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  • International Conference on Machine Learning 2014

International Conference on Machine Learning 2014

TechTalks from event: International Conference on Machine Learning 2014

Session : June 22 pm2 - Track B - Learning Theory I

  • Concentration in unbounded metric spaces and algorithmic stability Authors: Aryeh Kontorovich
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  • Heavy-tailed regression with a generalized median-of-means Authors: Daniel Hsu; Sivan Sabato
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  • Learnability of the Superset Label Learning Problem Authors: Liping Liu; Thomas Dietterich
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  • Maximum Margin Multiclass Nearest Neighbors Authors: Aryeh Kontorovich; Roi Weiss
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  • Sample Efficient Reinforcement Learning with Gaussian Processes Authors: Robert Grande; Thomas Walsh; Jonathan How
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  • Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations Authors: Timothy Mann; Shie Mannor
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  • All Sessions
    • Session : June 22 - Keynote
    • Session : June 23 - Keynote
    • Session : June 24 - Keynote
    • Session : June 21 - Tutorial 1
    • Session : June 21 - Tutorial 2
    • Session : June 21 - Tutorial 3
    • Session : June 21 - Tutorial 4
    • Session : June 21 - Tutorial 6
    • Session : June 22 am - Track A - Networks and Graph-Based Learning I
    • Session : June 22 am - Track B - Reinforcement Learning I
    • Session : June 22 am - Track C - Bayesian Optimization and Gaussian Processes
    • Session : June 22 am - Track D - PCA and Subspace Models
    • Session : June 22 am - Track E - Supervised Learning
    • Session : June 22 am - Track F - Neural Networks and Deep Learning I
    • Session : June 22 pm1 - Track A - Graphical Models I
    • Session : June 22 pm1 - Track B - Bandits I
    • Session : June 22 pm1 - Track C - Monte Carlo
    • Session : June 22 pm1 - Track D - Statistical Methods
    • Session : June 22 pm1 - Track E - Structured Prediction
    • Session : June 22 pm1 - Track F - Deep Learning and Vision
    • Session : June 22 pm2 - Track A - Matrix Completion and Graphs
    • Session : June 22 pm2 - Track B - Learning Theory I
    • Session : June 22 pm2 - Track C - Clustering and Nonparametrics
    • Session : June 22 pm2 - Track D - Active Learning
    • Session : June 22 pm2 - Track E - Optimization I
    • Session : June 22 pm2 - Track F - Large-Scale Learning
    • Session : June 23 am - Track A - Latent Variable Models
    • Session : June 23 am - Track B - Online Learning and Planning
    • Session : June 23 am - Track C - Clustering
    • Session : June 23 am - Track D - Metric Learning and Feature Selection
    • Session : June 23 am - Track E - Optimization II
    • Session : June 23 am - Track F - Neural Language and Speech
    • Session : June 23 pm1 - Track A - Graphical Models and Approximate Inference
    • Session : June 23 pm1 - Track B - Online Learning I
    • Session : June 23 pm1 - Track C - Monte Carlo and Approximate Inference
    • Session : June 23 pm1 - Track D - Method-Of-Moments and Spectral Methods
    • Session : June 23 pm1 - Track E - Boosting and Ensemble Methods
    • Session : June 23 pm1 - Track F - Neural Networks and Deep Learning II
    • Session : June 23 pm2 - Track A - Matrix Factorization I
    • Session : June 23 pm2 - Track B - Learning Theory II
    • Session : June 23 pm2 - Track C - Nonparametric Bayes I
    • Session : June 23 pm2 - Track D - Manifolds
    • Session : June 23 pm2 - Track E - Kernel Methods I
    • Session : June 23 pm2 - Track F - Unsupervised Learning and Detection
    • Session : June 24 am - Track A - Matrix Factorization II
    • Session : June 24 am - Track B - Bandits II
    • Session : June 24 am - Track C - Crowd-Sourcing
    • Session : June 24 am - Track D - Manifolds and Graphs
    • Session : June 24 am - Track E - Regularization and Lasso
    • Session : June 24 am - Track F - Nearest-Neighbors and Large-Scale Learning
    • Session : June 24 pm1 - Track A - Graphical Models II
    • Session : June 24 pm1 - Track B - Reinforcement Learning II
    • Session : June 24 pm1 - Track C - Topic Models
    • Session : June 24 pm1 - Track D - Sparsity
    • Session : June 24 pm1 - Track E - Kernel Methods II
    • Session : June 24 pm1 - Track F - Neural Theory and Spectral Methods
    • Session : June 24 pm2 - Track A - Networks and Graph-Based Learning II
    • Session : June 24 pm2 - Track B - Online Learning II
    • Session : June 24 pm2 - Track C - Nonparametric Bayes II
    • Session : June 24 pm2 - Track D - Features and Feature Selection
    • Session : June 24 pm2 - Track E - Optimization III
    • Session : June 24 pm2 - Track F - Time Series and Sequences

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