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  • Academy Events

  • 3rd Annual Machine Learning Symposium

    Friday, October 10, 2008 | 10:00 AM - 4:30 PM
    The New York Academy of Sciences

    Presented by the Machine Learning Discussion Group

      • Registration Closed

    This is the third symposium on Machine Learning at the New York Academy of Sciences. The aim of these series of symposia is to build a community of scientists in machine learning from the NYC area's academic, government, and industrial institutions by convening and promoting the exchange of ideas in a neutral setting.

    Steering Committee

    • Corinna Cortes, PhD, Google, Inc.
    • Tony Jebara, PhD, Columbia University
    • John Langford, PhD, Yahoo Research
    • Michael L. Littman, PhD, Rutgers University
    • Mehryar Mohri, PhD, Courant Institute of Mathematical Sciences
    • Robert Schapire, PhD, Princeton University
    • David Waltz, PhD, Columbia University

     


    Schedule

     9:30 AM   Coffee and Poster Set-Up

    10:00 AM   Opening Remarks

    10:15 AM   Edo Airoldi, Troyanskaya Lab, Princeton University

    11:00 AM   Dana Angluin, Yale University

    11:45 AM   Graduate Student Talks

              Corinna Cortes, Mehryar Mohri, Michael Riley and Afshin Rostamizadeh, Google Research

              Koby Crammer, Eyal Even-Dar, Yishay Mansour and Jennifer Wortman, University of Pennsylvania

              Sina Jafarpour, Princeton University

              Lihong Li and Thomas J. Walsh, Rutgers University

              Piotr W. Mirowski, Yann LeCun, Deepak Madhavan and Ruben Kuzniecky, Courant Institute of Mathematical Science

              Mehryar Mohri and Ameet Talwalkar, Courant Institute of Mathematical Sciences

              Indraneel Mukherjee and Robert E. Schapire, Princeton University

              Anil Raj and Chris H. Wiggins, Columbia University

              Victor S. Sheng, Foster Provost and Panagiotis G. Ipeirotis, New York University

              Pannagadatta K. Shivaswamy and Tony Jebara, Columbia University

    12:45 PM  Lunch & Poster Session

    2:30 PM   Tony Jebara, Columbia University

    3:15 PM   Robert Kleinberg, Cornell University

    4:00 PM   Student Award Winner Announcement & Closing Remarks

     


    Speaker Abstracts

    A Statistical Perspective on Cellular Growth
    Edo Airoldi, Princeton University

    Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this talk, we will introduce statistical and computational methods to identify quantitative aspects of the regulatory mechanisms underlying cell proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes accurately predict the instantaneous growth rate of any cellular culture, robust to changing biological conditions, experimental methods, and technological platforms. Our model also predicts growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution.

    We investigate the biological significance of the identified gene expression signature from multiple perspectives: by perturbing the regulatory network through the Ras/ cAMP/PKA pathway, observing strong up-regulation of growth rate even in the absence of appropriate nutrients, and by discovering potential transcription factor binding sites enriched in growth-correlated genes. Most importantly, statistical and computational methods enable substantive biological insights about growth at instantaneous time scales inaccessible by direct experimental methods.

    Value Injection Queries for Circuit Learning
    Dana Angluin, Yale University

    We survey results on algorithms to learn Boolean, analog and probabilistic circuits using value injection queries. A value injection query is a kind of enhanced membership query, in which we may control the values on interior wires, as well as on input wires of the circuit, but still may only observe the values on output wire(s) of the circuit. This type of query is inspired by the capabilities of gene suppression and gene over-expression in studying the structure of gene regulatory networks.

    We consider the theoretical power of such queries in learning Boolean circuits, where we give polynomial time algorithms to learn circuits with bounded fan-in and logarithmic depth, as well as unbounded fan-in constant depth circuits over AND, OR and NOT. For analog circuits, a topological parameter, the shortcut width of the circuit, turns out to be a key to its efficient learnability. Finally, for probabilistic circuits (equivalently, Bayesian networks) we can generalize the Boolean case for 0/1 values, but we also encounter novel phenomena. This talk describes joint work with James Aspnes, Jiang Chen, David Eisenstat, Lev Reyzin, and Yinghua Wu; relevant papers may be found on the webpage of James

    Embedding, Clustering and Matching with Graphs of GPS Data

    Tony Jebara, Columbia University

    Many machine learning tasks can naturally be framed as problems on graphs. These tasks include dimensionality reduction, clustering and classification. I will describe matching algorithms that recover graphs from data, minimum volume embedding algorithms that recover low dimensional visualizations from graphs and new spectral algorithms that partition graphs into pieces.

    At Sense Networks, we have been building graphs from spatio-temporal location data from many GPS equipped phones and devices. One example is a graph or network of places in the city that shows similarity between different locations and how active they are right now. Sense also builds a network of users showing how similar person X is to person Y by comparing their movement trails or histories. Embedding and clustering these graphs reveals interesting trends in behavior and tribes of people that are far more detailed than traditional census demographics. With machine learning algorithms applied to these human activity graphs, it becomes possible to make predictions for advertising, marketing and collaborative recommendation.

