Kilian Q. Weinberger




Sometimes I get invited to give talks about my research.

2017
December 2017, NIPS, Symposium on Interpretable Machine Learning, Keynote, Youtube Link
December 2017, NIPS, Symposium on Interpretable Machine Learning, Debate, Youtube Link
September 2017, GCPR, Keynote, Basel, CH
May 2017, Heidelberg University, Germany, Colloquium
Spring 2017, Seminar on Ethics in A.I., Cornell University, Ithaca, NY, Invited Talk, Youtube Link
February 2017, Nvidia Research, Santa Clara, CA

2016
October 2016, TTI-Chicago, Colloquium, Chicago, IL
September 2016, Northeastern, Colloquium, Boston, MA
May 2016, Facebook Research, NYC
May 2016, Microsoft Research, NYC
April 2016, Harvard, Boston MA, USA

2015
July 2015, ICML, Lille, France
June 2015, BeneLearn, Keynote

2014

October 2014, Universität Basel, CH, Colloquium
October 2014, ETH, CH, AI Seminar
October 2014, Purdue, AI Seminar
September 2014, Rutgers, AI Colloquium
September 2014, Cornell University, Colloquium
June 2014, Tsinghua University, Colloquium
May 2014, Stampede 2014, Machine Learning in Practice [pdf]
April 2014, Boston University, Colloquium
February 2014, Université Paris-Sud

2013
December 2013, NIPS Workshop on personalization, NIPS 2013 (Reno Nevada)
December 2013, NIPS Workshop on output representation learning, NIPS 2013 (Reno Nevada)
December 2013, Workshop on Resource-Efficient Machine Learning, NIPS 2013 (Reno Nevada)
September 2013, Rice University (Statistics), Colloquium
June 2013,ICML Workshop on representation learning, [pdf]
April 2013, University of Utah Colloquium, Salt Lake City, USA
April 2013, John’s Hopkins University, Baltimore Colloquium, MD, USA [Video]
April 2013, University of Maryland Colloquium, College Park, MD, USA
April 2013, Microsoft Research, NYC, NY, USA


2012
July 2012, University of Glasgow, GB
July 2012, Microsoft Research Bellevue, WA
June 2012,University of Illinois at Urbana-Champaign, IL
January 2012, Midwest Vision Workshop, TTI-C Chicago, IL

2011
December 2011, NIPS Workshop on Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity, Granada Spain
September 2011,Qualcomm Contextual Awareness Symposium, San Diego CA
August 2011, Dagstuhl Workshop (11341) on Learning in the context of very high dimensional data (Dagstuhl, Germany)

2010
December 2010, Distance Metric Learning for Kernel Machines, NIPS Multiple Kernel Learning Workshop (Whistler, CA) [videolectures.net]
July 2010, Tree Ensemble and Transfer Learning, ICML Learning to Rank Workshop (Haifa, Israel)

2009
October 2009, Large-Scale Multitask learning, Department of Computer Science, University of California San Diego
July 2009, Collaborative Email-Spam Filtering with the Hashing-Trick, CEAS 2009
June 2009, Feature Hashing for Large Scale Multitask Learning, ICML 2009, Montreal (Canada)
April 2009, Learning Data Representations with Convex Optimization, EPFL (Switzerland)
April 2009, Learning Data Representations with Convex Optimization, Department of Computer Science, Washington University St. Louis
March 2009, Learning Data Representations with Convex Optimization, Gatsby Unit, University College London (UK)
March 2009, Learning Data Representations with Convex Optimization, Computer Science Department, Indiana University

2008
July 2008, Fast Solvers and Efficient Implementations for Distance Metric Learning, ICML 2008, Helsinki (Finland) [videolectures.net]
October 2008 , Taxonomy Embedding, University of California Merced
November 2008, Learning Data Representations with Convex Optimization, Viterbi school of Engineering, University of Southern California

2007
January 2007, Metric learning with semidefinite programming, Workshop on Mathematical Programming in Machine Learning, Banff, Canada
March 2007, Metric learning with semidefinite programming, Yahoo Research, Santa Clara
March 2007, Metric learning with semidefinite programming, Toyota Institute of Technology, Chicago
February 2007, Metric learning with semidefinite programming, Machine Learning Department, Carnegie Mellon University
February 2007, Metric learning with semidefinite programming, Department of Computer Science, Brown University

2006
October 2006, Regression in high dimensional spaces, IBM T.J. Watson Hawthorne

2005
December 2005, Distance Metric Learning for Large Margin Nearest Neighbor Classification, NIPS 2005
July 2005, Survey on Distance Metric Learning, Department of Computer Science, University of Pennsylvania, (Dissertation Qualifier)
April 2005, Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization, CIAR Workshop 2005
April 2005, Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization, AISTATS 2005

2004
July 2004, Unsupervised Learning of Image Manifolds by Semidefinite Programming, CVPR 2004
July 2004, Learning a Kernel Matrix for Nonlinear Dimensionality Reduction, ICML 2004