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Workshop on Efficient Machine Learning
Organizers:
Samy Bengio, Corinna Cortes, Dennis DeCoste, Francois Fleuret, Ramesh Natarajan, Edwin Pednault, Dan Pelleg, Elad YomTov
Day 1
: Friday December 7, 2007
Morning session: 7:30am10:30am
7:30am:
Introduction
. S. Bengio
7:40am:
Invited talk: Efficient Learning with Sequence Kernels
. C. Cortes, Google
8:20am:
Fully Distributed EM for Very Large Datasets
. J. Wolfe, A. Haghighi, D. Klein
8:45am:
A Parallel NBody Data Mining Framework
G.F. Boyer, R.N. Riegel, A.G. Gray
9:10am:
Coffee break
9:25am:
Invited talk:
Large scale clustering and regression using the IBM Parallel Machine Learning toolbox
E. YomTov, D. Pelleg, R. Natarajan, E. Pednault, N. Slonim, IBM Research
10:05am:
Batch Performance for an Online Price
K. Crammer, M. Dredze, J. Blitzer, F. Pereira
Afternoon session: 3:30pm6:30pm
3:30pm:
Invited talk: Model compression: bagging your cake and eating it too
R. Caruana, Cornell, and D. DeCoste, Microsoft
4:10pm:
Poster Session
Fast SVD for LargeScale Matrices
, M. P. Holmes, A. G. Gray, C. L. Isbell
SVM Ocas
, V. Franc and S. Sonnenburg
A LargeScale Gaussian Belief Propagation Solver for Kernel Ridge Regression
, D. Bickson, E. YomTov, D. Dolev
Parallel support vector machine training
, K. Woodsend, J. Gondzio
5:10pm:
Invited talk:
Architecture Conscious Data Analysis: Progress and Future Outlook
S. Parthasarathy, Ohio State University
5:50pm:
Invited talk: Who is Afraid of NonConvex Loss Functions?
Y. LeCun, Courant Institute, New York University
Day 2: Saturday December 8, 2007
Morning session: 7:30am10:30am
7:30am:
Learning with Millions of Examples and Dimensions  Competition proposal
S. Sonnenburg, V. Franc
7:55am:
Large Scale Learning with String Kernels
S. Sonnenburg, K. Rieck, G. Ratsch
8:20am:
Invited talk:
Speeding up stochastic gradient descent
Y. Bengio, University of Montreal
9:00am:
Coffee break
9:25am:
Invited talk:
Stationary Features and Folded Hierarchies for Efficient Object Detection
D. Geman, John Hopkins University
10:05am:
Efficient machine learning using random projections
C. Hegde, M.A. Davenport, M.B. Wakin, R.G. Baraniuk
Afternoon session: 3:30pm6:30pm
3:30pm:
Invited talk:
New QuasiNewton Methods for Efficient LargeScale Machine Learning
S.V.N. Vishwanathan and N. N. Schraudolph, National ICT Australia
4:10pm:
LargeScale Euclidean MST and Hierarchical Clustering
, W. March, A. Gray
4:35pm:
Poster session:
Efficient Offline and Online Algorithms Training Conditional Random Fields Approximating Jacobians
, C.N. Hsu, H.S. Huang, Y.M. Chang
Online Stacked Graphical Learning
, Z. Kou, V. R. Carvalho, W. W. Cohen
High Detectionrate Cascades for RealTime Object Detection
, H. MasnadiShirazi, N. Vasconcelos
A Stochastic QuasiNewton Algorithm for NonSmooth and NonConvex Optimization
, S. Guenter, J. Yu, P. Sunehag, N. N. Schraudolph
6:00pm:
Large scale sequence labelling
A. Bordes, L. Bottou
6:25pm:
Wrapup
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Workshop on Efficient Machine Learning
Organizers:
Samy Bengio, Corinna Cortes, Dennis DeCoste, Francois Fleuret, Ramesh Natarajan, Edwin Pednault, Dan Pelleg, Elad YomTov
Day 1: Friday December 7, 2007
Morning session: 7:30am10:30am
7:30am: Introduction. S. Bengio
7:40am: Invited talk: Efficient Learning with Sequence Kernels. C. Cortes, Google
8:20am: Fully Distributed EM for Very Large Datasets. J. Wolfe, A. Haghighi, D. Klein
8:45am: A Parallel NBody Data Mining Framework G.F. Boyer, R.N. Riegel, A.G. Gray
9:10am: Coffee break
9:25am: Invited talk: Large scale clustering and regression using the IBM Parallel Machine Learning toolbox E. YomTov, D. Pelleg, R. Natarajan, E. Pednault, N. Slonim, IBM Research
10:05am: Batch Performance for an Online Price K. Crammer, M. Dredze, J. Blitzer, F. Pereira
Afternoon session: 3:30pm6:30pm
3:30pm: Invited talk: Model compression: bagging your cake and eating it too R. Caruana, Cornell, and D. DeCoste, Microsoft
4:10pm: Poster Session
5:10pm: Invited talk: Architecture Conscious Data Analysis: Progress and Future Outlook S. Parthasarathy, Ohio State University
5:50pm: Invited talk: Who is Afraid of NonConvex Loss Functions? Y. LeCun, Courant Institute, New York University
Day 2: Saturday December 8, 2007
Morning session: 7:30am10:30am
7:30am: Learning with Millions of Examples and Dimensions  Competition proposal S. Sonnenburg, V. Franc
7:55am: Large Scale Learning with String Kernels S. Sonnenburg, K. Rieck, G. Ratsch
8:20am: Invited talk: Speeding up stochastic gradient descent Y. Bengio, University of Montreal
9:00am: Coffee break
9:25am: Invited talk: Stationary Features and Folded Hierarchies for Efficient Object Detection D. Geman, John Hopkins University
10:05am: Efficient machine learning using random projections C. Hegde, M.A. Davenport, M.B. Wakin, R.G. Baraniuk
Afternoon session: 3:30pm6:30pm
3:30pm: Invited talk: New QuasiNewton Methods for Efficient LargeScale Machine Learning S.V.N. Vishwanathan and N. N. Schraudolph, National ICT Australia
4:10pm: LargeScale Euclidean MST and Hierarchical Clustering, W. March, A. Gray
4:35pm: Poster session:
6:00pm: Large scale sequence labelling A. Bordes, L. Bottou
6:25pm: Wrapup