Ran Gilad-Bachrach


 

Welcome to the home-page of Ran Gilad-Bachrach. I am a researcher at Microsoft Research in the machine learning department. Previously I was an applied researcher at Bing. Before that, I was a research scientist at Intel Research. I have earned my Ph.D. from the  Hebrew University of Jerusalem, Israel  in 2007.

Have a look at my technical blog: "one cent or less". The idea is to document there, solutions to these irritating problems I encounter when maintaining my computer or programing it.

 Fields of Interest

  • Algorithm design
  • Machine Learning
  • Healthcare
  • Machine Vision
  • Data Mining
  • Statistics
  • Probability
  • Distributed Computing
  • Combinatorics
  • ...

Bits of Information

List of Publications

2013
  • Jason D. Lee, Ran Gilad-Bachrach and Rich Caruana, Using Multiple Samples to Learn Mixture Models. NIPS, 2013. PDF
  • Jason D. Lee, Ran Gilad-Bachrach and Rich Caruana, Using Multiple Samples to Learn Mixture Models. CoRR abs/1311.7183, 2013.
  •  Ran Gilad-Bachrach and Christopher J.C. Burges, Classifier Selection using the Predicate Depth, MSR-TR-2013-8, 2013.
2012
  • Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir and Lin Xiao, Optimal Distributed Online Prediction Using Mini-Batches, JMLR, 2012. PDF
  • Karthik Raman, Krysta Marie Svore, Ran Gilad-Bachrach and Christopher J. C. Burges, Learning from Mistakes: Towards a Correctable Learning Algorithm, CIKM, 2012. PDF
  • Moshe Gabel, Assaf Schuster, Ran Gilad-Bachrach and Nikolaj Bjørner, Latent Fault Detection in Large Scale Services, DSN, 2012. PDF  
  • Trang Thai, Gerald DeJean and Ran Gilad-Bachrach, Confined Intra-Arm Communication and Medical Applications - Extended Abstract, MSR-TR-2012-74, 2012
  • Moshe Gabel, Erin Renshaw, Assaf Schuster and Ran Gilad-Bachrach, Full Body Gait Analysis with Kinect, EMBC, 2012. PDF

2011

  • Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir and Lin Xiao, Optimal Distributed Online Prediction, ICML 2011. PDF
  • Ashok Kumar Ponnuswami, Kumaresh Pattabiraman, Qiang Wu, Ran Gilad-Bachrach, Tapas Kanungo, On Composition of a Federated Web Search Result Page: Using Online Users to Provide Pairwise Preference for Heterogeneous Verticals, WSDM, 2011. PDF

2009

  •  Ran Gilad-Bachrach, Aharon Bar-Hillel and Liat Ein-Dor Efficient Human Computation, technical report arXiv:0903.1125v1PDF

2007

  • Liat Ein-Dor, Yossi Ittach, Aharon Bar-Hillel, Amir Di-Nur, and Ran Gilad-Bachrach Reinforcement learning for capacity tuning of multi core servers, in machine learning for systems problems (MLSys) workshop at NIPS 2007PDF 
  • Aharon Bar-Hillel, Amir Di-Nur, Liat Ein-Dor, Ran Gilad-Bachrach and Yossi Ittach Workstation Capacity Tuning using Reinforcement Learning. In proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2007.
  • Ran Gilad-Bachrach, To PAC and Beyond. Ph.D. Thesis, the Hebrew University of Jerusalem. PDF 

 2006

  • Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, Large Margin Principles for Feature Selection. In I. Guyon, S. Gunn,  M. Nikravesh and L. Zadeh (editors), Feature extraction, foundations and applications. Springer, 2006
  • Amir Navot, Ran Gilad-Bachrach, Yiftah Navot and Naftali Tishby, Is Feature Selection Still Necessary?. Lecture Notes in Computer Science, Volume 3940, Pages 127 - 138, May 2006. PDF

2005

  • Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, Query By Committeee Made Real. In proceedings  of the 19'th Advances in Neural Information Processing Systems conference (NIPS), 2005 PDF
  • Ran Gilad-Bachrach, Dimensionality Reduction for Online Learning Algorithms using Random Projections. Technical Report of the Leibniz Center, the Hebrew University number 2005-11, 2005 PDF

2004 

  • Ran GIlad-Bachrach, Amir Navot and Naftali Tishby, Margin Based Feature Selection - Theory and Algorithms. In proceedings of the 21'st International Conference on Machine Learning (ICML), 2004 PDF 
  • Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, Bayes and Tukey Meet at the Center Point, In proceedings of the 17'th Conference on Learning Theory (COLT), 2004 PDF

2003

  • Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, Kernel Query By Committee (KQBC). Technical Report of the Leibniz Center, the Hebrew University number 2003-88, 2003 PDF
  • Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, An Information Theoretic Tradeoff  Between Complexity and Accuracy. In proceedings of the 16'th Conference on Learning Theory (COLT), 2003 PDF

2002

  • Koby Crammer, Ran Gilad-Bachrach, Amir Navot and Naftali Tishby, Margin Analysis of the LVQ algorithm. In proceedings  of the 16'th Advances in Neural Information Processing Systems conference (NIPS), 2002 PDF 
  • Ran Bachrach, Shai Fine and Eli Shamir, Learning using Query By Committee, Linear Separation and Random Walks. Theoretical Computer Science, 284:1, 2002 PDF (preprint) 
  • Ran Bachrach, Ran El-Yaniv and Martin Reinstadtler, On the Competative Theory and Practice of Online List Accessing Algorithms. Algorithmica, 32:2 201-245, 2002 PDF

2001

  • Scott Axelrod, Shai Fine, Ran Gilad-Bachrach, Shahar Mendelson and Naftali Tishby, The Information Of Observations And Applications For Active Learning With Uncertainty. Technical Report of the Leibniz Center, the Hebrew University number 2001-81, 2001 PDF 

2000 

  •  Shai Fine, Ran Gilad-Bachrach, Shahar Mendelson and Naftali Tishby, Noise tolerant learning via the dual learning problem. Technical Report of the Leibniz Center, the Hebrew University number 2000-14, 2000 PDF

1999

  • Shai Fine, Ran Gilad-Bachrach, Eli Shamir and Naftali Tishby, Noise Tolerant Learning Using Early Predictors. Technical Report of the Leibniz Center, the Hebrew University number 1999-22, 1999 PDF 
  • Ran Bachrach, Shai Fine, Eli Shamir, Query By Committee, Linear Separation and Random Walks. In proceedomgs of the 4'th European Conference on Computational Learning Theory (EuroCOLT), 1999 

1998 

  • Ran Bachrach, Ran El-Yaniv, Noam Slonim and Naftali Tishby, Training Context-Insensitive Versus Context-Sensitive Text Classifiers using Small Data Sets. Manuscript, 1998 PDF

1997

  • Ran Bachrach and Ran El-Yaniv, Online List Accessing Algorithms and Their Applications: Recent Empirical Evidence.  In proceedings of the 8'th annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1997