    Multi-Armed Bandit Problems in Metric Spaces

    Robert Kleinberg, Cornell University

    Multi-armed bandit problems constitute a well-studied abstraction of the exploration/exploitation tradeoffs inherent in many sequential decision making problems. A broad range of computing applications require bandit algorithms with a large but structured set of alternatives. Often this structure takes the form of a metric: a distance function expressing the decision-maker's prior knowledge that certain alternatives will have similar payoffs. This talk focuses on two such applications, one in electronic commerce and the other in web advertising. We will show how both applications can be formulated as special cases of a general problem, the "Lipschitz multi-armed bandit problem," which generalizes the classical multi-armed bandit problem by allowing for a large (possibly uncountable) decision set comprising the points of a metric space. We will define an invariant that precisely determines the performance of the best possible algorithm for this problem in a given metric, and we will describe an algorithm that meets this bound. This is joint work with Alex Slivkins and Eli Upfal.


    Posters

    Hierarchial Bayesian Models of Categorical Data Annotation
    Bob Carpenter, Alias-I Inc.

    Sparse Regression and Model Degeneracy in fMRI
    Melissa K. Carroll, Guillermo A. Cecchi, Irina Rish, Rahul Garg and A. Ravi Rao, Princeton University

    Automatically Extracting Social Networks from Unstructured Text
    Jonathan Chang, Jordan Boyd-Graber and David M. Blei, Princeton University

    Sample Selection Bias Correction Theory
    Corinna Cortes, Mehryar Mohri, Michael Riley and Afshin Rostamizadeh, Google Research

    Regret Minimization with Concept Drift
    Koby Crammer, Eyal Even-Dar, Yishay Mansour and Jennifer Wortman, University of Pennsylvania

    Ranking Electrical Feeders of the New York Power Grid
    Philip Gross, Ansaf Salleb-Aouissi, Haimonti Dutta and Albert Boulanger, Columbia University

    Automatically Marking Houses in Rural Satellite Images of UN Millennium Villages in Africa
    Roy Han, Columbia University

    Large Margin Transformation Learning
    Andrew G. Howard and Tony Jebara, Columbia University

    Learning Directly from Compressed Sensed Data, Maching Learning and Compressed Sensing Benefits
    Sina Jafarpour, Princeton University

    Scaling Up Linear SVM Classifiers Using Confidence-Based Boosting, A Theoretical Analysis Based on Rademacher Complexity
    Sina Jafarpour, Princeton University

    Learning Animal Movement Models and Location Estimates Using HMMs
    Berk Kapicioglu, Robert E. Schapire, Martin Wikelski and Tamara Broderick, Princeton University

    High-Performance Analysis of Sequences
    Pavel Kuksa, Pai-Hsi Huang and Vladimir Pavlovic, Rutgers University

    Fast Feature Selection for Reinforcement-Learning-Based Spoken Dialog Management: A Case Study
    Lihong Li, Jason D. Williams and Suhrid Balakrishnan, Rutgers University

    Knows What It Knows: A Framework for Self-Aware Learning
    Lihong Li and Thomas J. Walsh, Rutgers University

    Learning Regulatory Motifs from Gene Expression Trajectories Using Graph-Regularized Partial Least Square Regression
    Xuejing Li, Chris H. Wiggins, Valerie Reinke and Christina Leslie, Columbia University

    Reducing Statistical Dependencies in Natural Images Using Radial Gaussianization
    Siwei Lyu and Eero P. Simoncelli, University at Albany, SUNY

    Comparing SVM and Convolutional Networks for Epileptic Seizure Prediction from EEG
    Piotr W. Mirowski, Yann LeCun, Deepak Madhavan and Ruben Kuzniecky, Courant Institute of Mathematical Sciences

    A Dynamical Factor Graph with Latent Variables for Time Series Prediction
    Piotr W. Mirowski and Yann LeCun, Courant Institute of Mathematical Sciences

    Improved Bounds for the Nyström Method
    Mehryar Mohri and Ameet Talwalkar, Courant Institute of Mathematical Sciences

    Learning with Continuous Experts Using Drifting Games
    Indraneel Mukherjee and Robert E. Schapire, Princeton University

    PAC-MDP Reinforcement Learning with Bayesian Priors
    Ali Nouri and Lihong Li, Rutgers University

    An Information-Theoretic Derivation of Min-Cut Based Graph Partitioning
    Anil Raj and Chris H. Wiggins, Columbia University

    Mining Retail Data for Targeting Customers with Headroom
    Madhu Shashanka and Michael Giering, Mars Inc.

    Graph Embedding with Global Structure Preserving Constraints
    Blake Shaw and Tony Jabara, Columbia University

    Improving Data Quality and Data Mining Using Multiple, Noisy Labelers
    Victor S. Sheng, Foster Provost and Panagiotis G. Ipeirotis, New York University

    A Heuristic to Enable Auditing Decisions in Travel & Entertainment Expense Management
    Anshul Sheopuri, Jose Gomes, Sai Zeng, Paolina Centonze and Ioana Boier-Martin, IBM T J Watson Rsearch Center

    Relative Margin Machines
    Pannagadatta K. Shivaswamy and Tony Jebara, Columbia University

    Efficient Learning of Action Schemas and Web-Service Descriptions
    Thomas J. Walsh, Rutgers University

     


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