| Relative Deviation Margin Bounds | 0 | 0.34 | 2021 |
| A Discriminative Technique For Multiple-Source Adaptation | 0 | 0.34 | 2021 |
| Agnostic Learning with Multiple Objectives | 0 | 0.34 | 2020 |
| Online Learning with Dependent Stochastic Feedback Graphs | 0 | 0.34 | 2020 |
| Adaptive Region-Based Active Learning | 0 | 0.34 | 2020 |
| Active Learning with Disagreement Graphs | 0 | 0.34 | 2019 |
| Regularized Gradient Boosting | 0 | 0.34 | 2019 |
| AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles | 0 | 0.34 | 2019 |
| Online Learning with Sleeping Experts and Feedback Graphs | 0 | 0.34 | 2019 |
| Adaptation Based on Generalized Discrepancy. | 1 | 0.35 | 2019 |
| Learning GANs and Ensembles Using Discrepancy | 0 | 0.34 | 2019 |
| On-line Learning with Abstention. | 3 | 0.48 | 2018 |
| Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses. | 0 | 0.34 | 2018 |
| Online Non-Additive Path Learning under Full and Partial Information. | 0 | 0.34 | 2018 |
| AdaNet: Adaptive Structural Learning of Artificial Neural Networks. | 23 | 0.88 | 2017 |
| Multi-Armed Bandits with Non-Stationary Rewards. | 0 | 0.34 | 2017 |
| Structured Prediction Theory Based on Factor Graph Complexity. | 0 | 0.34 | 2016 |
| Structured Prediction Theory and Voted Risk Minimization. | 0 | 0.34 | 2016 |
| Learning with Rejection. | 16 | 0.87 | 2016 |
| Boosting with Abstention. | 0 | 0.34 | 2016 |
| Structural Maxent Models | 0 | 0.34 | 2015 |
| On-Line Learning Algorithms for Path Experts with Non-Additive Losses | 2 | 0.40 | 2015 |
| Voted Kernel Regularization. | 0 | 0.34 | 2015 |
| Kernel Extraction via Voted Risk Minimization | 1 | 0.36 | 2015 |
| Ensemble Methods for Structured Prediction. | 9 | 0.57 | 2014 |
| Adaptation Algorithm and Theory Based on Generalized Discrepancy. | 5 | 0.40 | 2014 |
| Domain adaptation and sample bias correction theory and algorithm for regression | 33 | 1.36 | 2014 |
| Learning Ensembles Of Structured Prediction Rules | 1 | 0.37 | 2014 |
| Deep Boosting. | 0 | 0.34 | 2014 |
| Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions. | 7 | 0.53 | 2013 |
| Multi-Class Classification with Maximum Margin Multiple Kernel. | 11 | 0.55 | 2013 |
| Learning Kernels Using Local Rademacher Complexity. | 26 | 0.72 | 2013 |
| Accuracy at the Top. | 22 | 0.85 | 2012 |
| Ensembles of Kernel Predictors | 8 | 0.73 | 2012 |
| L2 regularization for learning kernels | 31 | 1.06 | 2012 |
| Domain adaptation in regression | 14 | 0.79 | 2011 |
| A Dual Coordinate Descent Algorithm For Svms Combined With Rational Kernels | 1 | 0.35 | 2011 |
| Half Transductive Ranking | 0 | 0.34 | 2010 |
| Half Transductive Ranking | 2 | 0.66 | 2010 |
| Two-Stage Learning Kernel Algorithms | 80 | 2.18 | 2010 |
| On the Impact of Kernel Approximation on Learning Accuracy | 0 | 0.34 | 2010 |
| On the Impact of Kernel Approximation on Learning Accuracy | 51 | 1.75 | 2010 |
| Large-scale training of SVMs with automata kernels | 1 | 0.37 | 2010 |
| Learning Bounds for Importance Weighting. | 51 | 1.94 | 2010 |
| New Generalization Bounds for Learning Kernels | 51 | 1.94 | 2009 |
| Stability Analysis and Learning Bounds for Transductive Regression Algorithms | 3 | 0.41 | 2009 |
| Invited talk: Can learning kernels help performance? | 19 | 0.89 | 2009 |
| Learning Non-Linear Combinations of Kernels. | 115 | 3.40 | 2009 |
| Polynomial Semantic Indexing. | 17 | 1.37 | 2009 |
| Kernel methods for learning languages | 6 | 0.63 | 2008 |
