Selected and Recent papers
		Online Learning in The Manifold of Low-Rank Matrices U. Shalit, D. Weinshall, G. Chechik Neural Information Processing Systems (NIPS spotlight) 2010		PDF| supplement
		Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network G. Chechik, E. Oh, O. Rando, J. Weissman, A. Regev and D. Koller Nature Biotechnology, 26(11) pp 1251-1259. Nov 2008 , Local PDF version Activity motifs web-page | Research Highlights in Nat Chem Biology, Nat Reviews genetics		web-page | bibtex Local PDF version Online Text version
		Functional Organization of the S. cerevisiae Phosphorylation Network D. Fiedler, H. Braberg, M. Mehta, G. Chechik, G. Cagney, P. Mukherjee, and A.C. Silva, M. Shales, S.R. Collins, S. van Wageningen, P. Kemmeren, F.C.P. Holstege, J.S. Weissman, M. Christopher-Keogh, D. Koller, K.M. Shokat, and N.J. Krogan Cell, 136(5) 952-63. 2009
		Timing properties of gene expression responses to environmental changes G. Chechik and D. Koller J. Computational Biology. Vol 16, p. 279-290, 2009		web-page Local PDF (long)
		Reduction of Information Redundancy in the Ascending Auditory Pathway G. Chechik, M. Anderson, O. Bar-Yosef, E. Young, N. Tishby and I. Nelken Neuron 51 (3), 359-368, 2006. Full Text | PDF News-and-views by J. Schnupp; Accompanying web-page. Faculty of 1000		web-page | PDF News and views Faculty of 1000
		Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks Alexis Battle, Gal Chechik and Daphne Koller Neural Information Processing Systems NIPS 2006; Human Brain Mapping 2006; 3rd prize, 2006 EBC competition..		NIPS PDF bibtex

Full list of Publications

		Journal Papers
		20. Large scale online Learning of Image Similarity through ranking G. Chechik, V. Sharma, U. Shalit, S. Bengio J. Machine Learning Research. 11 p. 1109-1135, 2010, OASIS web-page		web-page | code Local PDF version
		19. Sound retrieval and ranking using auditory sparse-code representations RF. Lyon, M. Rehn, T. Walters, S. Bengio, G. Chechik Neural Computation, 22(9) 2390-2416, 2010
		18. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network G. Chechik, E. Oh, O. Rando, J. Weissman, A. Regev and D. Koller Nature Biotechnology, 26(11) pp 1251-1259. Nov 2008		web-page | bibtex Local PDF version Online Text version
		17. Functional Organization of the S. cerevisiae Phosphorylation Network D. Fiedler, H. Braberg, M. Mehta, G. Chechik, G. Cagney, P. Mukherjee, and A.C. Silva, M. Shales, S.R. Collins, S. van Wageningen, P. Kemmeren, F.C.P. Holstege, J.S. Weissman, M. Christopher-Keogh, D. Koller, K.M. Shokat, and N.J. Krogan Cell, 136(5) 952-63. 2009
		16. Timing properties of gene expression responses to environmental changes G. Chechik and D. Koller J. Computational Biology. Vol 16, p. 279-290, 2009		web-page Local PDF (long)
