linear regression

High-dimensional (Group) Adversarial Training in Linear Regression NeurIPS 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 Large Language Models as Optimizers ICLR 2024 Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning ICML 2024 Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions ICLR 2024 Learning from many trajectories JMLR 2024 Learning from Uncertain Data: From Possible Worlds to Possible Models NeurIPS 2024 Linear Regression using Heterogeneous Data Batches NeurIPS 2024 Linear Regression With Unmatched Data: A Deconvolution Perspective JMLR 2024 Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning CLeaR 2024 Minimax Linear Regression under the Quantile Risk COLT 2024 Minimum-Norm Interpolation Under Covariate Shift ICML 2024 Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization AISTATS 2024 One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention ICLR 2024 On the Efficiency of ERM in Feature Learning NeurIPS 2024 On the relationship between speech production and vocabulary size in 3-5 year olds Interspeech 2024 Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation ICML 2024 Private Learning with Public Features AISTATS 2024 Provable and Efficient Dataset Distillation for Kernel Ridge Regression NeurIPS 2024 Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable NeurIPS 2024 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression ICLR 2024 Robust Sparse Regression with Non-Isotropic Designs NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data NeurIPS 2024 Team jelarson at SemEval 2024 Task 8: Predicting Boundary Line Between Human and Machine Generated Text SemEval 2024 Test-Time Linear Out-of-Distribution Detection CVPR 2024