Awesome Gradient Boosting Research Papers.

A curated list of gradient and adaptive boosting papers with implementations from the following conferences:

• Machine learning
  • NeurIPS
  • ICML
  • ICLR
• Computer vision
  • CVPR
  • ICCV
  • ECCV
• Natural language processing
  • ACL
  • NAACL
  • EMNLP
• Data
  • KDD
  • CIKM
  • ICDM
  • SDM
  • PAKDD
  • PKDD/ECML
  • RECSYS
  • SIGIR
  • WWW
  • WSDM
• Artificial intelligence
  • AAAI
  • AISTATS
  • ICANN
  • IJCAI
  • UAI

Similar collections about graph classification, classification/regression tree, fraud detection, Monte Carlo tree search, and community detection papers with implementations.

2019

• Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME (AAAI 2019)

  • Farhad Shakerin, Gopal Gupta
  • [Paper]

• Verifying Robustness of Gradient Boosted Models (AAAI 2019)

  • Gil Einziger, Maayan Goldstein, Yaniv Sa'ar, Itai Segall
  • [Paper]

• Online Multiclass Boosting with Bandit Feedback (AISTATS 2019)

  • Daniel T. Zhang, Young Hun Jung, Ambuj Tewari
  • [Paper]

• AdaFair: Cumulative Fairness Adaptive Boosting (CIKM 2019)

  • Vasileios Iosifidis, Eirini Ntoutsi
  • [Paper]

• Interpretable MTL from Heterogeneous Domains using Boosted Tree (CIKM 2019)

  • Ya-Lin Zhang, Longfei Li
  • [Paper]

• Adversarial Training of Gradient-Boosted Decision Trees (CIKM 2019)

  • Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei
  • [Paper]

• Boosted Density Estimation Remastered (ICML 2019)

  • Zac Cranko, Richard Nock
  • [Paper]

• Lossless or Quantized Boosting with Integer Arithmetic (ICML 2019)

  • Richard Nock, Robert C. Williamson
  • [Paper]

• Optimal Minimal Margin Maximization with Boosting (ICML 2019)

  • Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund
  • [Paper]

• Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number (ICML 2019)

  • Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang
  • [Paper]

• Boosting for Comparison-Based Learning (IJCAI 2019)

  • Michaël Perrot, Ulrike von Luxburg
  • [Paper]

• AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation (IJCAI 2019)

  • Philip Tannor, Lior Rokach
  • [Paper]

• Gradient Boosting with Piece-Wise Linear Regression Trees (IJCAI 2019)

  • Yu Shi, Jian Li, Zhize Li
  • [Paper]
  • [Code]

• Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)

  • Maksym Andriushchenko, Matthias Hein
  • [Paper]
  • [Code]

• Block-distributed Gradient Boosted Trees (SIGIR 2019)

  • Theodore Vasiloudis, Hyunsu Cho, Henrik Boström
  • [Paper]

• Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning (SIGIR 2019)

  • Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
  • [Paper]

2018

• Boosted Generative Models (AAAI 2018)

  • Aditya Grover, Stefano Ermon
  • [Paper]
  • [Code]

• Boosting Variational Inference: an Optimization Perspective (AISTATS 2018)

  • Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch
  • [Paper]
  • [Code]

• Online Boosting Algorithms for Multi-label Ranking (AISTATS 2018)

  • Young Hun Jung, Ambuj Tewari
  • [Paper]
  • [Code]

• DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain (CIKM 2018)

  • Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen
  • [Paper]

• Functional Gradient Boosting based on Residual Network Perception (ICML 2018)

  • Atsushi Nitanda, Taiji Suzuki
  • [Paper]
  • [Code]

• Finding Influential Training Samples for Gradient Boosted Decision Trees (ICML 2018)

  • Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke
  • [Paper]

• Learning Deep ResNet Blocks Sequentially using Boosting Theory (ICML 2018)

  • Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
  • [Paper]
  • [Code]

• UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits (IJCAI 2018)

  • Fang Liu, Sinong Wang, Swapna Buccapatnam, Ness B. Shroff
  • [Paper]
  • [Code]

• Adaboost with Auto-Evaluation for Conversational Models (IJCAI 2018)

  • Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu
  • [Paper]

• Ensemble Neural Relation Extraction with Adaptive Boosting (IJCAI 2018)

  • Dongdong Yang, Senzhang Wang, Zhoujun Li
  • [Paper]

• CatBoost: Unbiased Boosting with Categorical Features (NIPS 2018)

  • Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
  • [Paper]
  • [Code]

• Multitask Boosting for Survival Analysis with Competing Risks (NIPS 2018)

  • Alexis Bellot, Mihaela van der Schaar
  • [Paper]

• Multi-Layered Gradient Boosting Decision Trees (NIPS 2018)

  • Ji Feng, Yang Yu, Zhi-Hua Zhou
  • [Paper]
  • [Code]

• Boosted Sparse and Low-Rank Tensor Regression (NIPS 2018)

  • Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang
  • [Paper]
  • [Code]

• Selective Gradient Boosting for Effective Learning to Rank (SIGIR 2018)

  • Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani
  • [Paper]
  • [Code]

2017

• Boosting for Real-Time Multivariate Time Series Classification (AAAI 2017)

  • Haishuai Wang, Jun Wu
  • [Paper]

• Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost (AAAI 2017)

  • Xingchang Huang, Yanghui Rao, Haoran Xie, Tak-Lam Wong, Fu Lee Wang
  • [Paper]
  • [Code]

• Extreme Gradient Boosting and Behavioral Biometrics (AAAI 2017)

  • Benjamin Manning
  • [Paper]

• FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation (AAAI 2017)

  • Yulei Niu, Zhiwu Lu, Songfang Huang, Xin Gao, Ji-Rong Wen
  • [Paper]

• Boosting Complementary Hash Tables for Fast Nearest Neighbor Search (AAAI 2017)

  • Xianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li
  • [Paper]

• Gradient Boosting on Stochastic Data Streams (AISTATS 2017)

  • Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
  • [Paper]

• BoostVHT: Boosting Distributed Streaming Decision Trees (CIKM 2017)

  • Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales
  • [Paper]

• Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features (CVPR 2017)

  • Arthur Daniel Costea, Robert Varga, Sergiu Nedevschi
  • [Paper]

• BIER - Boosting Independent Embeddings Robustly (ICCV 2017)

  • Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof
  • [Paper]
  • [Code]

• An Analysis of Boosted Linear Classifiers on Noisy Data with Applications to Multiple-Instance Learning (ICDM 2017)

  • Rui Liu, Soumya Ray
  • [Paper]

• Variational Boosting: Iteratively Refining Posterior Approximations (ICML 2017)

  • Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
  • [Paper]
  • [Code]

• Boosted Fitted Q-Iteration (ICML 2017)

  • Samuele Tosatto, Matteo Pirotta, Carlo D'Eramo, Marcello Restelli
  • [Paper]

• A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency (ICML 2017)

  • Ron Appel, Pietro Perona
  • [Paper]
  • [Code]

• Gradient Boosted Decision Trees for High Dimensional Sparse Output (ICML 2017)

  • Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh
  • [Paper]
  • [Code]

• Local Topic Discovery via Boosted Ensemble of Nonnegative Matrix Factorization (IJCAI 2017)

  • Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy
  • [Paper]
  • [Code]

• Boosted Zero-Shot Learning with Semantic Correlation Regularization (IJCAI 2017)

  • Te Pi, Xi Li, Zhongfei (Mark) Zhang
  • [Paper]

• BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (KDD 2017)

  • Yin Lou, Mikhail Obukhov
  • [Paper]