		15. Max Margin classification of data with absent features Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel and Daphne Koller Journal of Machine Learning Research, JMLR, 9(Jan):1--21, 2008		PDF | online
		14. Euclidean Embedding of Co-occurrence Data. Amir Globerson, Gal Chechik, Fernando Pereira and Naftali Tishby Journal of Machine Learning Research, JMLR, 8 (Oct), 2007		PDF | code | data
		13. Information theory in auditory research Israel Nelken, Gal Chechik Hearing Research 229, p. 94-105, 2007		Online
		12. Reduction of Information Redundancy in the Ascending Auditory Pathway G. Chechik, M. Anderson, O. Bar-Yosef, E. Young, N. Tishby and I. Nelken Neuron 51 (3), p. 359-368, 2006.		Full text | PDF | News-and-views | web-page
		11. Discrete profile alignment via information bottleneck. S. O'Rourke, G. Chechik, R. Friedman, and E. Eskin BMC bioinformatics, 7(S1):S8, Feb 2006, p. 1-11.		Full text | PDF
		10. Encoding stimulus information by spike numbers and mean response time in primary auditory cortex. I. Nelken, G. Chechik, T.D. Mrsic Flogel A.J. King and J.W.H. Schupp J. Computational Neuroscience 19(2):199-221, 2005		Full text | PDF
		9. Information Bottleneck for Gaussian variables. G. Chechik, A. Globerson, N. Tishby and Y. Weiss J. Machine Learning Research 6(Jan) p.165-188, 2005		PDF | Local
		8. Applying an artificial neural network to warfarin maintenance dose prediction. I. Solomon, N. Marashak, G. Chechik, L. Leibovici, A. Lubetsky, H. Halkin, D. Ezra and N. Ash Isr. Med. Assoc. J., 6(12): 732-735, 2004		Abstract
		7. Spike timing dependent plasticity and relevant information maximization. Gal Chechik Neural Computation 15 (7) p.1481-1510, 2003		Full text | PDF
		6. Effective Learning with Ineffective Hebbian Learning Rules. Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neural Computation 13(4) p.817-840, 2001		Full text | PDF
		5. Spike time dependent plasiticty and mutual information maximization Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neurocomputing, 38: 147-152,2001		Full text
		4. Neuronal normalization provides effective learning through ineffecive learning rules. Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neurocomputing, 32:345-351, 2000
		3. Neuronal Regulation: A Mechanism for Efficient Synaptic Pruning During Brain Maturation. Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neural Computation 11(8) p. 2151-2170. 1999		Full text | PDF
		2. Neuronal regulation: A biologically plausible mechanism for efficient synaptic pruning in development Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neurocomputing, 26-27: 633-639, 1999
		1. Synaptic pruning in development: a computational account. Gal Chechik, Isaac Meilijson, and Eytan Ruppin Neural Computation 10 (7) p.1759-1777, 1998		Full text | PDF

