8,"area":null},{"id":"2305.09129","title":"Graph Reinforcement Learning for Network Control via Bi-Level Optimization","created":"2023-05-16","cited_by_count":8,"area":null},{"id":"2104.10527","title":"Stateless Neural Meta-Learning using Second-Order Gradients","created":"2021-04-21","cited_by_count":7,"area":null},{"id":"2304.11153","title":"Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single","created":"2023-04-21","cited_by_count":7,"area":null},{"id":"2310.04590","title":"Deep Model Predictive Optimization","created":"2023-10-06","cited_by_count":7,"area":null},{"id":"2310.19927","title":"Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms","created":"2023-10-30","cited_by_count":7,"area":null},{"id":"2406.00153","title":"$\\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers","created":"2024-05-31","cited_by_count":7,"area":null},{"id":"2507.01649","title":"GradMetaNet: An Equivariant Architecture for Learning on Gradients","created":"2025-07-02","cited_by_count":7,"area":null},{"id":"2202.02790","title":"Learning Synthetic Environments and Reward Networks for Reinforcement Learning","created":"2022-02-06","cited_by_count":6,"area":null},{"id":"2209.13575","title":"Efficient Non-Parametric Optimizer Search for Diverse Tasks","created":"2022-09-27","cited_by_count":6,"area":null},{"id":"2111.00206","title":"One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning","created":"2021-10-30","cited_by_count":5,"area":null},{"id":"2209.11303","title":"An Investigation of the Bias-Variance Tradeoff in Meta-Gradients","created":"2022-09-22","cited_by_count":5,"area":null},{"id":"2304.12180","title":"Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies","created":"2023-04-21","cited_by_count":5,"area":null},{"id":"2402.04915","title":"Moco: A Learnable Meta Optimizer for Combinatorial Optimization","created":"2024-02-07","cited_by_count":5,"area":null},{"id":"2102.07813","title":"Online hyperparameter optimization by real-time recurrent learning","created":"2021-02-15","cited_by_count":4,"area":null},{"id":"2304.07442","title":"Learning To Optimize Quantum Neural Network Without Gradients","created":"2023-04-15","cited_by_count":4,"area":null},{"id":"2408.09140","title":"Learning to Explore for Stochastic Gradient MCMC","created":"2024-08-17","cited_by_count":4,"area":null},{"id":"2501.12670","title":"Celo: Training Versatile Learned Optimizers on a Compute Diet","created":"2025-01-22","cited_by_count":4,"area":null},{"id":"2512.01565","title":"Deep FlexQP: Accelerated Nonlinear Programming via Deep Unfolding","created":"2025-12-01","cited_by_count":4,"area":null},{"id":"2602.16928","title":"Discovering Multiagent Learning Algorithms with Large Language Models","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2101.09721","title":"Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies","created":"2021-01-24","cited_by_count":3,"area":null},{"id":"2210.06171","title":"Learning to Optimize Quasi-Newton Methods","created":"2022-10-11","cited_by_count":3,"area":null},{"id":"2410.10417","title":"A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2510.00419","title":"Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs","created":"2025-10-01","cited_by_count":3,"area":null},{"id":"2111.12545","title":"ModelPred: A Framework for Predicting Trained Model from Training Data","created":"2021-11-24","cited_by_count":2,"area":null},{"id":"2301.07806","title":"Federated Automatic Differentiation","created":"2023-01-18","cited_by_count":2,"area":null},{"id":"2310.18191","title":"Is Scaling Learned Optimizers Worth It? Evaluating The Value of VeLO's 4000 TPU Months","created":"2023-10-27","cited_by_count":2,"area":null},{"id":"2312.02204","title":"Meta-learning Optimizers for Communication-Efficient Learning","created":"2023-12-02","cited_by_count":2,"area":null},{"id":"2405.18222","title":"From Learning to Optimize to Learning Optimization Algorithms","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2502.02190","title":"Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization","created":"2025-02-04","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":"2203.06474","title":"Optimizer Amalgamation","created":"2022-03-12","cited_by_count":1,"area":null},{"id":"2410.07704","title":"A Generalization Result for Convergence in Learning-to-Optimize","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2602.19142","title":"Celo2: Towards Learned Optimization Free Lunch","created":"2026-02-22","cited_by_count":1,"area":null},{"id":"2112.09990","title":"FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows","created":"2021-12-18","cited_by_count":0,"area":null},{"id":"2312.07174","title":"Investigation into the Training Dynamics of Learned Optimizers","created":"2023-12-12","cited_by_count":0,"area":null},{"id":"2510.08968","title":"Learning Regularizers: Learning Optimizers that can Regularize","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2510.18783","title":"Enhancing Fractional Gradient Descent with Learned Optimizers","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2607.06772","title":"Efficient Long-Horizon Learning for Learned Optimization","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2609.21758","title":"Bilevel Optimization of Topology and Hyperparameters (BOTH)","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.22342","title":"Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization","created":"2026-09-16","cited_by_count":0,"area":null}],"next_cursor":null}