• CatBoost: Gradient Boosting with Categorical Features Support (NIPS 2017)

  • Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
  • [Paper]
  • [Code]

• Cost Efficient Gradient Boosting (NIPS 2017)

  • Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler
  • [Paper]
  • [Code]

• AdaGAN: Boosting Generative Models (NIPS 2017)

  • Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
  • [Paper]
  • [Code]

• LightGBM: A Highly Efficient Gradient Boosting Decision Tree (NIPS 2017)

  • Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu
  • [Paper]
  • [Code]

• Early Stopping for Kernel Boosting Algorithms: A General Analysis with Localized Complexities (NIPS 2017)

  • Yuting Wei, Fanny Yang, Martin J. Wainwright
  • [Paper]
  • [Code]

• Online Multiclass Boosting (NIPS 2017)

  • Young Hun Jung, Jack Goetz, Ambuj Tewari
  • [Paper]

• Stacking Bagged and Boosted Forests for Effective Automated Classification (SIGIR 2017)

  • Raphael R. Campos, Sérgio D. Canuto, Thiago Salles, Clebson C. A. de Sá, Marcos André Gonçalves
  • [Paper]
  • [Code]

• GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees (WWW 2017)

  • Qian Zhao, Yue Shi, Liangjie Hong
  • [Paper]
  • [Code]

2016

• Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection (AAAI 2016)

  • Chao Zhu, Yuxin Peng
  • [Paper]
  • [Code]

• Communication Efficient Distributed Agnostic Boosting (AISTATS 2016)

  • Shang-Tse Chen, Maria-Florina Balcan, Duen Horng Chau
  • [Paper]

• Logistic Boosting Regression for Label Distribution Learning (CVPR 2016)

  • Chao Xing, Xin Geng, Hui Xue
  • [Paper]

• Structured Regression Gradient Boosting (CVPR 2016)

  • Ferran Diego, Fred A. Hamprecht
  • [Paper]

• L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization (ICDM 2016)

  • Sangho Suh, Jaegul Choo, Joonseok Lee, Chandan K. Reddy
  • [Paper]
  • [Code]

• Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning (ICML 2016)

  • Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
  • [Paper]

• Generalized Dictionary for Multitask Learning with Boosting (IJCAI 2016)

  • Boyu Wang, Joelle Pineau
  • [Paper]

• Self-Paced Boost Learning for Classification (IJCAI 2016)

  • Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang
  • [Paper]

• Interactive Martingale Boosting (IJCAI 2016)

  • Ashish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan
  • [Paper]

• Optimal and Adaptive Algorithms for Online Boosting (IJCAI 2016)

  • Alina Beygelzimer, Satyen Kale, Haipeng Luo
  • [Paper]
  • [Code]

• Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (IJCAI 2016)

  • Yunzhi Tan, Min Zhang, Yiqun Liu, Shaoping Ma
  • [Paper]

• XGBoost: A Scalable Tree Boosting System (KDD 2016)

  • Tianqi Chen, Carlos Guestrin
  • [Paper]
  • [Code]

• Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments (KDD 2016)

  • Alexey Poyarkov, Alexey Drutsa, Andrey Khalyavin, Gleb Gusev, Pavel Serdyukov
  • [Paper]

• Boosting with Abstention (NIPS 2016)

  • Corinna Cortes, Giulia DeSalvo, Mehryar Mohri
  • [Paper]

• SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques (NIPS 2016)

  • Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky
  • [Paper]
  • [Code]

• Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition (NIPS 2016)

  • Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong
  • [Paper]
  • [Code]

• Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests (SIGIR 2016)

  • Clebson C. A. de Sá, Marcos André Gonçalves, Daniel Xavier de Sousa, Thiago Salles
  • [Paper]

2015

• Online Boosting Algorithms for Anytime Transfer and Multitask Learning (AAAI 2015)

  • Boyu Wang, Joelle Pineau
  • [Paper]

• A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling (AAAI 2015)

  • Chao Zhu, Yuxin Peng
  • [Paper]

• Efficient Second-Order Gradient Boosting for Conditional Random Fields (AISTATS 2015)

  • Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin
  • [Paper]

• Tumblr Blog Recommendation with Boosted Inductive Matrix Completion (CIKM 2015)

  • Donghyuk Shin, Suleyman Cetintas, Kuang-Chih Lee, Inderjit S. Dhillon
  • [Paper]

• Basis mapping based boosting for object detection (CVPR 2015)

  • Haoyu Ren, Ze-Nian Li
  • [Paper]

• Tracking-by-Segmentation with Online Gradient Boosting Decision Tree (ICCV 2015)

  • Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han
  • [Paper]
  • [Code]

• Learning to Boost Filamentary Structure Segmentation (ICCV 2015)

  • Lin Gu, Li Cheng
  • [Paper]

• Optimal and Adaptive Algorithms for Online Boosting (ICML 2015)

  • Alina Beygelzimer, Satyen Kale, Haipeng Luo
  • [Paper]
  • [Code]

• Rademacher Observations, Private Data, and Boosting (ICML 2015)

  • Richard Nock, Giorgio Patrini, Arik Friedman
  • [Paper]

• Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (ICML 2015)

  • Taehoon Lee, Sungroh Yoon
  • [Paper]

• A Direct Boosting Approach for Semi-supervised Classification (IJCAI 2015)

  • Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang
  • [Paper]

• A Boosting Algorithm for Item Recommendation with Implicit Feedback (IJCAI 2015)

  • Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao
  • [Paper]
  • [Code]

• Training-Time Optimization of a Budgeted Booster (IJCAI 2015)

  • Yi Huang, Brian Powers, Lev Reyzin
  • [Paper]

• Optimal Action Extraction for Random Forests and Boosted Trees (KDD 2015)

  • Zhicheng Cui, Wenlin Chen, Yujie He, Yixin Chen
  • [Paper]

• Online Gradient Boosting (NIPS 2015)

  • Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
  • [Paper]
  • [Code]

• BROOF: Exploiting Out-of-Bag Errors Boosting and Random Forests for Effective Automated Classification (SIGIR 2015)

  • Thiago Salles, Marcos André Gonçalves, Victor Rodrigues, Leonardo C. da Rocha
  • [Paper]

• Boosting Search with Deep Understanding of Contents and Users (WSDM 2015)

  • Kaihua Zhu
  • [Paper]

2014

• On Boosting Sparse Parities (AAAI 2014)

  • Lev Reyzin
  • [Paper]

• Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis (CVPR 2014)

  • Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen
  • [Paper]

• From Categories to Individuals in Real Time - A Unified Boosting Approach (CVPR 2014)

  • David Hall, Pietro Perona
  • [Paper]
  • [Code]

• Efficient Boosted Exemplar-Based Face Detection (CVPR 2014)

  • Haoxiang Li, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Gang Hua
  • [Paper]

• Facial Expression Recognition via a Boosted Deep Belief Network (CVPR 2014)

  • Ping Liu, Shizhong Han, Zibo Meng, Yan Tong
  • [Paper]

• Confidence-Rated Multiple Instance Boosting for Object Detection (CVPR 2014)

  • Karim Ali, Kate Saenko
  • [Paper]

• The Return of AdaBoost.MH: Multi-Class Hamming Trees (ICLR 2014)

  • Balázs Kégl
  • [Paper]
  • [Code]

• Deep Boosting (ICML 2014)

  • Corinna Cortes, Mehryar Mohri, Umar Syed
  • [Paper]
  • [Code]

• A Convergence Rate Analysis for LogitBoost, MART and Their Variant (ICML 2014)

  • Peng Sun, Tong Zhang, Jie Zhou
  • [Paper]

