Gal Chechik joined Google in 2007 after working as a research affiliate in the Stanford AI lab with Daphne Koller, where he studied computational biology models of molecular networks. Before that, he earned his PhD from the Hebrew University, working on machine learning approaches to analyze neural coding in the auditory system. See his Stanford page and list of publications.
An Online Algorithm for Large Scale Image Similarity Learning, Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio, Advances in Neural Information Processing Systems, 2009 (to appear).
Large Scale Online Learning of Image Similarity Through Ranking: Extended Abstract, Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio, 4th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA, 2009.
Large Scale Online Learning of Image Similarity Through Ranking, Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio, Journal of Machine Learning Research (JMLR) (2009) (to appear).
Sound Ranking Using Auditory Sparse-Code Representations, Martin Rehn, Richard F. Lyon, Samy Bengio, Thomas C. Walters, Gal Chechik, ICML 2009 Workshop on Sparse Method for Music Audio.
Timing properties of gene expression responses to environmental changes, Gal Chechik, Daphne Koller, J. Computational Biology, vol. 9 (2009).
Activity Motifs Reveal Principles of Timing in Transcriptional Control of the Yeast Metabolic Network, Gal Chechik, Eugene Oh, Oliver Rando, Jonathan Weissman, Aviv Regev, Daphne Koller, Nature Biotechnology, vol. 26 (11) (2008), pp. 1251-1259.
Large Scale Content-Based Audio Retrieval from Text Queries, Gal Chechik, Eugene Ie, Martin Rehn, Samy Bengio, Richard F. Lyon, ACM International Conference on Multimedia Information Retrieval (MIR), 2008.
Max-margin classification of data with absent features, G. Chechik, G. Heitz, G. Elidan, P. Abbeel, D. Koller, Journal of Machine Learning Research, vol. 9 (2008), pp. 1-21.
Euclidean Embedding of Co-occurrence Data, Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby, Journal of Machine Learning Research, vol. 8 (2007), pp. 2265-2295.
Information theory in auditory research, I. Nelken, G. Chechik, Hearing Research, vol. 229 (2007), pp. 94-105.
Max-margin classification of incomplete data, G. Chechik, G. Heitz, G. Elidan, P. Abbeel, D. Koller, Advances in Neural Information Processing Systems: Proceedings of the 2006 Conference (2007).
NIPS workshop on New Problems and Methods in Computational Biology, G. Chechik, C. Leslie, W.S. Noble, G. R\, Q. Morris, K. Tsuda, BMC Bioinformatics, vol. 8 (2007), S1.
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks, A. Battle, G. Chechik, D. Koller, Advances in Neural Information Processing Systems: Proceedings of the 2006 Conference, 2007.
Tuned protein variability in yeast metabolism, G. Chechik, M. Chen, D. Koller, 2007.
Discrete profile comparison using information bottleneck, Sean O'rourke, Gal Chechik, Robin Friedman, Elazar Eskin, BMC Bioinformatics, vol. 7 (2006).
Embedding Heterogeneous Data Using Statistical Models, Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby, AAAI, 2006.
Discrete profile comparison using information bottleneck, O.R. Sean, G. Chechik, R. Friedman, E. Eskin, BMC Bioinformatics, vol. 7 (2006), S8.
Reduction of Information Redundancy in the Ascending Auditory Pathway, G. Chechik, M.J. Anderson, O. Bar-Yosef, E.D. Young, N. Tishby, I. Nelken, Neuron, vol. 51 (2006), pp. 359-368.
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks, A. Battle, G. Chechik, D. Koller, Human Brain Mapping: Proceedings of the 2006 Conference.
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, J.W.H. Schnupp, Journal of Computational Neuroscience, vol. 19 (2005), pp. 199-221.
Extracting Continuous Relevant Features, A. Globerson, G. Chechik, N. Tishby, Innovations in Classification, Data Science, and Information Systems: Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V. (2005).
Gaussian information bottleneck, G. Chechik, A. Globerson, N. Tishby, Y. Weiss, Journal of Machine Learning Research, vol. 6 (2005), pp. 165-188.
Information Bottleneck for Gaussian Variables, G. Chechik, A. Globerson, N. Tishby, Y. Weiss, The Journal of Machine Learning Research, vol. 6 (2005), pp. 165-188.
Separation of overlapping subpopulations by mutual information, S. O���Rourke, G. Chechik, E. Eskin, Proc. NIPS Workshop Comput. Biol. Anal. Heterogeneous Data (2005).
