作者:我爱机器学习
原文链接:ICML历年Best Papers
ICML (Machine Learning)(1999-2016) | |||
2016 | Dueling Network Architectures for Deep Reinforcement Learning | Ziyu Wang | Google Inc. |
Pixel Recurrent Neural Networks | Aaron van den Oord | Google DeepMind | |
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling | Christopher De Sa | Stanford | |
2015 | A Nearly-Linear Time Framework for Graph-Structured Sparsity | Chinmay Hegde | Massachusetts Institute of Technology |
Optimal and Adaptive Algorithms for Online Boosting | Alina Beygelzimer | Yahoo! Research | |
2014 | Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis | Jian Tang | Peking University |
2013 | Vanishing Component Analysis | Roi Livni | The Hebrew University of Jerusalum |
Fast Semidifferential-based Submodular Function Optimization | Rishabh Iyer | University of Washington | |
2012 | Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring | Sungjin Ahn | University of California Irvine |
2011 | Computational Rationalization: The Inverse Equilibrium Problem | Kevin Waugh | Carnegie Mellon University |
2010 | Hilbert Space Embeddings of Hidden Markov Models | Le Song | Carnegie Mellon University |
2009 | Structure preserving embedding | Blake Shaw | Columbia University |
2008 | SVM Optimization: Inverse Dependence on Training Set Size | Shai Shalev-Shwartz | Toyota Technological Institute at Chicago |
2007 | Information-theoretic metric learning | Jason V. Davis | University of Texas at Austin |
2006 | Trading convexity for scalability | Ronan Collobert | NEC Labs America |
2005 | A support vector method for multivariate performance measures | Thorsten Joachims | Cornell University |
1999 | Least-Squares Temporal Difference Learning | Justin A. Boyan | NASA Ames Research Center |