Williamson, Robert C.

69 publications

JMLR 2025 Geometry and Stability of Supervised Learning Problems Facundo Mémoli, Brantley Vose, Robert C. Williamson

JMLR 2024 Information Processing Equalities and the Information–Risk Bridge Robert C. Williamson, Zac Cranko

JMLR 2024 Risk Measures and Upper Probabilities: Coherence and Stratification Christian Fröhlich, Robert C. Williamson

JMLR 2023 The Geometry and Calculus of Losses Robert C. Williamson, Zac Cranko

ISIPTA 2023 The Set Structure of Precision Rabanus Derr, Robert C. Williamson

ISIPTA 2023 Towards a Strictly Frequentist Theory of Imprecise Probability Christian Fröhlich, Rabanus Derr, Robert C. Williamson

NeurIPS 2020 PAC-Bayesian Bound for the Conditional Value at Risk Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson

NeurIPS 2019 A Primal-Dual Link Between GANs and Autoencoders Hisham Husain, Richard Nock, Robert C. Williamson

NeurIPS 2018 A Loss Framework for Calibrated Anomaly Detection Aditya Krishna Menon, Robert C. Williamson

NeurIPS 2018 Constant Regret, Generalized Mixability, and Mirror Descent Zakaria Mhammedi, Robert C. Williamson

NeurIPS 2017 F-GANs in an Information Geometric Nutshell Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, Robert C. Williamson

JMLR 2016 Bipartite Ranking: A Risk-Theoretic Perspective Aditya Krishna Menon, Robert C. Williamson

JMLR 2016 Composite Multiclass Losses Robert C. Williamson, Elodie Vernet, Mark D. Reid

COLT 2015 Exp-Concavity of Proper Composite Losses Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang

JMLR 2015 Fast Rates in Statistical and Online Learning Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson

COLT 2015 Generalized Mixability via Entropic Duality Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta

NeurIPS 2015 Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson

COLT 2014 Bayes-Optimal Scorers for Bipartite Ranking Aditya Krishna Menon, Robert C. Williamson

COLT 2014 Elicitation and Identification of Properties Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang

NeurIPS 2014 From Stochastic Mixability to Fast Rates Nishant A Mehta, Robert C. Williamson

COLT 2014 On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson

COLT 2014 The Geometry of Losses Robert C. Williamson

COLT 2012 Divergences and Risks for Multiclass Experiments Dario García-García, Robert C. Williamson

JMLR 2012 Mixability Is Bayes Risk Curvature Relative to Log Loss Tim van Erven, Mark D. Reid, Robert C. Williamson

NeurIPS 2012 Mixability in Statistical Learning Tim V. Erven, Peter Grünwald, Mark D. Reid, Robert C. Williamson

COLT 2012 Preface Shie Mannor, Nathan Srebro, Robert C. Williamson

ICML 2012 The Convexity and Design of Composite Multiclass Losses Mark D. Reid, Robert C. Williamson, Peng Sun

NeurIPS 2011 Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson

JMLR 2011 Information, Divergence and Risk for Binary Experiments Mark D. Reid, Robert C. Williamson

COLT 2011 Mixability Is Bayes Risk Curvature Relative to Log Loss Tim Erven, Mark D. Reid, Robert C. Williamson

JMLR 2010 Composite Binary Losses Mark D. Reid, Robert C. Williamson

COLT 2009 Generalised Pinsker Inequalities Mark D. Reid, Robert C. Williamson

ICML 2009 Surrogate Regret Bounds for Proper Losses Mark D. Reid, Robert C. Williamson

ALT 2005 Learnability of Probabilistic Automata via Oracles Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson

JMLR 2005 Learning the Kernel with Hyperkernels Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

COLT 2002 Agnostic Learning Nonconvex Function Classes Shahar Mendelson, Robert C. Williamson

JMLR 2002 Algorithmic Luckiness Ralf Herbrich, Robert C. Williamson

NeurIPS 2002 Hyperkernels Cheng S. Ong, Robert C. Williamson, Alex J. Smola

ALT 2002 Large Margin Classification for Moving Targets Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson

NeurIPS 2001 Algorithmic Luckiness Ralf Herbrich, Robert C. Williamson

COLT 2001 Computational Learning Theory, 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, the Netherlands, July 16-19, 2001, Proceedings David P. Helmbold, Robert C. Williamson

NeCo 2001 Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson

NeurIPS 2001 Kernel Machines and Boolean Functions Adam Kowalczyk, Alex J. Smola, Robert C. Williamson

NeurIPS 2001 Online Learning with Kernels Jyrki Kivinen, Alex J. Smola, Robert C. Williamson

JMLR 2001 Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms Robert E. Mahony, Robert C. Williamson

JMLR 2001 Regularized Principal Manifolds (Kernel Machines Section) Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson

COLT 2000 Entropy Numbers of Linear Function Classes Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf

NeurIPS 2000 From Margin to Sparsity Thore Graepel, Ralf Herbrich, Robert C. Williamson

NeCo 2000 New Support Vector Algorithms Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett

NeurIPS 2000 Regularization with Dot-Product Kernels Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson

COLT 1999 Covering Numbers for Support Vector Machines Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson

NeurIPS 1999 Support Vector Method for Novelty Detection Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt

NeurIPS 1999 The Entropy Regularization Information Criterion Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson

NeurIPS 1998 Shrinking the Tube: A New Support Vector Regression Algorithm Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson

COLT 1997 A PAC Analysis of a Bayesian Estimator John Shawe-Taylor, Robert C. Williamson

NeCo 1997 Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes' Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1996 A Framework for Structural Risk Minimisation John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony

COLT 1996 The Importance of Convexity in Learning with Squared Loss Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

NeCo 1996 The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs Peter L. Bartlett, Robert C. Williamson

NeurIPS 1995 Examples of Learning Curves from a Modified VC-Formalism Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson

NeCo 1995 Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1995 On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

COLT 1995 Online Learning via Congregational Gradient Descent Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares

COLT 1994 Fat-Shattering and the Learnability of Real-Valued Functions Peter L. Bartlett, Philip M. Long, Robert C. Williamson

COLT 1994 Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson

NeurIPS 1992 Rational Parametrizations of Neural Networks Uwe Helmke, Robert C. Williamson

COLT 1991 Investigating the Distribution Assumptions in the Pac Learning Model Peter L. Bartlett, Robert C. Williamson

NeurIPS 1991 Splines, Rational Functions and Neural Networks Robert C. Williamson, Peter L. Bartlett

NeurIPS 1990 E-Entropy and the Complexity of Feedforward Neural Networks Robert C. Williamson