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Ensemble Methods

300 papers

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Papers

Aggregating Binary Judgments Ranked by Accuracy AAAI 2021 Analyzing the tree-layer structure of Deep Forests ICML 2021 Anytime Inference with Distilled Hierarchical Neural Ensembles AAAI 2021 Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees? ICLR 2021 Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics CVPR 2021 Boosting in the Presence of Massart Noise COLT 2021 Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification EMNLP 2021 Efficient Training of Robust Decision Trees Against Adversarial Examples ICML 2021 Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks AAAI 2021 Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling ICML 2021 Machine Unlearning for Random Forests ICML 2021 Regressive Ensemble for Machine Translation Quality Evaluation EMNLP 2021 RTM Super Learner Results at Quality Estimation Task EMNLP 2021 TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness NeurIPS 2021 Versatile Verification of Tree Ensembles ICML 2021 Written Justifications are Key to Aggregate Crowdsourced Forecasts EMNLP 2021 A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning AAAI 2020 Arabic dialect identification: An Arabic-BERT model with data augmentation and ensembling strategy COLING 2020 Arabic Dialects Identification for All Arabic countries COLING 2020 Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training EMNLP 2020 CASIA’s System for IWSLT 2020 Open Domain Translation ACL 2020 Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method AISTATS 2020 Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification COLING 2020 CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets EMNLP 2020 Deep Dynamic Boosted Forest ACML 2020