Awesome Gradient Boosting Research Papers.

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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.

2023

• Computing Abductive Explanations for Boosted Trees (AISTATS 2023)

  • Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski
  • [Paper]

• Boosted Off-Policy Learning (AISTATS 2023)

  • Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
  • [Paper]

• Variational Boosted Soft Trees (AISTATS 2023)

  • Tristan Cinquin, Tammo Rukat, Philipp Schmidt, Martin Wistuba, Artur Bekasov
  • [Paper]

• Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces (AISTATS 2023)

  • Eric Xia, Martin J. Wainwright
  • [Paper]

• FairGBM: Gradient Boosting with Fairness Constraints (ICLR 2023)

  • André Ferreira Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro
  • [Paper]

• Gradient Boosting Performs Gaussian Process Inference (ICLR 2023)

  • Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
  • [Paper]

2022

• TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion (AAAI 2022)

  • Yiheng Sun, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong, Yunjie Xu
  • [Paper]

• A Resilient Distributed Boosting Algorithm (ICML 2022)

  • Yuval Filmus, Idan Mehalel, Shay Moran
  • [Paper]

• Fast Provably Robust Decision Trees and Boosting (ICML 2022)

  • Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou
  • [Paper]

• Building Robust Ensembles via Margin Boosting (ICML 2022)

  • Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
  • [Paper]

• Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment (KDD 2022)

  • Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu
  • [Paper]

• Federated Functional Gradient Boosting (AISTATS 2022)

  • Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi
  • [Paper]

• ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics (AISTATS 2022)

  • Daniel Csillag, Carolina Piazza, Thiago Ramos, João Vitor Romano, Roberto I. Oliveira, Paulo Orenstein
  • [Paper]

2021

• Precision-based Boosting (AAAI 2021)

  • Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles
  • [Paper]

• BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data (CIKM 2021)

  • Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach
  • [Paper]

• Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees (CIKM 2021)

  • Panpan Qi, Wei Wang, Lei Zhu, See-Kiong Ng
  • [Paper]

• Individually Fair Gradient Boosting (ICLR 2021)

  • Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
  • [Paper]

• Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees (ICLR 2021)

  • Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
  • [Paper]

• AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models (ICLR 2021)

  • Ke Sun, Zhanxing Zhu, Zhouchen Lin
  • [Paper]
  • [Code]

• Uncertainty in Gradient Boosting via Ensembles (ICLR 2021)

  • Andrey Malinin, Liudmila Prokhorenkova, Aleksei Ustimenko
  • [Paper]

• Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (ICLR 2021)

  • Sergei Ivanov, Liudmila Prokhorenkova
  • [Paper]

• GBHT: Gradient Boosting Histogram Transform for Density Estimation (ICML 2021)

  • Jingyi Cui, Hanyuan Hang, Yisen Wang, Zhouchen Lin
  • [Paper]

• Boosting for Online Convex Optimization (ICML 2021)

  • Elad Hazan, Karan Singh
  • [Paper]

• Accuracy, Interpretability, and Differential Privacy via Explainable Boosting (ICML 2021)

  • Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni
  • [Paper]

• SGLB: Stochastic Gradient Langevin Boosting (ICML 2021)

  • Aleksei Ustimenko, Liudmila Prokhorenkova
  • [Paper]

• Self-boosting for Feature Distillation (IJCAI 2021)

  • Yulong Pei, Yanyun Qu, Junping Zhang
  • [Paper]

• Boosting Variational Inference With Locally Adaptive Step-Sizes (IJCAI 2021)

  • Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
  • [Paper]

• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression (KDD 2021)

  • Olivier Sprangers, Sebastian Schelter, Maarten de Rijke
  • [Paper]

• Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction (KDD 2021)

  • Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Weiwei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning
  • [Paper]

• Better Short than Greedy: Interpretable Models through Optimal Rule Boosting (SDM 2021)

  • Mario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I. Webb
  • [Paper]

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)

  • Kenta Oono, Taiji Suzuki
  • [Paper]
  • [Code]

• Gradient Boosted Normalizing Flows (NeurIPS 2020)

  • Robert Giaquinto, Arindam Banerjee
  • [Paper]
  • [Code]

• 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)

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

• 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)

  • 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]

• 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)

  • 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]

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License

• CC0 Universal