linear classification

Margin in Abstract Spaces COLT 2026 Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification ICLR 2025 Efficient Optimization Algorithms for Linear Adversarial Training AISTATS 2025 Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context ICLR 2025 Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow ICLR 2025 Isotropy, Clusters, and Classifiers ACL 2024 Measuring Self-Supervised Representation Quality for Downstream Classification Using Discriminative Features AAAI 2024 The importance of feature preprocessing for differentially private linear optimization ICLR 2024 Continual Learning in Linear Classification on Separable Data ICML 2023 Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation ICCV 2023 Provable Benefit of Mixup for Finding Optimal Decision Boundaries ICML 2023 Self-Directed Linear Classification COLT 2023 Tight Risk Bounds for Gradient Descent on Separable Data NeurIPS 2023 Why adversarial training can hurt robust accuracy ICLR 2023 Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? ICLR 2022 Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization NeurIPS 2021 Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters NeurIPS 2021 Unsupervised Representation Learning via Neural Activation Coding ICML 2021 A Limitation of the PAC-Bayes Framework NeurIPS 2020 Predictive Multiplicity in Classification ICML 2020 Unsupervised Representation Learning by Invariance Propagation NeurIPS 2020 Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case ICML 2019 Distributed Inference for Linear Support Vector Machine JMLR 2019 Meta-Learning With Differentiable Convex Optimization CVPR 2019 The Implicit Bias of AdaGrad on Separable Data NeurIPS 2019