Euclidean Embedding of Co-Occurrence Data, Amir Globerson, Gal Chechik, Fernando C. Pereira, Naftali Tishby, Advances in Neural Information Processing Systems (2004), pp. 497-504.
Information Bottleneck for Gaussian Variables, Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss, Advances in Neural Information Processing Systems 16, 2004.
A needle in a haystack: local one-class optimization, K. Crammer, G. Chechik, Proceedings of the twenty-first international conference on Machine learning (2004).
Sufficient Dimensionality Reduction with Irrelevant Statistics, Amir Globerson, Gal Chechik, Naftali Tishby, Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), 2003, pp. 281-288.
An Information Theoretic Approach to the Study of Auditory Coding, G. Chechik, 2003.
Are there representations in embodied evolved agents? taking measures, H. Avraham, G. Chechik, E. Ruppin, Advances in Artificial Life-Proceedings of the 7th European Conference on Artificial Life (2003).
Extracting relevant structures with side information, G. Chechik, N. Tishby, Advances in Neural Information Processing Systems 15, 2003, pp. 857-864.
Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization, G. Chechik, Neural Computation, vol. 15 (2003), pp. 1481-1510.
Sufficient dimensionality reduction with irrelevance statistics, A. Globerson, G. Chechik, N. Tishby, Proceeding of the 19th Conference on Uncertainty in Artificial Intelligence, Acapulco, Mexico (2003).
Transformation of stimulus representation in the ascending auditory system, I. Nelken, N. Ulanovsky, L. Las, O. Bar-Yosef, M. Anderson, G. Chechik, N. Tishby, E.D. Young, Auditory signal processing: physiology, psychoacoustics and models. Edited by Pressnitzer D, de Cheveigne A, McAdams S, Collet L. New York: Springer Verlag (2003), pp. 358-416.
Types, super-types and the mutual information distribution, G. Chechik, 2003.
Group redundancy measures reveal redundancy reduction in the auditory pathway, G. Chechik, A. Globerson, MJ Anderson, ED Young, I. Nelken, N. Tishby, Advances in Neural Information Processing Systems 14: Proceedings of the 2002 Conference, pp. 173-180.
Spike timing dependent plasticity and mutual information in spiking neurons, Gal Chechik, Neurocomputing, vol. 38-40 (2001), pp. 147-152.
Distributional clustering of movements based on neural responses, A. Globerson, G. Chechik, N. Tishby, O. Steinberg, E. Vaadia, 2001.
Effective Neuronal Learning with Ineffective Hebbian Learning Rules, G. Chechik, I. Meilijson, E. Ruppin, Neural Computation, vol. 13 (2001), pp. 817-840.
Neuronal Regulation and Hebbian Learning, G. Chechik, D. Horn, E. Ruppin, The handbook of brain theory and neural networks. 2nd Edition, 2000.
Neuronal normalization provides effective learning through ineffective synaptic learning rules, G. Chechik, I. Meilijson, E. Ruppin, Neurocomputing, vol. 32 (2000), pp. 345-351.
Neuronal regulation: A biologically plausible mechanism for efficient syanptic prunning in development, Gal Chechik, Isaac Meilijson, Eyten Ruppin, Neurocomputing, vol. 26-27 (1999), pp. 633-639.
Neuronal Regulation Implements Efficient Synaptic Pruning, G. Chechik, I. Meilijson, E. Ruppin, Proceedings of the 1998 conference on Advances in neural information processing systems II table of contents (1999), pp. 97-103.
Neuronal Regulation: A Mechanism for Synaptic Pruning During Brain Maturation, G. Chechik, I. Meilijson, E. Ruppin, Neural Computation, vol. 11 (1999), pp. 2061-2080.
Synaptic Pruning in Development: A Computational Account, G. Chechik, I. Meilijson, E. Ruppin, Neural Computation, vol. 10 (1998), pp. 1759-1777.
Synaptic pruning during development: A novel account in neural terms, G. Chechik, I. Meilijson, E. Ruppin, Sixth Annual Computational Neuroscience Meeting.(CNS97), Big Ski, Montana, 1997.
Effective Learning Requires Neuronal Remodeling of Hebbian Synapses, G. Chechik, I. Meilijson, E. Ruppin, Proceedings of the 1999 conference on Advances in neural information processing systems II table of contents.
Information bottleneck and linear projections of Gaussian processes, G. Chechik, A. Globerson.
Temporally Dependent Plasticity: An Information Theoretic Account, G. Chechik, N. Tishby, Advances in Neural Information Processing Systems: Proceedings of the 2000 Conference.