• Boosting with Online Binary Learners for the Multiclass Bandit Problem (ICML 2014)

  • Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu
  • [Paper]

• Boosting Multi-Step Autoregressive Forecasts (ICML 2014)

  • Souhaib Ben Taieb, Rob J. Hyndman
  • [Paper]

• Dynamic Programming Boosting for Discriminative Macro-Action Discovery (ICML 2014)

  • Leonidas Lefakis, François Fleuret
  • [Paper]

• Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (ICML 2014)

  • Oscar Beijbom, Mohammad J. Saberian, David J. Kriegman, Nuno Vasconcelos
  • [Paper]

• A Multi-Class Boosting Method with Direct Optimization (KDD 2014)

  • Shaodan Zhai, Tian Xia, Shaojun Wang
  • [Paper]

• Gradient Boosted Feature Selection (KDD 2014)

  • Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng
  • [Paper]
  • [Code]

• Multi-Class Deep Boosting (NIPS 2014)

  • Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
  • [Paper]

• Deconvolution of High Dimensional Mixtures via Boosting with Application to Diffusion-Weighted MRI of Human Brain (NIPS 2014)

  • Charles Y. Zheng, Franco Pestilli, Ariel Rokem
  • [Paper]

• A Drifting-Games Analysis for Online Learning and Applications to Boosting (NIPS 2014)

  • Haipeng Luo, Robert E. Schapire
  • [Paper]

• A Boosting Framework on Grounds of Online Learning (NIPS 2014)

  • Tofigh Naghibi Mohamadpoor, Beat Pfister
  • [Paper]

• Gradient Boosting Factorization Machines (RECSYS 2014)

  • Chen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu
  • [Paper]

2013

• Boosting Binary Keypoint Descriptors (CVPR 2013)

  • Tomasz Trzcinski, C. Mario Christoudias, Pascal Fua, Vincent Lepetit
  • [Paper]
  • [Code]

• PerturBoost: Practical Confidential Classifier Learning in the Cloud (ICDM 2013)

  • Keke Chen, Shumin Guo
  • [Paper]

• Multiclass Semi-Supervised Boosting Using Similarity Learning (ICDM 2013)

  • Jafar Tanha, Mohammad Javad Saberian, Maarten van Someren
  • [Paper]

• Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner (ICML 2013)

  • Peng Sun, Jie Zhou
  • [Paper]

• General Functional Matrix Factorization Using Gradient Boosting (ICML 2013)

  • Tianqi Chen, Hang Li, Qiang Yang, Yong Yu
  • [Paper]

• Margins, Shrinkage, and Boosting (ICML 2013)

  • Matus Telgarsky
  • [Paper]

• Quickly Boosting Decision Trees - Pruning Underachieving Features Early (ICML 2013)

  • Ron Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona
  • [Paper]
  • [Code]

• Human Boosting (ICML 2013)

  • Harsh H. Pareek, Pradeep Ravikumar
  • [Paper]

• Collaborative Boosting for Activity Classification in Microblogs (KDD 2013)

  • Yangqiu Song, Zhengdong Lu, Cane Wing-ki Leung, Qiang Yang
  • [Paper]

• Direct 0-1 Loss Minimization and Margin Maximization with Boosting (NIPS 2013)

  • Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
  • [Paper]

• Reservoir Boosting : Between Online and Offline Ensemble Learning (NIPS 2013)

  • Leonidas Lefakis, François Fleuret
  • [Paper]

• Non-Linear Domain Adaptation with Boosting (NIPS 2013)

  • Carlos J. Becker, C. Mario Christoudias, Pascal Fua
  • [Paper]

• Boosting in the Presence of Label Noise (UAI 2013)

  • Jakramate Bootkrajang, Ata Kabán
  • [Paper]

2012

• Contextual Boost for Pedestrian Detection (CVPR 2012)

  • Yuanyuan Ding, Jing Xiao
  • [Paper]

• Shrink Boost for Selecting Multi-LBP Histogram Features in Object Detection (CVPR 2012)

  • Cher Keng Heng, Sumio Yokomitsu, Yuichi Matsumoto, Hajime Tamura
  • [Paper]

• Boosting Bottom-Up and Top-Down Visual Features for Saliency Estimation (CVPR 2012)

  • Ali Borji
  • [Paper]

• Boosting Algorithms for Simultaneous Feature Extraction and Selection (CVPR 2012)

  • Mohammad J. Saberian, Nuno Vasconcelos
  • [Paper]

• Sharing Features in Multi-class Boosting via Group Sparsity (CVPR 2012)

  • Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
  • [Paper]

• Feature Weighting and Selection Using Hypothesis Margin of Boosting (ICDM 2012)

  • Malak Alshawabkeh, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli
  • [Paper]

• An AdaBoost Algorithm for Multiclass Semi-supervised Learning (ICDM 2012)

  • Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh
  • [[Paper]]https://ieeexplore.ieee.org/document/6413799)

• AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (ICML 2012)

  • Peng Sun, Mark D. Reid, Jie Zhou
  • [[Paper]](AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem)
  • [Code]

• An Online Boosting Algorithm with Theoretical Justifications (ICML 2012)

  • Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu
  • [Paper]

• Learning Image Descriptors with the Boosting-Trick (NIPS 2012)

  • Tomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua
  • [Paper]
  • [Code]

• Accelerated Training for Matrix-norm Regularization: A Boosting Approach (NIPS 2012)

  • Xinhua Zhang, Yaoliang Yu, Dale Schuurmans
  • [Paper]

• Learning from Heterogeneous Sources via Gradient Boosting Consensus (SDM 2012)

  • Xiaoxiao Shi, Jean-François Paiement, David Grangier, Philip S. Yu
  • [Paper]
  • [Code]

2011

• Selective Transfer Between Learning Tasks Using Task-Based Boosting (AAAI 2011)

  • Eric Eaton, Marie desJardins
  • [Paper]

• Incorporating Boosted Regression Trees into Ecological Latent Variable Models (AAAI 2011)

  • Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
  • [Paper]

• FlowBoost - Appearance Learning from Sparsely Annotated Video (CVPR 2011)

  • Karim Ali, David Hasler, François Fleuret
  • [Paper]

• AdaBoost on Low-Rank PSD Matrices for Metric Learning (CVPR 2011)

  • Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Matthias Wolf
  • [Paper]

• Boosted Local Structured HOG-LBP for Object Localization (CVPR 2011)

  • Junge Zhang, Kaiqi Huang, Yinan Yu, Tieniu Tan
  • [Paper]

• A Direct Formulation for Totally-Corrective Multi-Class Boosting (CVPR 2011)

  • Chunhua Shen, Zhihui Hao
  • [Paper]

• Gated Classifiers: Boosting Under High Intra-class Variation (CVPR 2011)

  • Oscar M. Danielsson, Babak Rasolzadeh, Stefan Carlsson
  • [Paper]

• TaylorBoost: First and Second-order Boosting Algorithms with Explicit Margin Control (CVPR 2011)

  • Mohammad J. Saberian, Hamed Masnadi-Shirazi, Nuno Vasconcelos
  • [Paper]
  • [Code]

• Robust and Efficient Regularized Boosting Using Total Bregman Divergence (CVPR 2011)

  • Meizhu Liu, Baba C. Vemuri
  • [Paper]

• Treat Samples differently: Object Tracking with Semi-Supervised Online CovBoost (ICCV 2011)

  • Guorong Li, Lei Qin, Qingming Huang, Junbiao Pang, Shuqiang Jiang
  • [Paper]

• LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction (ICDM 2011)

  • Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain
  • [Paper]

• Learning Markov Logic Networks via Functional Gradient Boosting (ICDM 2011)

  • Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik
  • [Paper]
  • [Code]

• Boosting on a Budget: Sampling for Feature-Efficient Prediction (ICML 2011)

  • Lev Reyzin
  • [Paper]

• Multiclass Boosting with Hinge Loss based on Output Coding (ICML 2011)

  • Tianshi Gao, Daphne Koller
  • [Paper]
  • [Code]

• Generalized Boosting Algorithms for Convex Optimization (ICML 2011)

  • Alexander Grubb, Drew Bagnell
  • [Paper]

• Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (IJCAI 2011)

  • Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
  • [Paper]

• Boosting with Maximum Adaptive Sampling (NIPS 2011)

  • Charles Dubout, François Fleuret
  • [Paper]

• The Fast Convergence of Boosting (NIPS 2011)

  • Matus Telgarsky
  • [Paper]

• ShareBoost: Efficient Multiclass Learning with Feature Sharing (NIPS 2011)

  • Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua
  • [Paper]

• Multiclass Boosting: Theory and Algorithms (NIPS 2011)

  • Mohammad J. Saberian, Nuno Vasconcelos
  • [Paper]

• Variance Penalizing AdaBoost (NIPS 2011)

  • Pannagadatta K. Shivaswamy, Tony Jebara
  • [Paper]

• MKBoost: A Framework of Multiple Kernel Boosting (SDM 2011)

  • Hao Xia, Steven C. H. Hoi
  • [Paper]

• A Boosting Approach to Improving Pseudo-Relevance Feedback (SIGIR 2011)

  • Yuanhua Lv, ChengXiang Zhai, Wan Chen
  • [Paper]

• Bagging Gradient-Boosted Trees for High Precision, Low Variance Ranking Models (SIGIR 2011)

  • Yasser Ganjisaffar, Rich Caruana, Cristina Videira Lopes
  • [Paper]

• Boosting as a Product of Experts (UAI 2011)

  • Narayanan Unny Edakunni, Gary Brown, Tim Kovacs
  • [Paper]

• Parallel Boosted Regression Trees for Web Search Ranking (WWW 2011)

  • Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin
  • [Paper]
  • [Code]

2010

• The Boosting Effect of Exploratory Behaviors (AAAI 2010)

  • Jivko Sinapov, Alexander Stoytchev
  • [Paper]

• Boosting-Based System Combination for Machine Translation (ACL 2010)

  • Tong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang
  • [Paper]

• BagBoo: A Scalable Hybrid Bagging-the-Boosting Model (CIKM 2010)

  • Dmitry Yurievich Pavlov, Alexey Gorodilov, Cliff A. Brunk
  • [Paper]
  • [Code]

• Automatic Detection of Craters in Planetary Images: an Embedded Framework Using Feature Selection and Boosting (CIKM 2010)

  • Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao
  • [Paper]

• Facial Point Detection Using Boosted Regression and Graph Models (CVPR 2010)

  • Michel François Valstar, Brais Martínez, Xavier Binefa, Maja Pantic
  • [Paper]

• Boosting for Transfer Learning with Multiple Sources (CVPR 2010)

  • Yi Yao, Gianfranco Doretto
  • [Paper]

• Efficient Rotation Invariant Object Detection Using Boosted Random Ferns (CVPR 2010)

  • Michael Villamizar, Francesc Moreno-Noguer, Juan Andrade-Cetto, Alberto Sanfeliu
  • [Paper]

• Implicit Hierarchical Boosting for Multi-view Object Detection (CVPR 2010)

  • Xavier Perrotton, Marc Sturzel, Michel Roux
  • [Paper]

• On-Line Semi-Supervised Multiple-Instance Boosting (CVPR 2010)

  • Bernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof
  • [Paper]

• Online Multi-Class LPBoost (CVPR 2010)

  • Amir Saffari, Martin Godec, Thomas Pock, Christian Leistner, Horst Bischof
  • [Paper]
  • [Code]

• Homotopy Regularization for Boosting (ICDM 2010)

  • Zheng Wang, Yangqiu Song, Changshui Zhang
  • [Paper]

• Exploiting Local Data Uncertainty to Boost Global Outlier Detection (ICDM 2010)

  • Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu
  • [Paper]

• Boosting Classifiers with Tightened L0-Relaxation Penalties (ICML 2010)

  • Noam Goldberg, Jonathan Eckstein
  • [Paper]

• Boosting for Regression Transfer (ICML 2010)

  • David Pardoe, Peter Stone
  • [Paper]
  • [Code]

• Boosted Backpropagation Learning for Training Deep Modular Networks (ICML 2010)

  • Alexander Grubb, J. Andrew Bagnell
  • [Paper]

• Fast Boosting Using Adversarial Bandits (ICML 2010)

  • Róbert Busa-Fekete, Balázs Kégl
  • [Paper]

• Boosting with Structure Information in the Functional Space: an Application to Graph Classification (KDD 2010)

  • Hongliang Fei, Jun Huan
  • [Paper]

• Multi-task Learning for Boosting with Application to Web Search Ranking (KDD 2010)

  • Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng
  • [Paper]

• A Theory of Multiclass Boosting (NIPS 2010)

  • Indraneel Mukherjee, Robert E. Schapire
  • [Paper]

• Boosting Classifier Cascades (NIPS 2010)

  • Mohammad J. Saberian, Nuno Vasconcelos
  • [Paper]

• Joint Cascade Optimization Using A Product Of Boosted Classifiers (NIPS 2010)

  • Leonidas Lefakis, François Fleuret
  • [Paper]

• Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost (UAI 2010)

  • Ping Li
  • [Paper]
  • [Code]

2009

• Feature Selection for Ranking Using Boosted Trees (CIKM 2009)

  • Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato
  • [Paper]

• Boosting KNN Text Classification Accuracy by Using Supervised Term Weighting Schemes (CIKM 2009)

  • Iyad Batal, Milos Hauskrecht
  • [Paper]

• Stochastic Gradient Boosted Distributed Decision Trees (CIKM 2009)

  • Jerry Ye, Jyh-Herng Chow, Jiang Chen, Zhaohui Zheng
  • [Paper]

• A General Magnitude-Preserving Boosting Algorithm for Search Ranking (CIKM 2009)

  • Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen
  • [Paper]

• Reducing Joint Boost-Based Multiclass Classification to Proximity Search (CVPR 2009)

  • Alexandra Stefan, Vassilis Athitsos, Quan Yuan, Stan Sclaroff
  • [Paper]

• Imbalanced RankBoost for Efficiently Ranking Large-Scale Image-Video Collections (CVPR 2009)

  • Michele Merler, Rong Yan, John R. Smith
  • [Paper]

• Regularized Multi-Class Semi-Supervised Boosting (CVPR 2009)

  • Amir Saffari, Christian Leistner, Horst Bischof
  • [Paper]

• Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene (CVPR 2009)

  • Yuan Li, Chang Huang, Ram Nevatia
  • [Paper]

• Boosted Multi-task Learning for Face Verification with Applications to Web Image and Video Search (CVPR 2009)

  • Xiaogang Wang, Cha Zhang, Zhengyou Zhang
  • [Paper]

• LidarBoost: Depth Superresolution for ToF 3D Shape Scanning (CVPR 2009)

  • Sebastian Schuon, Christian Theobalt, James E. Davis, Sebastian Thrun
  • [Paper]

• Model Adaptation via Model Interpolation and Boosting for Web Search Ranking (EMNLP 2009)

  • Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie Svore, Yi Su, Nazan Khan, Shalin Shah, Hongyan Zhou
  • [Paper]

