{"total":110,"rows":[{"id":"2306.05284","title":"Simple and Controllable Music Generation","created":"2023-06-08","cited_by_count":488,"area":null},{"id":"2409.20325","title":"Old Optimizer, New Norm: An Anthology","created":"2024-09-30","cited_by_count":193,"area":null},{"id":"2309.14322","title":"Small-scale proxies for large-scale Transformer training instabilities","created":"2023-09-25","cited_by_count":183,"area":null},{"id":"2405.15682","title":"The Road Less Scheduled","created":"2024-05-24","cited_by_count":163,"area":null},{"id":"2306.06101","title":"Prodigy: An Expeditiously Adaptive Parameter-Free Learner","created":"2023-06-09","cited_by_count":133,"area":null},{"id":"2310.02994","title":"Multiple Physics Pretraining for Physical Surrogate Models","created":"2023-10-04","cited_by_count":98,"area":null},{"id":"2302.12022","title":"DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule","created":"2023-02-08","cited_by_count":85,"area":null},{"id":"2405.14813","title":"Scalable Optimization in the Modular Norm","created":"2024-05-23","cited_by_count":68,"area":null},{"id":"2306.10619","title":"Towards Stability of Autoregressive Neural Operators","created":"2023-06-18","cited_by_count":59,"area":null},{"id":"2401.04577","title":"Masked Audio Generation using a Single Non-Autoregressive Transformer","created":"2024-01-09","cited_by_count":50,"area":null},{"id":"2305.16284","title":"DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method","created":"2023-05-25","cited_by_count":35,"area":null},{"id":"2402.15734","title":"Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning","created":"2024-02-24","cited_by_count":32,"area":null},{"id":"2401.00847","title":"Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera","created":"2024-01-01","cited_by_count":31,"area":null},{"id":"2305.07583","title":"MoMo: Momentum Models for Adaptive Learning Rates","created":"2023-05-12","cited_by_count":24,"area":null},{"id":"2309.11962","title":"Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views","created":"2023-09-21","cited_by_count":22,"area":null},{"id":"2506.02285","title":"Why Gradients Rapidly Increase Near the End of Training","created":"2025-06-02","cited_by_count":22,"area":null},{"id":"2308.02261","title":"Adaptive Proximal Gradient Method for Convex Optimization","created":"2023-08-04","cited_by_count":21,"area":null},{"id":"2303.12277","title":"Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation","created":"2023-03-22","cited_by_count":20,"area":null},{"id":"2306.00144","title":"Mechanic: A Learning Rate Tuner","created":"2023-05-31","cited_by_count":20,"area":null},{"id":"2310.10082","title":"A simple uniformly optimal method without line search for convex optimization","created":"2023-10-16","cited_by_count":18,"area":null},{"id":"2409.07450","title":"VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos","created":"2024-09-11","cited_by_count":15,"area":null},{"id":"2402.07793","title":"Tuning-Free Stochastic Optimization","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2402.03126","title":"How Free is Parameter-Free Stochastic Optimization?","created":"2024-02-05","cited_by_count":12,"area":null},{"id":"2506.23803","title":"SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration","created":"2025-06-30","cited_by_count":12,"area":null},{"id":"2405.20540","title":"Fully Unconstrained Online Learning","created":"2024-05-30","cited_by_count":10,"area":null},{"id":"2407.04358","title":"An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes","created":"2024-07-05","cited_by_count":10,"area":null},{"id":"2310.11291","title":"An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent","created":"2023-10-17","cited_by_count":9,"area":null},{"id":"2407.02772","title":"Gradient descent with generalized Newton's method","created":"2024-07-03","cited_by_count":9,"area":null},{"id":"2403.02648","title":"Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad","created":"2024-03-05","cited_by_count":8,"area":null},{"id":"2407.06183","title":"Stepping on the Edge: Curvature Aware Learning Rate Tuners","created":"2024-07-08","cited_by_count":8,"area":null},{"id":"2306.11201","title":"Adaptive Federated Learning with Auto-Tuned Clients","created":"2023-06-19","cited_by_count":7,"area":null},{"id":"2503.00703","title":"Towards hyperparameter-free optimization with differential privacy","created":"2025-03-02","cited_by_count":7,"area":null},{"id":"2506.19882","title":"Position: Machine Learning Conferences Should Establish a \"Refutations and Critiques\" Track","created":"2025-06-24","cited_by_count":7,"area":null},{"id":"2605.19095","title":"ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models","created":"2026-05-18","cited_by_count":7,"area":null},{"id":"2311.08615","title":"Non-Uniform Smoothness for Gradient Descent","created":"2023-11-15","cited_by_count":5,"area":null},{"id":"2312.17369","title":"SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms","created":"2023-12-28","cited_by_count":5,"area":null},{"id":"2402.02342","title":"MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters","created":"2024-02-04","cited_by_count":5,"area":null},{"id":"2502.09172","title":"LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data","created":"2025-02-13","cited_by_count":5,"area":null},{"id":"2504.01898","title":"Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation","created":"2025-04-02","cited_by_count":5,"area":null},{"id":"2507.09823","title":"Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization","created":"2025-07-13","cited_by_count":5,"area":null},{"id":"2602.05813","title":"Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers","created":"2026-02-05","cited_by_count":5,"area":null},{"id":"2304.04172","title":"$\\mu^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism","created":"2023-04-09","cited_by_count":4,"area":null},{"id":"2411.01088","title":"CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks","created":"2024-11-02","cited_by_count":4,"area":null},{"id":"2510.00823","title":"Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization","created":"2025-10-01","cited_by_count":4,"area":null},{"id":"2401.13971","title":"Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity","created":"2024-01-25","cited_by_count":3,"area":null},{"id":"2402.05264","title":"AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size","created":"2024-02-07","cited_by_count":3,"area":null},{"id":"2405.15010","title":"Parameter-free Clipped Gradient Descent Meets Polyak","created":"2024-05-23","cited_by_count":3,"area":null},{"id":"2405.19585","title":"The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2501.10258","title":"DADA: Dual Averaging with Distance Adaptation","created":"2025-01-17","cited_by_count":3,"area":null},{"id":"2505.24399","title":"LightSAM: Parameter-Agnostic Sharpness-Aware Minimization","created":"2025-05-30","cited_by_count":3,"area":null},{"id":"2602.07145","title":"Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate","created":"2026-02-06","cited_by_count":3,"area":null},{"id":"2605.23061","title":"Anytime Training with Schedule-Free Spectral Optimization","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2305.19605","title":"Parameter-free projected gradient descent","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2405.12521","title":"Unleash Graph Neural Networks from Heavy Tuning","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2412.19444","title":"Towards Simple and Provable Parameter-Free Adaptive Gradient Methods","created":"2024-12-27","cited_by_count":2,"area":null},{"id":"2505.21651","title":"AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2505.24005","title":"How far away are truly hyperparameter-free learning algorithms?","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2509.18396","title":"Development of Deep Learning Optimizers: Approaches, Concepts, and Update Rules","created":"2025-09-22","cited_by_count":2,"area":null},{"id":"2510.17067","title":"Convergence of Regret Matching in Potential Games and Constrained Optimization","created":"2025-10-20","cited_by_count":2,"area":null},{"id":"2512.06370","title":"Greedy Alignment Principle for Optimizer Selection","created":"2025-12-06","cited_by_count":2,"area":null},{"id":"2305.01377","title":"Random Function Descent","created":"2023-05-02","cited_by_count":1,"area":null},{"id":"2305.09978","title":"Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems","created":"2023-05-17","cited_by_count":1,"area":null},{"id":"2405.04376","title":"Towards Stability of Parameter-Free Optimization","created":"2024-05-07","cited_by_count":1,"area":null},{"id":"2406.02296","title":"Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2410.02145","title":"Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes","created":"2024-10-03","cited_by_count":1,"area":null},{"id":"2410.05697","title":"Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2412.17082","title":"MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes","created":"2024-12-22","cited_by_count":1,"area":null},{"id":"2501.06954","title":"A Hessian-informed hyperparameter optimization for differential learning rate","created":"2025-01-12","cited_by_count":1,"area":null},{"id":"2502.03701","title":"First-ish Order Methods: Hessian-aware Scalings of Gradient Descent","created":"2025-02-06","cited_by_count":1,"area":null},{"id":"2506.11336","title":"The Sample Complexity of Parameter-Free Stochastic Convex Optimization","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2510.04988","title":"Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization","created":"2025-10-06","cited_by_count":1,"area":null},{"id":"2511.04981","title":"Scaling depth capacity via zero/one-layer model expansion","created":"2025-11-07","cited_by_count":1,"area":null},{"id":"2512.23884","title":"Autoregressive long-horizon prediction of plasma edge dynamics","created":"2025-12-29","cited_by_count":1,"area":null},{"id":"2601.20076","title":"Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes","created":"2026-01-27","cited_by_count":1,"area":null},{"id":"2602.13112","title":"AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2605.07841","title":"\\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.18999","title":"Distance-Aware Muon: Adaptive Step Scaling for Normalized Optimization","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2606.01827","title":"Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2304.03374","title":"Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2306.17470","title":"Oblivious Stochastic Composite Optimization","created":"2023-06-30","cited_by_count":0,"area":null},{"id":"2308.03102","title":"Learning-Rate-Free Learning: Dissecting D-Adaptation and Probabilistic Line Search","created":"2023-08-06","cited_by_count":0,"area":null},{"id":"2311.13877","title":"Locally Optimal Descent for Dynamic Stepsize Scheduling","created":"2023-11-23","cited_by_count":0,"area":null},{"id":"2401.03240","title":"Interpreting Adaptive Gradient Methods by Parameter Scaling for Learning-Rate-Free Optimization","created":"2024-01-06","cited_by_count":0,"area":null},{"id":"2402.01515","title":"Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2409.04424","title":"Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques","created":"2024-09-06","cited_by_count":0,"area":null},{"id":"2410.05871","title":"A second-order-like optimizer with adaptive gradient scaling for deep learning","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2410.18973","title":"Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2411.06573","title":"An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method","created":"2024-11-10","cited_by_count":0,"area":null},{"id":"2411.16975","title":"ExpTest: Loss-Curve Hypothesis Testing for Autonomous Learning-Rate Selection in Deep Neural Networks","created":"2024-11-25","cited_by_count":0,"area":null},{"id":"2501.07405","title":"PROTECT: Protein circadian time prediction using unsupervised learning","created":"2025-01-13","cited_by_count":0,"area":null},{"id":"2503.09411","title":"Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2506.22505","title":"Weakly Supervised Object Segmentation by Background Conditional Divergence","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2507.01724","title":"Revisiting Learning Rate Control","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2508.05408","title":"Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2509.14969","title":"Stochastic Adaptive Gradient Descent Without Descent","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2509.21653","title":"A regret minimization approach to fixed-point iterations","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2510.09923","title":"AutoGD: Automatic Learning Rate Selection for Gradient Descent","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2510.20157","title":"ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2601.15212","title":"ZENITH: Automated Gradient Norm Informed Stochastic Optimization","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2602.09842","title":"Step-Size Stability in Stochastic Optimization: A Theoretical Perspective","created":"2026-02-10","cited_by_count":0,"area":null}],"next_cursor":"100"}