| PAC-Bayesian Bound for the Conditional Value at Risk | 0 | 0.34 | 2020 |
| Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. | 0 | 0.34 | 2019 |
| A Primal-Dual link between GANs and Autoencoders | 0 | 0.34 | 2019 |
| Fairness risk measures. | 0 | 0.34 | 2019 |
| Lossless or Quantized Boosting with Integer Arithmetic | 0 | 0.34 | 2019 |
| Proper-Composite Loss Functions in Arbitrary Dimensions. | 0 | 0.34 | 2019 |
| Minimax Lower Bounds for Cost Sensitive Classification. | 0 | 0.34 | 2018 |
| Constant Regret, Generalized Mixability, and Mirror Descent. | 0 | 0.34 | 2018 |
| The cost of fairness in binary classification. | 0 | 0.34 | 2018 |
| f-GANs in an Information Geometric Nutshell. | 4 | 0.40 | 2017 |
| Provably Fair Representations. | 0 | 0.34 | 2017 |
| A Theory of Learning with Corrupted Labels. | 4 | 0.39 | 2017 |
| The cost of fairness in classification. | 1 | 0.41 | 2017 |
| A Modular Theory of Feature Learning. | 0 | 0.34 | 2016 |
| Bipartite Ranking: a Risk-Theoretic Perspective. | 0 | 0.34 | 2016 |
| Fast Rates in Statistical and Online Learning | 8 | 0.55 | 2015 |
| Exp-Concavity of Proper Composite Losses | 0 | 0.34 | 2015 |
| Learning in the Presence of Corruption. | 3 | 0.40 | 2015 |
| Learning with Symmetric Label Noise: The Importance of Being Unhinged | 4 | 0.38 | 2015 |
| The Geometry of Losses. | 2 | 0.43 | 2014 |
| Bayes-Optimal Scorers for Bipartite Ranking. | 0 | 0.34 | 2014 |
| From Stochastic Mixability to Fast Rates. | 6 | 0.54 | 2014 |
| Generalized Mixability via Entropic Duality. | 3 | 0.45 | 2014 |
| On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. | 2 | 0.37 | 2014 |
| Generalised Mixability, Constant Regret, and Bayesian Updating. | 0 | 0.34 | 2014 |
| Elicitation and Identification of Properties. | 5 | 0.66 | 2014 |
| Loss Functions. | 0 | 0.34 | 2013 |
| Divergences and Risks for Multiclass Experiments. | 6 | 0.53 | 2012 |
| Mixability in Statistical Learning. | 4 | 0.52 | 2012 |
| Clustering: Science or Art? | 4 | 0.41 | 2012 |
| Divergences and Risks for Multiclass Experiments | 0 | 0.34 | 2012 |
| The Convexity and Design of Composite Multiclass Losses. | 0 | 0.34 | 2012 |
| Strategy-Proof Prediction Markets | 0 | 0.34 | 2012 |
| The Convexity and Design of Composite Multiclass Losses | 1 | 0.39 | 2012 |
| Mixability is Bayes Risk Curvature Relative to Log Loss | 0 | 0.34 | 2011 |
| Convexity of Proper Composite Binary Losses | 0 | 0.34 | 2010 |
| Surrogate regret bounds for proper losses | 22 | 1.11 | 2009 |
| Generalised Pinsker Inequalities | 4 | 0.41 | 2009 |
| Correction to "The Importance of Convexity in Learning With Squared Loss | 1 | 0.50 | 2008 |
| The Need for Open Source Software in Machine Learning | 64 | 11.47 | 2007 |
| Particle filter design using importance sampling for acoustic source localisation and tracking in reverberant environments | 1 | 0.35 | 2006 |
| Learnability of probabilistic automata via oracles | 9 | 0.55 | 2005 |
| Learning the Kernel with Hyperkernels | 130 | 6.57 | 2005 |
| Online Bayes point machines | 0 | 0.34 | 2003 |
| Channel equalization and the Bayes point machine | 0 | 0.34 | 2003 |
| Particle filtering algorithms for tracking an acoustic source in a reverberant environment. | 126 | 8.39 | 2003 |
| Covering numbers for support vector machines | 28 | 13.23 | 2002 |
| Agnostic Learning Nonconvex Function Classes | 2 | 1.25 | 2002 |
| Exploiting sparsity in adaptive filters | 36 | 3.46 | 2002 |
| Large Margin Classification for Moving Targets | 8 | 5.15 | 2002 |