• Finding Shareable Informative Patterns and Optimal Coding Matrix for Multiclass Boosting (ICCV 2009)

  • Bang Zhang, Getian Ye, Yang Wang, Jie Xu, Gunawan Herman
  • [Paper]

• RankBoost with L1 Regularization for Facial Expression Recognition and Intensity Estimation (ICCV 2009)

  • Peng Yang, Qingshan Liu, Dimitris N. Metaxas
  • [Paper]

• A Robust Boosting Tracker with Minimum Error Bound in a Co-Training Framework (ICCV 2009)

  • Rong Liu, Jian Cheng, Hanqing Lu
  • [Paper]

• Tutorial Summary: Survey of Boosting from an Optimization Perspective (ICML 2009)

  • Manfred K. Warmuth, S. V. N. Vishwanathan
  • [Paper]

• Boosting Products of Base Classifiers (ICML 2009)

  • Balázs Kégl, Róbert Busa-Fekete
  • [Paper]

• ABC-boost: Adaptive Base Class Boost for Multi-Class Classification (ICML 2009)

  • Ping Li
  • [Paper]

• Boosting with Structural Sparsity (ICML 2009)

  • John C. Duchi, Yoram Singer
  • [Paper]

• Boosting Constrained Mutual Subspace Method for Robust Image-Set Based Object Recognition (IJCAI 2009)

  • Xi Li, Kazuhiro Fukui, Nanning Zheng
  • [Paper]

• Information Theoretic Regularization for Semi-supervised Boosting (KDD 2009)

  • Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
  • [Paper]

• Potential-Based Agnostic Boosting (NIPS 2009)

  • Adam Kalai, Varun Kanade
  • [Paper]

• Positive Semidefinite Metric Learning with Boosting (NIPS 2009)

  • Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel
  • [Paper]

• Boosting with Spatial Regularization (NIPS 2009)

  • Zhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter J. Ramadge
  • [Paper]

• Effective Boosting of Na%C3%AFve Bayesian Classifiers by Local Accuracy Estimation (PAKDD 2009)

  • Zhipeng Xie
  • [Paper]

• Multi-resolution Boosting for Classification and Regression Problems (PAKDD 2009)

  • Chandan K. Reddy, Jin Hyeong Park
  • [Paper]

• Efficient Active Learning with Boosting (SDM 2009)

  • Zheng Wang, Yangqiu Song, Changshui Zhang
  • [Paper]

2008

• Group-Based Learning: A Boosting Approach (CIKM 2008)

  • Weijian Ni, Jun Xu, Hang Li, Yalou Huang
  • [Paper]

• Semi-Supervised Boosting Using Visual Similarity Learning (CVPR 2008)

  • Christian Leistner, Helmut Grabner, Horst Bischof
  • [Paper]

• Mining Compositional Features for Boosting (CVPR 2008)

  • Junsong Yuan, Jiebo Luo, Ying Wu
  • [Paper]

• Boosted Deformable Model for Human Body Alignment (CVPR 2008)

  • Xiaoming Liu, Ting Yu, Thomas Sebastian, Peter H. Tu
  • [Paper]

• Discriminative Modeling by Boosting on Multilevel Aggregates (CVPR 2008)

  • Jason J. Corso
  • [Paper]

• Face Alignment via Boosted Ranking Model (CVPR 2008)

  • Hao Wu, Xiaoming Liu, Gianfranco Doretto
  • [Paper]

• Boosting Adaptive Linear Weak Classifiers for Online Learning and Tracking (CVPR 2008)

  • Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal
  • [Paper]

• Detection with Multi-Exit Asymmetric Boosting (CVPR 2008)

  • Minh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham
  • [Paper]

• Boosting Ordinal Features for Accurate and Fast Iris Recognition (CVPR 2008)

  • Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong
  • [Paper]

• Adaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting (CVPR 2008)

  • Cheng Chen, Yueting Zhuang, Jun Xiao, Fei Wu
  • [Paper]

• Boosting Relational Sequence Alignments (ICDM 2008)

  • Andreas Karwath, Kristian Kersting, Niels Landwehr
  • [Paper]

• Boosting with Incomplete Information (ICML 2008)

  • Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori, Feng Jiao
  • [Paper]

• ManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning (ICML 2008)

  • Nicolas Loeff, David A. Forsyth, Deepak Ramachandran
  • [Paper]

• Random Classification Noise Defeats All Convex Potential Boosters (ICML 2008)

  • Philip M. Long, Rocco A. Servedio
  • [Paper]

• Multi-class Cost-Sensitive Boosting with P-norm Loss Functions (KDD 2008)

  • Aurelie C. Lozano, Naoki Abe
  • [Paper]

• MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features (NIPS 2008)

  • Tae-Kyun Kim, Roberto Cipolla
  • [Paper]

• PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning (NIPS 2008)

  • Chunhua Shen, Alan Welsh, Lei Wang
  • [Paper]

• On the Design of Loss Functions for Classification: Theory, Robustness to Outliers, and SavageBoost (NIPS 2008)

  • Hamed Masnadi-Shirazi, Nuno Vasconcelos
  • [Paper]

• Adaptive Martingale Boosting (NIPS 2008)

  • Philip M. Long, Rocco A. Servedio
  • [Paper]

• A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data (SIGIR 2008)

  • Massih-Reza Amini, Tuong-Vinh Truong, Cyril Goutte
  • [Paper]

2007

• Using Error-Correcting Output Codes with Model-Refinement to Boost Centroid Text Classifier (ACL 2007)

  • Songbo Tan
  • [Paper]

• Fast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression (CVPR 2007)

  • Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto
  • [Paper]

• Generic Face Alignment using Boosted Appearance Model (CVPR 2007)

  • Xiaoming Liu
  • [Paper]

• Eigenboosting: Combining Discriminative and Generative Information (CVPR 2007)

  • Helmut Grabner, Peter M. Roth, Horst Bischof
  • [Paper]

• Online Learning Asymmetric Boosted Classifiers for Object Detection (CVPR 2007)

  • Minh-Tri Pham, Tat-Jen Cham
  • [Paper]

• Improving Part based Object Detection by Unsupervised Online Boosting (CVPR 2007)

  • Bo Wu, Ram Nevatia
  • [Paper]

• A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features (CVPR 2007)

  • Masayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, Ryusuke Miyamoto
  • [Paper]

• Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier (CVPR 2007)

  • Bo Wu, Ram Nevatia
  • [Paper]

• Compositional Boosting for Computing Hierarchical Image Structures (CVPR 2007)

  • Tianfu Wu, Gui-Song Xia, Song Chun Zhu
  • [Paper]

• Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition (CVPR 2007)

  • Peng Yang, Qingshan Liu, Dimitris N. Metaxas
  • [Paper]

• Object Classification in Visual Surveillance Using Adaboost (CVPR 2007)

  • John-Paul Renno, Dimitrios Makris, Graeme A. Jones
  • [Paper]

• A Boosting Regression Approach to Medical Anatomy Detection (CVPR 2007)

  • Shaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu
  • [Paper]

• Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network (CVPR 2007)

  • Jingdan Zhang, Shaohua Kevin Zhou, Leonard McMillan, Dorin Comaniciu
  • [Paper]

• Kernel Sharing With Joint Boosting For Multi-Class Concept Detection (CVPR 2007)

  • Wei Jiang, Shih-Fu Chang, Alexander C. Loui
  • [Paper]

• Scale-Space Based Weak Regressors for Boosting (ECML 2007)

  • Jin Hyeong Park, Chandan K. Reddy
  • [Paper]

