ICML '07 - Corvallis

ICML 2007 - The 24th Annual International Conference on Machine Learning

The 24th Annual International Conference on Machine Learning is being held in conjunction with the 2007 International Conference on Inductive Logic Programming at Oregon State University in Corvallis, Oregon. As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. LEARN MORE FROM THE VIDEOS!

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Tutorials Workshop


Opening
851 views, 09:47 Introduction to the ICML07 ConferenceIntroduction to the ICML07 Conference Zoubin Ghahramani Zoubin Ghahramani
Panel
917 views, 1:12:05 ILP Invited Panel - Structured Machine Learning: The Next 10 YearsILP Invited Panel - Structured Machine Learning: The Next 10 ... Lise Getoor, Bernhard Pfahringer, et al. Lise Getoor, Bernhard Pfahringer, Pedro Domingos, Thomas Dietterich, Stephen Muggleton 4 comments	[syn] 53 views, 03:10 Introduction to the panelIntroduction to the panel Prasad Tadepalli Prasad Tadepalli	245 views, 15:38 DebateDebate Lise Getoor, Bernhard Pfahringer, et al. Lise Getoor, Bernhard Pfahringer, Pedro Domingos, Thomas Dietterich, Prasad Tadepalli	1990 views, 10:50 Ten problems for the next 10 yearsTen problems for the next 10 years Pedro Domingos Pedro Domingos 1 comment	527 views, 11:24 Declarative Vs. ProceduralDeclarative Vs. Procedural Thomas Dietterich Thomas Dietterich
44 views, 07:31 Reunited the splinter groups into one big relevanr forceReunited the splinter groups into one big relevanr force Bernhard Pfahringer Bernhard Pfahringer	[syn] 603 views, 10:52 SRL - The next decadeSRL - The next decade Lise Getoor Lise Getoor	266 views, 13:33 The next 10 years of ILPThe next 10 years of ILP Stephen Muggleton Stephen Muggleton
Invited talks
[syn] 2271 views, 57:29 invited talk Kernel Tricks, Means and EndsKernel Tricks, Means and Ends Bernhard Schölkopf Bernhard Schölkopf	[syn] 3364 views, 1:14:13 invited talk Bayesian models of human inductive learningBayesian models of human inductive learning Josh Tenenbaum Josh Tenenbaum 3 comments	[syn] 855 views, 52:52 invited talk Graphical Models for HIV Vaccine DesignGraphical Models for HIV Vaccine Design David Heckerman David Heckerman
Metric Learning
[syn] 1924 views, 23:17 best paper Best Paper - Information-Theoretic Metric LearningBest Paper - Information-Theoretic Metric Learning Brian Kulis Brian Kulis	441 views, 20:41 Learning Distance Function by Coding SimilarityLearning Distance Function by Coding Similarity Rioe Kliper Rioe Kliper 2 comments	[syn] 393 views, 21:57 A Transductive Framework of Distance Metric Learning by Spectral Dimensionality ReductionA Transductive Framework of Distance Metric Learning by Spectral Dimensionality ... Fuxin Li Fuxin Li	[syn] 297 views, 19:33 Dirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional DataDirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional ... Hua-Yan Wang Hua-Yan Wang
Relational Learning and ILR
[syn] 1184 views, 26:17 Bias/variance analysis of relational domainsBias/variance analysis of relational domains Jennifer Neville Jennifer Neville 3 comments	[syn] 450 views, 27:47 Learning Probabilistic Stochastic Models from Probabilistic ExamplesLearning Probabilistic Stochastic Models from Probabilistic Examples Stephen Muggleton Stephen Muggleton	[syn] 105 views, 24:23 Learning from Interpretations: A Rooted Kernel for Ordered HypergraphsLearning from Interpretations: A Rooted Kernel for Ordered Hypergraphs Gabriel Wachman Gabriel Wachman	[syn] 310 views, 22:10 Statistical Predicate InventionStatistical Predicate Invention Stanley Kok Stanley Kok
Networks and Graphs
[syn] 787 views, 26:07 Scalable Modeling of Real Graphs using Kronecker MultiplicationScalable Modeling of Real Graphs using Kronecker Multiplication Jure Leskovec Jure Leskovec	[syn] 262 views, 23:34 Recovering Temporally Rewiring Networks: A model-based approachRecovering Temporally Rewiring Networks: A model-based approach Fan Guo Fan Guo	[syn] 149 views, 15:59 Entire Regularization Paths for Graph DataEntire Regularization Paths for Graph Data Koji Tsuda Koji Tsuda 1 comment	[syn] 918 views, 22:01 Graph Clustering With Network Structure IndicesGraph Clustering With Network Structure Indices Matthew J. Rattigan Matthew J. Rattigan
