logistic loss

Reassessing How to Compare and Improve the Calibration of Machine Learning Models ICLR 2025 Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods ICLR 2025 A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces ICML 2024 Classification with Deep Neural Networks and Logistic Loss JMLR 2024 Discovering Preference Optimization Algorithms with and for Large Language Models NeurIPS 2024 Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization NeurIPS 2024 Learning with Fitzpatrick Losses NeurIPS 2024 Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss COLT 2024 The Space Complexity of Approximating Logistic Loss NeurIPS 2024 Structured Prediction with Stronger Consistency Guarantees NeurIPS 2023 Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data COLT 2022 Fast Sparse Classification for Generalized Linear and Additive Models AISTATS 2022 Self-training Converts Weak Learners to Strong Learners in Mixture Models AISTATS 2022 Coresets for Classification – Simplified and Strengthened NeurIPS 2021 Early-stopped neural networks are consistent NeurIPS 2021 Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime JMLR 2021 On the interplay between data structure and loss function in classification problems NeurIPS 2021 When Does Gradient Descent with Logistic Loss Find Interpolating Two-Layer Networks? JMLR 2021 When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? COLT 2021 Gradient descent follows the regularization path for general losses COLT 2020 Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss COLT 2020 On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks AAAI 2020 Robust high dimensional learning for Lipschitz and convex losses JMLR 2020 Convergence of Gradient Descent on Separable Data AISTATS 2019 Geometric Losses for Distributional Learning ICML 2019