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Events for March 17, 2017
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CS Yahoo! Machine Learning Seminar: Anshumali Shrivastava (Rice University) - Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
Fri, Mar 17, 2017 @ 10:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Anshumali Shrivastava, Rice University
Talk Title: Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: Large scale machine learning and data mining applications are constantly dealing with datasets at TB scale and the anticipation is that soon it will reach PB level. At this scale, simple data mining operations such as search, learning, and clustering become challenging.
In this talk, we will start with a basic introduction to probabilistic hashing (or fingerprinting) and the classical LSH algorithm. Then I will present some of my recent adventures with probabilistic hashing in making large-scale machine learning practical. I will show how the
idea of probabilistic hashing can be used to significantly reduce the computations in classical machine learning algorithms such Deep Learning (using our recent success with asymmetric hashing for inner products). I will highlight the computational bottleneck, i.e. the hashing time, and will show an efficient variant of minwise hashing. In the end, if time permits, I will demonstrate the use of probabilistic hashing for obtaining practical privacy-preserving
algorithms.
Biography: Anshumali Shrivastava is an assistant professor in the computer science department at Rice University. His broad research interests include large scale machine learning, randomized algorithms for big data systems and graph mining. He is a recipient of 2017 NSF CAREER Award. His research on hashing inner products has won Best Paper Award at NIPS 2014 while his work on representing graphs got the Best Paper Award at IEEE/ACM ASONAM 2014. He obtained his PhD in computer science from Cornell University in 2015.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Fri, Mar 17, 2017 @ 02:30 PM - 04:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: SPRING BREAK, NO CLASS, SPRING BREAK, NO CLASS
Talk Title: SPRING BREAK, NO CLASS
Series: Seminars in BME (Lab Rotations)
Host: Brent Liu, PhD
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Heterogeneous Attribute Embedding and Sequence Modeling for Recommendation with Implicit Feedback
Fri, Mar 17, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Kuan Liu, USC/ISI
Talk Title: Heterogeneous Attribute Embedding and Sequence Modeling for Recommendation with Implicit Feedback
Series: Natural Language Seminar
Abstract: Incorporating implicit feedback into a recommender system is a challenging problem due to sparse and noisy observations. I will present our approaches that exploit heterogeneous attributes and sequence properties within the observations. We build a neural network framework to embed heterogeneous attributes in an end-to-end fashion, and apply the framework to three sequence-based models. Our methods achieve significant improvements on four large scale datasets compared to state-of-the-art baseline models 30 to 90 percent relative increase in NDCG. Experimental results show that attribute embedding and sequence modeling both lead to improvements and, further, that our novel output attribute layer plays a crucial role. I will conclude with our exploratory studies that investigate why sequence modeling works well in recommendation systems and advocate its use for large scale recommendation tasks.
Biography: Kuan Liu is a fifth year Ph.D. student at ISI/USC working with Prof. Prem Natarajan. Before that, He received a bachelor degree from Tsinghua University with a major in Computer Science. His research interests include machine learning, large scale optimization, deep learning, and applications to recommender systems, network analysis.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
