Regression

DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021? SemEval 2021 Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions MLHC 2021 Efficient Algorithms for Learning from Coarse Labels COLT 2021 Efficient Designs Of SLOPE Penalty Sequences In Finite Dimension AISTATS 2021 Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging NeurIPS 2021 Efficient Statistics for Sparse Graphical Models from Truncated Samples AISTATS 2021 Efficient Truncated Linear Regression with Unknown Noise Variance NeurIPS 2021 Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ACL 2021 Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled EACL 2021 EmpNa at WASSA 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories EACL 2021 Encoder-decoder-based image transformation approach for integrating precipitation forecasts ACML 2021 Entangled Kernels - Beyond Separability JMLR 2021 Error Analysis and the Role of Morphology EACL 2021 Error-Sensitive Evaluation for Ordinal Target Variables EMNLP 2021 Estimating Identifiable Causal Effects through Double Machine Learning AAAI 2021 Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data ALT 2021 Estimating the Lasso's Effective Noise JMLR 2021 Estimating Uncertainty Intervals from Collaborating Networks JMLR 2021 Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach JMLR 2021 Evading the Curse of Dimensionality in Unconstrained Private GLMs AISTATS 2021 Evaluating Scholarly Impact: Towards Content-Aware Bibliometrics EMNLP 2021 Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models ICML 2021 Expanding Boundaries of Gap Safe Screening JMLR 2021 Explainable Quality Estimation: CUNI Eval4NLP Submission EMNLP 2021 Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach NeurIPS 2021