• Avoiding Boosting Overfitting by Removing Confusing Samples (ECML 2007)

  • Alexander Vezhnevets, Olga Barinova
  • [Paper]

• DynamicBoost: Boosting Time Series Generated by Dynamical Systems (ICCV 2007)

  • René Vidal, Paolo Favaro
  • [Paper]

• Incremental Learning of Boosted Face Detector (ICCV 2007)

  • Chang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade
  • [Paper]

• Gradient Feature Selection for Online Boosting (ICCV 2007)

  • Xiaoming Liu, Ting Yu
  • [Paper]

• Fast Training and Selection of Haar Features Using Statistics in Boosting-based Face Detection (ICCV 2007)

  • Minh-Tri Pham, Tat-Jen Cham
  • [Paper]

• Cluster Boosted Tree Classifier for Multi-View - Multi-Pose Object Detection (ICCV 2007)

  • Bo Wu, Ramakant Nevatia
  • [Paper]

• Asymmetric Boosting (ICML 2007)

  • Hamed Masnadi-Shirazi, Nuno Vasconcelos
  • [Paper]

• Boosting for Transfer Learning (ICML 2007)

  • Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu
  • [Paper]

• Gradient Boosting for Kernelized Output Spaces (ICML 2007)

  • Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
  • [Paper]

• Boosting a Complete Technique to Find MSS and MUS Thanks to a Local Search Oracle (IJCAI 2007)

  • Éric Grégoire, Bertrand Mazure, Cédric Piette
  • [Paper]

• Training Conditional Random Fields Using Virtual Evidence Boosting (IJCAI 2007)

  • Lin Liao, Tanzeem Choudhury, Dieter Fox, Henry A. Kautz
  • [Paper]

• Simple Training of Dependency Parsers via Structured Boosting (IJCAI 2007)

  • Qin Iris Wang, Dekang Lin, Dale Schuurmans
  • [Paper]

• Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree (IJCAI 2007)

  • Claudia Henry, Richard Nock, Frank Nielsen
  • [Paper]

• Managing Domain Knowledge and Multiple Models with Boosting (IJCAI 2007)

  • Peng Zang, Charles Lee Isbell Jr.
  • [Paper]

• Model-Shared Subspace Boosting for Multi-label Classification (KDD 2007)

  • Rong Yan, Jelena Tesic, John R. Smith
  • [Paper]

• Regularized Boost for Semi-Supervised Learning (NIPS 2007)

  • Ke Chen, Shihai Wang
  • [Paper]

• Boosting Algorithms for Maximizing the Soft Margin (NIPS 2007)

  • Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch
  • [Paper]

• McRank: Learning to Rank Using Multiple Classification and Gradient Boosting (NIPS 2007)

  • Ping Li, Christopher J. C. Burges, Qiang Wu
  • [Paper]

• One-Pass Boosting (NIPS 2007)

  • Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio
  • [Paper]

• Boosting the Area under the ROC Curve (NIPS 2007)

  • Philip M. Long, Rocco A. Servedio
  • [Paper]

• FilterBoost: Regression and Classification on Large Datasets (NIPS 2007)

  • Joseph K. Bradley, Robert E. Schapire
  • [Paper]

• A General Boosting Method and its Application to Learning Ranking Functions for Web Search (NIPS 2007)

  • Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun
  • [Paper]

• Efficient Multiclass Boosting Classification with Active Learning (SDM 2007)

  • Jian Huang, Seyda Ertekin, Yang Song, Hongyuan Zha, C. Lee Giles
  • [Paper]

• AdaRank: a Boosting Algorithm for Information Retrieval (SIGIR 2007)

  • Jun Xu, Hang Li
  • [Paper]

2006

• Gradient Boosting for Sequence Alignment (AAAI 2006)

  • Charles Parker, Alan Fern, Prasad Tadepalli
  • [Paper]

• Boosting Kernel Models for Regression (ICDM 2006)

  • Ping Sun, Xin Yao
  • [Paper]

• Boosting for Learning Multiple Classes with Imbalanced Class Distribution (ICDM 2006)

  • Yanmin Sun, Mohamed S. Kamel, Yang Wang
  • [Paper]

• Boosting the Feature Space: Text Classification for Unstructured Data on the Web (ICDM 2006)

  • Yang Song, Ding Zhou, Jian Huang, Isaac G. Councill, Hongyuan Zha, C. Lee Giles
  • [Paper]

• Totally Corrective Boosting Algorithms that Maximize the Margin (ICML 2006)

  • Manfred K. Warmuth, Jun Liao, Gunnar Rätsch
  • [Paper]

• How Boosting the Margin Can Also Boost Classifier Complexity (ICML 2006)

  • Lev Reyzin, Robert E. Schapire
  • [Paper]

• Multiclass Boosting with Repartitioning (ICML 2006)

  • Ling Li
  • [Paper]

• AdaBoost is Consistent (NIPS 2006)

  • Peter L. Bartlett, Mikhail Traskin
  • [Paper]

• Boosting Structured Prediction for Imitation Learning (NIPS 2006)

  • Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt
  • [Paper]

• Chained Boosting (NIPS 2006)

  • Christian R. Shelton, Wesley Huie, Kin Fai Kan
  • [Paper]

• When Efficient Model Averaging Out-Performs Boosting and Bagging (PKDD 2006)

  • Ian Davidson, Wei Fan
  • [Paper]

2005

• Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting (AAAI 2005)

  • Axel Rottmann, Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard
  • [Paper]

• Boosting-based Parse Reranking with Subtree Features (ACL 2005)

  • Taku Kudo, Jun Suzuki, Hideki Isozaki
  • [Paper]

• Using RankBoost to Compare Retrieval Systems (CIKM 2005)

  • Huyen-Trang Vu, Patrick Gallinari
  • [Paper]

• Classifier Fusion Using Shared Sampling Distribution for Boosting (ICDM 2005)

  • Costin Barbu, Raja Tanveer Iqbal, Jing Peng
  • [Paper]

• Semi-Supervised Mixture of Kernels via LPBoost Methods (ICDM 2005)

  • Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
  • [Paper]

• Efficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes (ICML 2005)

  • Yushi Jing, Vladimir Pavlovic, James M. Rehg
  • [Paper]

• Unifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework (ICML 2005)

  • Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu
  • [Paper]

• A Smoothed Boosting Algorithm Using Probabilistic Output Codes (ICML 2005)

  • Rong Jin, Jian Zhang
  • [Paper]

• Robust Boosting and its Relation to Bagging (KDD 2005)

  • Saharon Rosset
  • [Paper]

• Efficient Computations via Scalable Sparse Kernel Partial Least Squares and Boosted Latent Features (KDD 2005)

  • Michinari Momma
  • [Paper]

• Multiple Instance Boosting for Object Detection (NIPS 2005)

  • Paul A. Viola, John C. Platt, Cha Zhang
  • [Paper]

• Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations (NIPS 2005)

  • Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire
  • [Paper]

• Boosted decision trees for word recognition in handwritten document retrieval (SIGIR 2005)

  • Nicholas R. Howe, Toni M. Rath, R. Manmatha
  • [Paper]

• Obtaining Calibrated Probabilities from Boosting (UAI 2005)

  • Alexandru Niculescu-Mizil, Rich Caruana
  • [Paper]

2004

• Online Parallel Boosting (AAAI 2004)

  • Jesse A. Reichler, Harlan D. Harris, Michael A. Savchenko
  • [Paper]

• A Boosting Approach to Multiple Instance Learning (ECML 2004)

  • Peter Auer, Ronald Ortner
  • [Paper]

