Machine Learning Group - Talks

Temas

  1. (S)(4) Latent Maximum Margin Clustering by H. Guang-Tong Zhou, Tian Lan, Arash Vahdat, and Greg Mori
  2. (0) What do row and column marginals reveal about your dataset? by Behzad Golshan, John W. Byers, Evimaria Terzi
  3. (S)(5) Stochastic variational inference by Hoffman, Wang, Blei and Paisley
  4. (2) Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Welling
  5. (S)(6) Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams
  6. (S)(4) Kernel Bayes Rule by Fukumizu, Song, Gretton
  7. (S)(6) Representation Learning: A Review and New Perspectives by Yoshua Bengio, Aaron Courville, Pascal Vincent
  8. (S)(4) On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan,Shivani Agarwal
  9. (1) Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
  10. (1) BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit Dhillon, Pradeep Ravikumar, Russell Poldrack
  11. (1) A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
  12. (S)(5) Learning with Noisy Labels Nagarajan Natarajan, Inderjit Dhillon, Pradeep Ravikumar, Ambuj Tewari
  13. (3) Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
  14. (S)(4) Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
  15. (3) Statistical Active Learning Algorithms Maria-Florina Balcan, Vitaly Feldman
  16. (1) Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh
  17. (2) (More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Dan Russo, Benjamin Van Roy
  18. (1) Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
  19. (1) Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
  20. (2) Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri
  21. (S)(4) Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen
  22. (1)Understanding Dropout Pierre Baldi, Peter J. Sadowski

© Miguel Lázaro-Gredilla
Last modified: 2014-02-23, 03:05