Brief Biography

Research Interests

My research interests lie in machine learning and data mining, more specifically:

• Methods for learning decision policies from data describing outcomes of previous decisions (including cost-sensitive learning and bias correction).
• Reductions between machine learning problems (for example: between reinforcement learning and classifier learning).
• Applications to business problems such as customer segmentation, customer wallet estimation and fraud detection.

Teaching (in Portuguese)

2010/2

• Inteligência Artificial (Graduação)
  
• Inteligência Artificial (Pós-Graduação)
  

2010/1

• Aprendizado de Máquina (Pós-Graduação)
  

2009/1

• Inteligência Artificial (Graduação)
  
• Aprendizado de Máquina (Pós-Graduação)
  

2008/2

• Programação de Computadores (Graduação)
  
• Processamento Estatístico de Linguagem Natural (Pós-Graduação)
  

2007/2

• Informática I
  
• Introdução à Informática
  

2006/2

• Informática I
  
• Programação de Computadores III
  
• Tópicos Avançados em Otimização e IA II (Aprendizado Automático)
  

2006/1

• Informática I
  
• Engenharia de Software II
  

Publications

2006

• S. Rosset, C. Perlich and B. Zadrozny, Ranking-based Evaluation of Regression Models. In Knowledge and Information Systems.
• N. Abe, B. Zadrozny and J. Langford, Outlier Detection by Active Learning. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD'06).
• J. Langford, R. Oliveira and B. Zadrozny, Predicting Conditional Quantiles via Reduction to Classification. In Proceedings of the Uncertainty in Artificial Intelligence Conference ) (UAI'06).

2005

• S. Rosset, C. Perlich, B. Zadrozny, S. Merugu, S. Weiss and R. Lawrence. Wallet Estimation Models. In Proceedings of the International Workshop on Customer Relationship Management: Data Mining Meets Marketing (CRM Workshop).

• S. Rosset, C. Perlich and B. Zadrozny. Ranking-Based Evaluation of Regression Models. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05).

• W. Fan, I. Davidson, B. Zadrozny and P. S. Yu. An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05).

• M. Ettl, B. Zadrozny, P. Chowdhary and N. Abe. Business Performance Management System for CRM and Sales Execution. In Proceedings of the Sixteenth International Conference on Database and Expert Systems Applications (DEXA'05).

• B. Zadrozny. One-Benefit Learning: Cost-Sensitive Learning with Restricted Cost Information. In Proceedings of the Workshop on Utility-Based Data Mining at the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (UBDM@KDD'05).

• A. Beygelzimer, V. Dani, T. Hayes, J. Langford and B. Zadrozny. Error Limiting Reductions Between Classification Tasks. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML'05).

• J. Langford and B. Zadrozny. Relating Reinforcement Learning Performance to Classification Performance. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML'05).

• A. Beygelzimer, J. Langford and B. Zadrozny. Estimating Class Membership Probabilities using Classifier Learners. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI'05).

• J. Langford and B. Zadrozny. Estimating Class Membership Probabilities using Classifier Learners. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTAT'05).

2004

• N. Abe, B. Zadrozny and J. Langford. An Iterative Method for Multi-Class Cost-Sensitive Learning. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD'04).

• B. Zadrozny. Learning and Evaluating Classifiers under Sample Selection Bias. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML'04).

• N. Abe, C. Apte, B. Bhattacharjee, K. Goldman, J. Langford and B. Zadrozny. Sampling Approach to Resource Light Mining. In Proceedings of the Data Mining in Resource Constrained Environments Workshop at the Fourth SIAM International Conference on Data Mining (SIAM'04).

2003

• B. Zadrozny.  Policy mining: Learning decision policies from fixed sets of data, Ph.D. Thesis, University of California, San Diego, 2003.

• B. Zadrozny, J. Langford and N. Abe.  Cost-Sensitive Learning by Cost-Proportionate Example Weighting. In Proceedings of the 2003 IEEE International Conference on Data Mining (ICDM'03).

2002

• N. Abe, E. Pednault, H. Wang, B. Zadrozny, W. Fan, C. Apte.  Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02).

• B. Zadrozny and C. Elkan. Transforming classifier scores into accurate multiclass probability estimates .  In Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD'02).

• E. Pednault, N. Abe, B. Zadrozny and others. Sequential cost-sensitive decision-making with reinforcement learning.  In Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD'02).

2001

• B. Zadrozny. Reducing multiclass to binary by coupling probability estimates. In Advances in Neural Information Processing Systems 14 (NIPS*2001).

• B. Zadrozny and C. Elkan. Learning and Making Decisions When Costs and Probabilities are Both Unknown . In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining (KDD'01).

• B. Zadrozny and C. Elkan. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers.  In Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01).