{"total":42,"rows":[{"id":"2402.19449","title":"Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models","created":"2024-02-29","cited_by_count":80,"area":null},{"id":"2305.17490","title":"The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent","created":"2023-05-27","cited_by_count":44,"area":null},{"id":"2502.04664","title":"Implicit Bias of Spectral Descent and Muon on Multiclass Separable Data","created":"2025-02-07","cited_by_count":41,"area":null},{"id":"2405.16002","title":"Does SGD really happen in tiny subspaces?","created":"2024-05-25","cited_by_count":36,"area":null},{"id":"2406.10650","title":"The Implicit Bias of Adam on Separable Data","created":"2024-06-15","cited_by_count":36,"area":null},{"id":"2402.15926","title":"Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency","created":"2024-02-24","cited_by_count":35,"area":null},{"id":"2307.04204","title":"Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory","created":"2023-07-09","cited_by_count":26,"area":null},{"id":"2311.02076","title":"Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos","created":"2023-11-03","cited_by_count":22,"area":null},{"id":"2310.01687","title":"From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression","created":"2023-10-02","cited_by_count":17,"area":null},{"id":"2406.08654","title":"Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization","created":"2024-06-12","cited_by_count":17,"area":null},{"id":"2402.18724","title":"Learning Associative Memories with Gradient Descent","created":"2024-02-28","cited_by_count":16,"area":null},{"id":"2406.05033","title":"Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes","created":"2024-06-07","cited_by_count":14,"area":null},{"id":"2502.13283","title":"Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression","created":"2025-02-18","cited_by_count":9,"area":null},{"id":"2506.02336","title":"Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression","created":"2025-06-03","cited_by_count":9,"area":null},{"id":"2310.17074","title":"Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates","created":"2023-10-26","cited_by_count":8,"area":null},{"id":"2504.04105","title":"Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes","created":"2025-04-05","cited_by_count":7,"area":null},{"id":"2506.06940","title":"Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2507.11228","title":"Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?","created":"2025-07-15","cited_by_count":5,"area":null},{"id":"2311.14387","title":"Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling","created":"2023-11-24","cited_by_count":4,"area":null},{"id":"2510.17506","title":"Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2311.15051","title":"Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults","created":"2023-11-25","cited_by_count":3,"area":null},{"id":"2601.06597","title":"Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective","created":"2026-01-10","cited_by_count":3,"area":null},{"id":"2605.19458","title":"Implicit Bias of Mirror Flow in Homogeneous Neural Networks: Sparse and Dense Feature Learning","created":"2026-05-19","cited_by_count":3,"area":null},{"id":"2606.18080","title":"Edge Flow: A Tractable and Predictive Continuous-Time Model for Gradient Descent at the Edge of Stability","created":"2026-06-16","cited_by_count":3,"area":null},{"id":"2506.12903","title":"Variational Learning Finds Flatter Solutions at the Edge of Stability","created":"2025-06-15","cited_by_count":2,"area":null},{"id":"2506.13974","title":"Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2512.04006","title":"Diagonalizing the Softmax: Hadamard Initialization for Tractable Cross-Entropy Dynamics","created":"2025-12-03","cited_by_count":2,"area":null},{"id":"2406.00127","title":"Training on the Edge of Stability Is Caused by Layerwise Jacobian Alignment","created":"2024-05-31","cited_by_count":1,"area":null},{"id":"2502.04889","title":"Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2602.12471","title":"Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2604.02653","title":"Product-Stability: Provable Convergence for Gradient Descent on the Edge of Stability","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2606.21683","title":"Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design","created":"2026-06-19","cited_by_count":1,"area":null},{"id":"2607.08380","title":"Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima","created":"2026-07-09","cited_by_count":1,"area":null},{"id":"2608.18415","title":"The Road Taken: The Role of Optimizers at the Edge of Stability","created":"2026-08-19","cited_by_count":1,"area":null},{"id":"2510.09034","title":"Convergence of optimizers implies eigenvalues filtering at equilibrium","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2602.00827","title":"Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2602.18946","title":"Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression","created":"2026-02-21","cited_by_count":0,"area":null},{"id":"2604.02920","title":"Efficient Logistic Regression with Mixture of Sigmoids","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2604.16809","title":"A Mechanism Study of Delayed Loss Spikes in Batch-Normalized Linear Models","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2604.18202","title":"Centre manifold theorem for maps along manifolds of fixed points","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2605.23087","title":"The Implicit Bias of Depth: From Neural Collapse to Softmax Codes","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2609.01034","title":"The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow","created":"2026-09-01","cited_by_count":0,"area":null}],"next_cursor":null}