Vision, Graphics and Robotics
[syn] 1012 views, 25:25 Learning to Compress Images and VideoLearning to Compress Images and Video Li Cheng Li Cheng	[syn] 1321 views, 25:17 Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold LearningAdaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold ... Sridhar Mahadevan Sridhar Mahadevan	[syn] 452 views, 21:24 Map Building without Localization by Dimensionality Reduction TechniquesMap Building without Localization by Dimensionality Reduction Techniques Takehisa Yairi Takehisa Yairi
Large-scale Optimization
[syn] 459 views, 23:19 Scalable Training of L1-regularized Log-linear ModelsScalable Training of L1-regularized Log-linear Models Galen Andrew Galen Andrew	588 views, 19:35 Support Cluster MachineSupport Cluster Machine Bin Li Bin Li	[syn] 616 views, 19:24 Trust Region Newton Methods for Large-Scale Logistic RegressionTrust Region Newton Methods for Large-Scale Logistic Regression Chin Jen Lin Chin Jen Lin	[syn] 147 views, 22:25 Large-scale RLSC Learning Without AgonyLarge-scale RLSC Learning Without Agony Wenye Li Wenye Li
Language, Topic Modelling and Hierarchies
[syn] 429 views, 22:43 Unsupervised Prediction of Citation InfluencesUnsupervised Prediction of Citation Influences Laura Dietz Laura Dietz	418 views, 22:13 Three New Graphical Models for Statistical Language ModellingThree New Graphical Models for Statistical Language Modelling Andriy Mnih Andriy Mnih	272 views, 19:59 Mixtures of Hierarchical Topics with Pachinko Allo cationMixtures of Hierarchical Topics with Pachinko Allo cation David Mimno David Mimno	[syn] 206 views, 20:39 Unsupervised Estimation for Noisy-Channel ModelsUnsupervised Estimation for Noisy-Channel Models Markos Mylonakis Markos Mylonakis	[syn] 836 views, 20:54 Hierarchical Maximum Entropy Density EstimationHierarchical Maximum Entropy Density Estimation Miroslav Dudík Miroslav Dudík
Metric Learning II
[syn] 123 views, 25:56 Learning for Efficient Retrieval of Structured Data with Noisy QueriesLearning for Efficient Retrieval of Structured Data with Noisy Queries Charles Parker Charles Parker	[syn] 183 views, 24:28 Learning to Combine Distances for Complex RepresentationsLearning to Combine Distances for Complex Representations Adam Woznica Adam Woznica 1 comment	[syn] 251 views, 18:25 Optimal Dimensionality of Metric Space for ClassificationOptimal Dimensionality of Metric Space for Classification Xiangyang Xue Xiangyang Xue	[syn] 874 views, 23:30 Non-Isometric Manifold Learning: Analysis and an AlgorithmNon-Isometric Manifold Learning: Analysis and an Algorithm Piotr Dollár Piotr Dollár	[syn] 253 views, 21:37 Manifold-adaptive dimension estimationManifold-adaptive dimension estimation Amir-massoud Farahmand Amir-massoud Farahmand
532 views, 18:09 Adaptive Dimension Reduction Using Discriminant Analysis and K-means ClusteringAdaptive Dimension Reduction Using Discriminant Analysis and K-means Clustering Shuiwang Ji Shuiwang Ji	432 views, 17:51 Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian EigenmapseRobust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmapse Samuel Gerber Samuel Gerber

Reviews and comments:

1 arturo gallegos, July 2, 2007 at 10:14 p.m.:

somos de monterrey n.l. nesecitamos ayuda para el transporte a los angeles somos un grupo de ciegos y deviles visuales

2 reza, July 4, 2007 at 2:35 a.m.:

hi there,

many thanks for making the videos available. i would appreciate if you also put the tutorial on "group theoretic approaches to machine learning" by risi kondor.

best regards,

3 Michael Rabbat, October 25, 2007 at 11:03 p.m.:

For what it's worth, I'll second the request for Risi Kondor's tutorial. Thanks!

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ICML '07 - Corvallis

ICML 2007 - The 24th Annual International Conference on Machine Learning

Event sections

Opening

Panel

Invited talks

Metric Learning

Relational Learning and ILR

Networks and Graphs

Vision, Graphics and Robotics

Large-scale Optimization

Language, Topic Modelling and Hierarchies

Metric Learning II

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