• A Boosting Algorithm for Classification of Semi-Structured Text (EMNLP 2004)

  • Taku Kudo, Yuji Matsumoto
  • [Paper]

• Text Classification by Boosting Weak Learners based on Terms and Concepts (ICDM 2004)

  • Stephan Bloehdorn, Andreas Hotho
  • [Paper]

• Boosting Grammatical Inference with Confidence Oracles (ICML 2004)

  • Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier
  • [Paper]

• Surrogate Maximization/Minimization Algorithms for AdaBoost and the Logistic Regression Model (ICML 2004)

  • Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
  • [Paper]

• Training Conditional Random Fields via Gradient Tree Boosting (ICML 2004)

  • Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov
  • [Paper]

• Boosting Margin Based Distance Functions for Clustering (ICML 2004)

  • Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
  • [Paper]

• Column-Generation Boosting Methods for Mixture of Kernels (KDD 2004)

  • Jinbo Bi, Tong Zhang, Kristin P. Bennett
  • [Paper]

• Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging (NIPS 2004)

  • Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse
  • [Paper]

• Boosting on Manifolds: Adaptive Regularization of Base Classifiers (NIPS 2004)

  • Balázs Kégl, Ligen Wang
  • [Paper]

• Contextual Models for Object Detection Using Boosted Random Fields (NIPS 2004)

  • Antonio Torralba, Kevin P. Murphy, William T. Freeman
  • [Paper]

• Generalization Error and Algorithmic Convergence of Median Boosting (NIPS 2004)

  • Balázs Kégl
  • [Paper]

• An Application of Boosting to Graph Classification (NIPS 2004)

  • Taku Kudo, Eisaku Maeda, Yuji Matsumoto
  • [Paper]

• Logistic Regression and Boosting for Labeled Bags of Instances (PAKDD 2004)

  • Xin Xu, Eibe Frank
  • [Paper]

• Fast and Light Boosting for Adaptive Mining of Data Streams (PAKDD 2004)

  • Fang Chu, Carlo Zaniolo
  • [Paper]

2003

• On Boosting and the Exponential Loss (AISTATS 2003)

  • Abraham J. Wyner
  • [Paper]

• Boosting Support Vector Machines for Text Classification through Parameter-Free Threshold Relaxation (CIKM 2003)

  • James G. Shanahan, Norbert Roma
  • [Paper]

• Learning Cross-Document Structural Relationships Using Boosting (CIKM 2003)

  • Zhu Zhang, Jahna Otterbacher, Dragomir R. Radev
  • [Paper]

• On Boosting Improvement: Error Reduction and Convergence Speed-Up (ECML 2003)

  • Marc Sebban, Henri-Maxime Suchier
  • [Paper]

• Boosting Lazy Decision Trees (ICML 2003)

  • Xiaoli Zhang Fern, Carla E. Brodley
  • [Paper]

• On the Convergence of Boosting Procedures (ICML 2003)

  • Tong Zhang, Bin Yu
  • [Paper]

• Linear Programming Boosting for Uneven Datasets (ICML 2003)

  • Jure Leskovec, John Shawe-Taylor
  • [Paper]

• Monte Carlo Theory as an Explanation of Bagging and Boosting (IJCAI 2003)

  • Roberto Esposito, Lorenza Saitta
  • [Paper]

• On the Dynamics of Boosting (NIPS 2003)

  • Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire
  • [Paper]

• Mutual Boosting for Contextual Inference (NIPS 2003)

  • Michael Fink, Pietro Perona
  • [Paper]

• Boosting Versus Covering (NIPS 2003)

  • Kohei Hatano, Manfred K. Warmuth
  • [Paper]

• Multiple-Instance Learning via Disjunctive Programming Boosting (NIPS 2003)

  • Stuart Andrews, Thomas Hofmann
  • [Paper]

• Averaged Boosting: A Noise-Robust Ensemble Method (PAKDD 2003)

  • Yongdai Kim
  • [Paper]

• SMOTEBoost: Improving Prediction of the Minority Class in Boosting (PKDD 2003)

  • Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer
  • [Paper]

2002

• Minimum Majority Classification and Boosting (AAAI 2002)

  • Philip M. Long
  • [Paper]

• Ranking Algorithms for Named Entity Extraction: Boosting and the Voted Perceptron (ACL 2002)

  • Michael Collins
  • [Paper]

• Boosting to Correct Inductive Bias in Text Classification (CIKM 2002)

  • Yan Liu, Yiming Yang, Jaime G. Carbonell
  • [Paper]

• How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code (ECML 2002)

  • Günther Eibl, Karl Peter Pfeiffer
  • [Paper]

• Scaling Boosting by Margin-Based Inclusionof Features and Relations (ECML 2002)

  • Susanne Hoche, Stefan Wrobel
  • [Paper]

• A Robust Boosting Algorithm (ECML 2002)

  • Richard Nock, Patrice Lefaucheur
  • [Paper]

• iBoost: Boosting Using an instance-Based Exponential Weighting Scheme (ECML 2002)

  • Stephen Kwek, Chau Nguyen
  • [Paper]

• Boosting Density Function Estimators (ECML 2002)

  • Franck Thollard, Marc Sebban, Philippe Ézéquel
  • [Paper]

• Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (ICML 2002)

  • Tong Zhang
  • [Paper]

• A Boosted Maximum Entropy Model for Learning Text Chunking (ICML 2002)

  • Seong-Bae Park, Byoung-Tak Zhang
  • [Paper]

• Towards Large Margin Speech Recognizers by Boosting and Discriminative Training (ICML 2002)

  • Carsten Meyer, Peter Beyerlein
  • [Paper]

• Incorporating Prior Knowledge into Boosting (ICML 2002)

  • Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta
  • [Paper]

• Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (ICML 2002)

  • Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik
  • [Paper]

• MARK: A Boosting Algorithm for Heterogeneous Kernel Models (KDD 2002)

  • Kristin P. Bennett, Michinari Momma, Mark J. Embrechts
  • [Paper]

• Predicting rare classes: can boosting make any weak learner strong (KDD 2002)

  • Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar
  • [Paper]

• Kernel Design Using Boosting (NIPS 2002)

  • Koby Crammer, Joseph Keshet, Yoram Singer
  • [Paper]

• FloatBoost Learning for Classification (NIPS 2002)

  • Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang
  • [Paper]

• Discriminative Learning for Label Sequences via Boosting (NIPS 2002)

  • Yasemin Altun, Thomas Hofmann, Mark Johnson
  • [Paper]

• Boosting Density Estimation (NIPS 2002)

  • Saharon Rosset, Eran Segal
  • [Paper]

• Self Supervised Boosting (NIPS 2002)

  • Max Welling, Richard S. Zemel, Geoffrey E. Hinton
  • [Paper]

• Boosted Dyadic Kernel Discriminants (NIPS 2002)

  • Baback Moghaddam, Gregory Shakhnarovich
  • [Paper]

• A Method to Boost Support Vector Machines (PAKDD 2002)

  • Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi
  • [Paper]

• A Method to Boost Naive Bayesian Classifiers (PAKDD 2002)

  • Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi
  • [Paper]

• Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting (PKDD 2002)

  • Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar
  • [Paper]

• Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance (PKDD 2002)

  • Yuta Choki, Einoshin Suzuki
  • [Paper]

• Staged Mixture Modelling and Boosting (UAI 2002)

  • Christopher Meek, Bo Thiesson, David Heckerman
  • [Paper]

• Advances in Boosting (UAI 2002)

  • Robert E. Schapire
  • [Paper]

2001

• Is Regularization Unnecessary for Boosting? (AISTATS 2001)