		Reviewed Conference Papers
		25. Online Learning in The Manifold of Low-Rank Matrices U. Shalit, D. Weinshall, G. Chechik Neural Information Processing Systems (NIPS spotlight) 2010		PDF| supplement
		24. Object Separation In X-Ray Image Sets GA Heitz, G. Chechik Computer Vision and Pattern Recognition (CVPR, oral) 2010		PDF
		23. Large scale online Learning of Image Similarity through ranking G. Chechik, V. Sharma, U. Shalit, S. Bengio Neural Information Processing System, NIPS 2009		web-page | code Local PDF version
		22. Large-Scale Content-Based Audio Retrieval from Text Queries. Gal Chechik, Eugene Ie, Martin Rehn, Samy Bengio, Dick Lyon Multimedia information retrieval, MIR 2008.		PDF
		21. Max Margin classification of incomplete data. Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel and Daphne Koller Neural Information Processing Systems, NIPS 2006.		PDF
		20. Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks Alexis Battle, Gal Chechik and Daphne Koller Neural Information Processing Systems NIPS 2006		PDF
		19. Euclidean Embedding of Co-occurrence Data Outstanding student paper award A. Globerson, G. Chechik, F. Pereira and N. Tishby Neural Information Processing Systems, NIPS 2004 p.497-504.		PDF | Code | Data
		18. Embedding Heterogeneous Data Using Statistical Models. A. Globerson, G. Chechik, F. Pereira and N. Tishby American Association for Artificial Intelligence (AAAI) 2006, Nectar Track		PDF | Code | Data
		17. Filling missing enzymes in metabolic pathways using heterogeneous data. Gal Chechik, Aviv Regev and Daphne Koller NIPS Computational Biology workshop, Whistler 2005
		16. Changes in Stimulus representations in the ascending auditory pathway G. Chechik, M. Anderson, O. Bar-Yosef, E. Young, N. Tishby and I. Nelken COSYNE 2005
		15. Discrete profile alignment via information bottleneck Sean O'Rourke, Gal Chechik, Robin Friedman and Eleazar Eskin NIPS 2004		ps | pdf
		14. A needle in a haystack: Local one class optimization Koby Crammer and Gal Chechik, International conference in machine learning, ICML 2004		ps | pdf | talk-ppt
		13. Information Bottleneck for Gaussian Variables. Gal Chechik, Amir Globerson, Naftali Tishby and Yair Weiss NIPS 2003		ps | pdf | talk-ppt talk-pdf
		12. Extracting continuous relevant features. Amir Globerson, Gal Chechik and Naftali Tishby in: Daniel Baier and Klaus-Dieter Wernecke (eds.): Innovations in Classification, Data Science, and Information Systems. Proc. 27th Annual GfKl Conference, University of Cottbus, Germany 2003. Springer-Verlag, Heidelberg-Berlin, 224-238, 2004.
		11. Sufficient Dimensionality reduction with irelevance statistics. Amir Globerson, Gal Chechik and Naftali Tishby Uncertainty in artificial inteligence, Acapulco Mexico (UAI 2003)		ps | pdf
		10. Are there representations in evolved embodied agents? Taking measures. Hezi Avraham, Gal Chechik and Eytan Ruppin European conference on artificial life,(ECAL 2003)		ps | pdf
		9. Extracting relevant structures with side information. Gal Chechik and Naftali Tishby Neural Information Processing Systems-15, NIPS 2002		ps | pdf short ver talk-ppt code
		8. Groups redundancy measures reveal redundancy reduction along the auditory pathway. Gal Chechik, A. Globerson, M.J. Anderson, Eric D. Young, Israel Nelken and N. Tishby Neural Information Processing Systems-14, (NIPS 2001)		ps | pdf
		7. Spike time dependant plasticity and mutual information Gal Chechik and Naftali Tishby Advances in Neural Information Processing Systems 13, Vancouver Canada (NIPS 2000)		ps | pdf
		6. Temporal dependant plasticity maximizes mutual information in a spiking neural network Gal Chechik Ninth Annual Computational Neuroscience Meeting, Bruge Belgium (CNS 2000)		pdf
		5. Effective learning requires synaptic remodeling at the neuronal level. Gal Chechik, Isaac Meilijson and Eytan Ruppin. Advances in Neural Information Processing Systems 12 (NIPS 1999)		ps | pdf
		4. Neuronal normalization provides effective learning through ineffective synaptic learning rules. Gal Chechik, Isaac Meilijson and Eytan Ruppin. Eighth Computational Neuroscience meeting, Pittsburgh, Pennsylvania. (CNS 1999)		ps | pdf
		3. Neuronal Regulation Implements Efficient Synaptic Pruning. Gal Chechik, Isaac Meilijson and Eytan Ruppin. Advances in Neural Information Processing Systems 11. (NIPS 1998)		ps | pdf
		2. Efficient Synaptic Pruning with Neuronal Regulation Gal Chechik, Isaac Meilijson and Eytan Ruppin Seventh Annual Computational Neuroscience Meeting, Santa Barbara, CA. (CNS 1998)		ps
		1. Synaptic Pruning: A Novel Account in Neural Terms. Gal Chechik, Isaac Meilijson, and Eytan Ruppin Sixth Annual Computational Neuroscience Meeting, CNS 1997		ps

