Mat-Stat seminar - Mark Carman (Monash University, Australia)
October 12, h. 10.30, in the Department of Economics, Management and Quantitative Methods seminar room, via conservatorio 7, 2nd floor, Mark Carman (Monash University, Australia) will hold a seminar:
“Investigating Performance and Scalability for Rank Learning with Regression Tree Ensembles”
When ranking Web pages against user queries, a large number of signals can be leveraged to estimate the relevance of each document, including query similarity, user-profile similarity, PageRank, etc. Rank learning algorithms provide a coherent framework for combining these signals in order to maximize retrieval performance. As such, they have become a crucial component of current Information Retrieval infrastructure. State-of-the-art rank learning techniques discover non-linear combinations of features and are mostly based on ensembles of regression trees, using either bagged or boosted ensembles. With an interest in both performance and scalability of these algorithms, we investigate the importance of different aspects, such as the number of negative examples used, the size of subsamples from which trees are learnt, and most importantly the type of objective function used for recursively partition the feature space.
Mark Carman is a Senior Lecturer at Monash University, a top-100 rated university in Melbourne, Australia. He joined Monash in 2010 after doing a postdoc at the University of Lugano. He received his PhD from the University of Trento in 2006 having spent his PhD tenure at both the Fondazione Bruno Kessler (FBK) and the Information Sciences Institute (ISI) of USC. Mark's research lies in Data Science with a particular focus on problems in Information Retrieval. He has worked on techniques for learning Web Search rankings, for scaling learning algorithms to large data quantities, for robust clustering in high dimensions, for improving quality-control in crowd-sourcing, and for personalising search results and recommending content. Other applications of his work include speeding up digital forensic investigations, detecting sentiment and sarcasm in text, correcting errors in OCR output, and estimating user expertise in social media. Mark has authored a large number of publications in prestigious venues, including full papers at SIGIR, KDD, IJCAI, CIKM, ECIR, WSDM, HT, CoNLL, EACL, HCOMP and ICDAR, and articles in TOIS, IR, JMLR, ML, PR, JAIR, CS&L, JASIST, DI and CSUR. Moreover, he has served on the program committees of many IR/DM/AI conferences, including SIGIR, WSDM, CIKM, ECIR, KDD, WWW, EMNLP, ACML, IJCAI and AAAI and is currently an Associate Editor for the journal TOIS.