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Awesome Gradient Boosting Research Papers.

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

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

2020

  • A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains (AAAI 2020)

    • Harsha Kokel, Phillip Odom, Shuo Yang, Sriraam Natarajan
    • [Paper]
    • [Code]
  • Practical Federated Gradient Boosting Decision Trees (AAAI 2020)

    • Qinbin Li, Zeyi Wen, Bingsheng He
    • [Paper]
  • Privacy-Preserving Gradient Boosting Decision Trees (AAAI 2020)

    • Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
    • [Paper]
  • Accelerating Gradient Boosting Machines (AISTATS 2020)

    • Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni
    • [Paper]
  • Scalable Feature Selection for Multitask Gradient Boosted Trees (AISTATS 2020)

    • Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian
    • [Paper]
  • Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees (AISTATS 2020)

    • Atsushi Nitanda, Taiji Suzuki
    • [Paper]
  • Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost (IJCAI 2020)

    • Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet
    • [Paper]
  • MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance (ICDM 2020)

    • Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy
    • [Paper]
  • Boosting for Control of Dynamical Systems (ICML 2020)

    • Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu
    • [Paper]
  • Quantum Boosting (ICML 2020)

    • Srinivasan Arunachalam, Reevu Maity
    • [Paper]
  • Boosted Histogram Transform for Regression (ICML 2020)

    • Yuchao Cai, Hanyuan Hang, Hanfang Yang, Zhouchen Lin
    • [Paper]
  • Boosting Frank-Wolfe by Chasing Gradients (ICML 2020)

    • Cyrille W. Combettes, Sebastian Pokutta
    • [Paper]
  • NGBoost: Natural Gradient Boosting for Probabilistic Prediction (ICML 2020)

    • Tony Duan, Avati Anand, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler
    • [Paper]
    • [Code]
  • Online Agnostic Boosting via Regret Minimization (NeurIPS 2020)

    • Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran
    • [Paper]
  • Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates (NeurIPS 2020)

    • Kaiwen Zhou, Anthony Man-Cho So, James Cheng
    • [Paper]
  • Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks (NeurIPS 2020)

  • Gradient Boosted Normalizing Flows (NeurIPS 2020)

  • HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (WSDM 2020)

    • Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiaoli Li
    • [Paper]

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)

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

    • Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei
    • [Paper]
  • Fair Adversarial Gradient Tree Boosting (ICDM 2019)

    • Vincent Grari, Boris Ruf, Sylvain Lamprier, Marcin Detyniecki
    • [Paper]
  • Boosted Density Estimation Remastered (ICML 2019)

  • 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)

  • SpiderBoost and Momentum: Faster Variance Reduction Algorithms (NeurIPS 2019)

    • Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
    • [Paper]
  • Faster Boosting with Smaller Memory (NeurIPS 2019)

    • Julaiti Alafate, Yoav Freund
    • [Paper]
  • Regularized Gradient Boosting (NeurIPS 2019)

    • Corinna Cortes, Mehryar Mohri, Dmitry Storcheus
    • [Paper]
  • Margin-Based Generalization Lower Bounds for Boosted Classifiers (NeurIPS 2019)

    • Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson
    • [Paper]
  • Minimal Variance Sampling in Stochastic Gradient Boosting (NeurIPS 2019)

    • Bulat Ibragimov, Gleb Gusev
    • [Paper]
  • Universal Boosting Variational Inference (NeurIPS 2019)

    • Trevor Campbell, Xinglong Li
    • [Paper]
  • Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks (NeurIPS 2019)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

2016

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

  • 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)

  • 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)

  • 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)

  • 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)

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

  • 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)

  • 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)

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

  • 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)

  • 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)

2014

  • On Boosting Sparse Parities (AAAI 2014)

  • 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)

  • 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)

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

  • Deep Boosting (ICML 2014)

  • 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)

  • 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)

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

    • Tianqi Chen, Hang Li, Qiang Yang, Yong Yu
    • [Paper]
  • Margins, Shrinkage, and Boosting (ICML 2013)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

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

  • 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)

  • 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)

  • 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)

  • 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)

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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

2006

  • Gradient Boosting for Sequence Alignment (AAAI 2006)

    • Charles Parker, Alan Fern, Prasad Tadepalli
    • [Paper]
  • Boosting Kernel Models for Regression (ICDM 2006)

  • 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)

  • 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)

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)

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

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

  • 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)

  • 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)

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

2003

  • On Boosting and the Exponential Loss (AISTATS 2003)

  • 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)

  • 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)

  • 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)

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

  • 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)

  • 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)

2001

  • Is Regularization Unnecessary for Boosting? (AISTATS 2001)

  • 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)

  • 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)

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

  • 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)

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

  • 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)

  • 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)

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

  • 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)

  • 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)

  • 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)

  • Improving Regressors Using Boosting Techniques (ICML 1997)

  • 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]

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