		Books and Special issues
		NIPS workshop on New Problems and Methods in Computational Biology. Yanjun Qi, Gal Chechik, editors, BMC bioinformatics, Volume 11 Suppl 8, 2010
		NIPS workshop on New Problems and Methods in Computational Biology. Gal Chechik, Christina Leslie, Gunnar Ratsch, Koji Tsuda, editors, BMC bioinformatics, Volume 7 Suppl 1, 2007
		NIPS workshop on New Problems and Methods in Computational Biology. Gal Chechik, Christina Leslie, Gunnar Ratsch, Koji Tsuda, editors, BMC bioinformatics, Volume 7 Suppl 1, 2005
		Information, Computation and Learning. (Hebrew) Gal Chechik, Lidror Troyanski and Naftali Tishby Hebrew University Press, 2003

		Book Chapters
		Neuronal regulation and synaptic normalization. Gal Chechik, David Horn and Eytan Ruppin In M. Arbib editor, The handbook of Brain Theory and Neural networks. 2nd edition. MIT Press 2002
		I Nelken, N. Ulanovsky, L. Las, O. Bar-Yosef, M. Anderson, G. Chechik, N. Tishby and E.D. Young. Transformations of stimulus representations in the ascending auditory system. In: Auditory signal processing: physiology psychoacoustics and models, Eds. D. Pressnitzer A. de Cheveigne S. McAdams and L. Collet. Springer New York 223-229. 2004,

		PhD Thesis
		Gal Chechik, Information theoretic approach to the study of auditory coding (122 pages)		ps | pdf
		Individual chapters Abstract Introduction Extracting information from spike trains Quantifying coding interactions Redundancy reduction in the auditory pathway Extracting relevant structures Summary Appendices

		Technical reports
		Gal Chechik Types, Super types, and the mutual information distribution. Technical Report of the Leibniz Center, The Hebrew university. 2002-61

		Conference Abstracts
		Large scale online Learning of Image Similarity through ranking G. Chechik, V. Sharma, U. Shalit, S. Bengio The 4th Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2009
		M. Rehn , RF. Lyon , S. Bengio , TC. Walters and G. Chechik Sound ranking using auditory sparse-code representations Proc. ICML: Workshop on Sparse Methods for Music Audio. (2009)
		Gal Chechik Redundancy reduction in the ascending auditory system Workshop on mathematical neuroscience. Montreal, 2007.
		Gal Chechik, A. Regev, D. Koller Its about Time: Transcription Timing in the Yeast Metabolic Pathway. Bioinformatics workshop, Graybill VI conference, Fort Collins, CO, 2007.
		Gal Chechik, A. Regev, D. Koller Filling missing components in yeast metabolic pathways using heterogeneous data. Computational biology workshop at NIPS 2005, Vancouver CA.
		Sean O'Rourke, Gal Chechik and Eleazar Eskin Separation of overlapping subpopulations by mutual information. Computational biology workshop at NIPS 2005, Vancouver CA.
		Gal Chechik, A. Regev, D. Koller Filling missing components in yeast metabolic pathways using heterogeneous data. 7th BioPathways Meeting at ISMB 2005. Detroit, 2005.
		Gal Chechik Information and redundancy in the auditory system. NIPS workshop on Estimation of entropy and information of undersampled distibutions: Theory and Applications to the neural code. Organized by I. Nemenman and W. Bialek, Whistler Canada 2003.		talk-pdf
		Gal Chechik, Israel Nelken and Naftali Tishby Extracting relevant structures using side information. NATO advanced study institute, learning theory and practice, Leuven Belgium 2002.
		G. Chechik, M. Anderson, E.D. Young, N. Tishby and I. Nelken, Redundancy reduction along the ascending auditory pathway. Society For Neuroscience meeting, San Diego CA 2001.
		G. Chechik, N. Tishby, Spike time dependant plasticity and mutual information. The 9th Annual Meeting of Israeli neuroscience society, Eilat, Israel. 2000.
		G. Chechik, I. Meilijson and E. Ruppin, Effective learning requires neuronal remodeling of Hebbian synapses. Neural Computation in Science and Technology (NCST-99). Maale Hachamisha, Israel 1999.
		G. Chechik, I. Meilijson and E. Ruppin, Robust Associative Memory with Asymmetric Synaptic Learning Rules. The 8th Annual Meeting of Israeli neuroscience society Eilat, Israel (1999).
		G. Chechik, I. Meilijson and E. Ruppin, Enforcing Effective Synaptic Learning via a Neuronal Mechanism. NeuroScience letters. Supl 51. Proceedings of the 7th annual meeting of the Israeli Neuroscience Society. (1998).
		G. Chechik, I. Meilijson and E. Ruppin, Neuronal Regulation: A Mechanism For Efficient Synaptic Pruning During Brain Maturation. NeuroScience letters. Supl 51. Proceedings of the 7th annual meeting of the Israeli Neuroscience Society. (1998).