  • Wenxin Jiang
  • [Paper]

• Online Bagging and Boosting (AISTATS 2001)

  • Nikunj C. Oza, Stuart J. Russell
  • [Paper]

• Text Categorization Using Transductive Boosting (ECML 2001)

  • Hirotoshi Taira, Masahiko Haruno
  • [Paper]

• Improving Term Extraction by System Combination Using Boosting (ECML 2001)

  • Jordi Vivaldi, Lluís Màrquez, Horacio Rodríguez
  • [Paper]

• Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example (ECML 2001)

  • Günther Eibl, Karl Peter Pfeiffer
  • [Paper]

• On the Practice of Branching Program Boosting (ECML 2001)

  • Tapio Elomaa, Matti Kääriäinen
  • [Paper]

• Boosting Mixture Models for Semi-supervised Learning (ICANN 2001)

  • Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise
  • [[Paper]](https://link.springer.com/chapter/10.1007/3-540-44668-0_7

• A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods (ICDM 2001)

  • Bernard Zenko, Ljupco Todorovski, Saso Dzeroski
  • [Paper]

• Using Boosting to Simplify Classification Models (ICDM 2001)

  • Virginia Wheway
  • [Paper]

• Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements (ICDM 2001)

  • Mahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal
  • [Paper]
  • [Code]

• Boosting Neighborhood-Based Classifiers (ICML 2001)

  • Marc Sebban, Richard Nock, Stéphane Lallich
  • [Paper]

• Boosting Noisy Data (ICML 2001)

  • Abba Krieger, Chuan Long, Abraham J. Wyner
  • [Paper]

• Some Theoretical Aspects of Boosting in the Presence of Noisy Data (ICML 2001)

  • Wenxin Jiang
  • [Paper]

• Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (ICML 2001)

  • Sanmay Das
  • [Paper]

• The Distributed Boosting Algorithm (KDD 2001)

  • Aleksandar Lazarevic, Zoran Obradovic
  • [Paper]

• Experimental Comparisons of Online and Batch Versions of Bagging and Boosting (KDD 2001)

  • Nikunj C. Oza, Stuart J. Russell
  • [Paper]

• Semi-supervised MarginBoost (NIPS 2001)

  • Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise
  • [Paper]

• Boosting and Maximum Likelihood for Exponential Models (NIPS 2001)

  • Guy Lebanon, John D. Lafferty
  • [Paper]

• Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade (NIPS 2001)

  • Paul A. Viola, Michael J. Jones
  • [Paper]

• Boosting Localized Classifiers in Heterogeneous Databases (SDM 2001)

  • Aleksandar Lazarevic, Zoran Obradovic
  • [Paper]

2000

• Boosted Wrapper Induction (AAAI 2000)

  • Dayne Freitag, Nicholas Kushmerick
  • [Paper]

• An Improved Boosting Algorithm and its Application to Text Categorization (CIKM 2000)

  • Fabrizio Sebastiani, Alessandro Sperduti, Nicola Valdambrini
  • [Paper]

• Boosting for Document Routing (CIKM 2000)

  • Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal
  • [Paper]

• On the Boosting Pruning Problem (ECML 2000)

  • Christino Tamon, Jie Xiang
  • [Paper]

• Boosting Applied to Word Sense Disambiguation (ECML 2000)

  • Gerard Escudero, Lluís Màrquez, German Rigau
  • [Paper]

• An Empirical Study of MetaCost Using Boosting Algorithms (ECML 2000)

  • Kai Ming Ting
  • [Paper]

• FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (ICML 2000)

  • Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum
  • [Paper]

• Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (ICML 2000)

  • Tadashi Nomoto, Yuji Matsumoto
  • [Paper]

• A Boosting Approach to Topic Spotting on Subdialogues (ICML 2000)

  • Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker
  • [Paper]

• A Comparative Study of Cost-Sensitive Boosting Algorithms (ICML 2000)

  • Kai Ming Ting
  • [Paper]

• Boosting a Positive-Data-Only Learner (ICML 2000)

  • Andrew R. Mitchell
  • [Paper]

• A Column Generation Algorithm For Boosting (ICML 2000)

  • Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor
  • [Paper]

• A Gradient-Based Boosting Algorithm for Regression Problems (NIPS 2000)

  • Richard S. Zemel, Toniann Pitassi
  • [Paper]

• Weak Learners and Improved Rates of Convergence in Boosting (NIPS 2000)

  • Shie Mannor, Ron Meir
  • [Paper]

• Adaptive Boosting for Spatial Functions with Unstable Driving Attributes (PAKDD 2000)

  • Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
  • [Paper]

• Scaling Up a Boosting-Based Learner via Adaptive Sampling (PAKDD 2000)

  • Carlos Domingo, Osamu Watanabe
  • [Paper]

• Learning First Order Logic Time Series Classifiers: Rules and Boosting (PKDD 2000)

  • Juan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström
  • [Paper]

• Bagging and Boosting with Dynamic Integration of Classifiers (PKDD 2000)

  • Alexey Tsymbal, Seppo Puuronen
  • [Paper]

• Text Filtering by Boosting Naive Bayes Classifiers (SIGIR 2000)

  • Yu-Hwan Kim, Shang-Yoon Hahn, Byoung-Tak Zhang
  • [Paper]

1999

• Boosting Methodology for Regression Problems (AISTATS 1999)

  • Greg Ridgeway, David Madigan, Thomas Richardson
  • [Paper]

• Boosting Applied to Tagging and PP Attachment (EMNLP 1999)

  • Steven Abney, Robert E. Schapire, Yoram Singer
  • [Paper]

• Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ICML 1999)

  • Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
  • [Paper]

• AdaCost: Misclassification Cost-Sensitive Boosting (ICML 1999)

  • Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan
  • [Paper]

• Boosting a Strong Learner: Evidence Against the Minimum Margin (ICML 1999)

  • Michael Bonnell Harries
  • [Paper]

• Boosting Algorithms as Gradient Descent (NIPS 1999)

  • Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean
  • [Paper]

• Boosting with Multi-Way Branching in Decision Trees (NIPS 1999)

  • Yishay Mansour, David A. McAllester
  • [Paper]

• Potential Boosters (NIPS 1999)

  • Nigel Duffy, David P. Helmbold
  • [Paper]

1998

• An Efficient Boosting Algorithm for Combining Preferences (ICML 1998)

  • Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer
  • [Paper]

• Query Learning Strategies Using Boosting and Bagging (ICML 1998)

  • Naoki Abe, Hiroshi Mamitsuka
  • [Paper]

• Regularizing AdaBoost (NIPS 1998)

  • Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller
  • [Paper]

1997

• Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (ICML 1997)

  • Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee
  • [Paper]

• Using Output Codes to Boost Multiclass Learning Problems (ICML 1997)

  • Robert E. Schapire
  • [Paper]

• Improving Regressors Using Boosting Techniques (ICML 1997)

  • Harris Drucker
  • [Paper]

• Pruning Adaptive Boosting (ICML 1997)

  • Dragos D. Margineantu, Thomas G. Dietterich
  • [Paper]

• Training Methods for Adaptive Boosting of Neural Networks (NIPS 1997)

  • Holger Schwenk, Yoshua Bengio
  • [Paper]

1996

• Experiments with a New Boosting Algorithm (ICML 1996)
  • Yoav Freund, Robert E. Schapire
  • [Paper]

1995

• Boosting Decision Trees (NIPS 1995)
  • Harris Drucker, Corinna Cortes
  • [Paper]

1994

• Boosting and Other Machine Learning Algorithms (ICML 1994)
  • Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
  • [Paper]