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