linear model

A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits AISTATS 2021 Approximate Data Deletion from Machine Learning Models AISTATS 2021 Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models NeurIPS 2021 Contrastive learning, multi-view redundancy, and linear models ALT 2021 DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks AISTATS 2021 Exploiting Equality Constraints in Causal Inference AISTATS 2021 How Important is the Train-Validation Split in Meta-Learning? ICML 2021 Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model JMLR 2021 Provably Strict Generalisation Benefit for Equivariant Models ICML 2021 RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection SemEval 2021 Regularizing towards Causal Invariance: Linear Models with Proxies ICML 2021 Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings SemEval 2021 Understanding the Dynamics of Gradient Flow in Overparameterized Linear models ICML 2021 A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models AAAI 2020 Calibrated Surrogate Losses for Adversarially Robust Classification COLT 2020 Debiasing Averaged Stochastic Gradient Descent to handle missing values NeurIPS 2020 Field-wise Learning for Multi-field Categorical Data NeurIPS 2020 Gradients as Features for Deep Representation Learning ICLR 2020 Model Interpretability through the lens of Computational Complexity NeurIPS 2020 The Statistical Complexity of Early-Stopped Mirror Descent NeurIPS 2020 The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks NeurIPS 2020 Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NeurIPS 2020 Adaptive Scale-Invariant Online Algorithms for Learning Linear Models ICML 2019 Large Scale Incremental Learning CVPR 2019 Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations ALT 2019