Robert C. Williamson

ORCID

Publication Activity (10 Years)

Years Active: 1990-2024
Publications (10 Years): 47

Top Venues

Publications

Rabanus Derr, Robert C. Williamson
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret. CoRR (2024)
Christian Fröhlich, Rabanus Derr, Robert C. Williamson
Strictly frequentist imprecise probability. Int. J. Approx. Reason. 168 (2024)
Benedikt Höltgen, Robert C. Williamson
Five reasons against assuming a data-generating distribution in Machine Learning. CoRR (2024)
Benedikt Höltgen, Robert C. Williamson
Causal modelling without counterfactuals and individualised effects. CoRR (2024)
Christian Fröhlich, Robert C. Williamson
Insights From Insurance for Fair Machine Learning. FAccT (2024)
Facundo Mémoli, Brantley Vose, Robert C. Williamson
Geometry and Stability of Supervised Learning Problems. CoRR (2024)
Mina Remeli, Moritz Hardt, Robert C. Williamson
Limits to Predicting Online Speech Using Large Language Models. CoRR (2024)
Armando J. Cabrera Pacheco, Rabanus Derr, Robert C. Williamson
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm. CoRR (2024)
Christian Fröhlich, Robert C. Williamson
Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates. CoRR (2023)
Christian Fröhlich, Robert C. Williamson
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. Trans. Mach. Learn. Res. 2023 (2023)
Yishay Mansour, Richard Nock, Robert C. Williamson
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. ICML (2023)
Benedikt Höltgen, Robert C. Williamson
On the Richness of Calibration. CoRR (2023)
Benedikt Höltgen, Robert C. Williamson
On the Richness of Calibration. FAccT (2023)
Armando J. Cabrera Pacheco, Robert C. Williamson
The Geometry of Mixability. CoRR (2023)
Christian Fröhlich, Rabanus Derr, Robert C. Williamson
Towards a strictly frequentist theory of imprecise probability. ISIPTA (2023)
Armando J. Cabrera Pacheco, Robert C. Williamson
The Geometry of Mixability. Trans. Mach. Learn. Res. 2023 (2023)
Rabanus Derr, Robert C. Williamson
The set structure of precision. ISIPTA (2023)
Rabanus Derr, Robert C. Williamson
Systems of Precision: Coherent Probabilities on Pre-Dynkin Systems and Coherent Previsions on Linear Subspaces. Entropy 25 (9) (2023)
Laura Iacovissi, Nan Lu, Robert C. Williamson
A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond. CoRR (2023)
Robert C. Williamson, Zac Cranko
The Geometry and Calculus of Losses. CoRR (2022)
Lachlan McCalman, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C. Williamson, Richard S. Zemel, Hsiao-Ying Lin
Assessing AI Fairness in Finance. Computer 55 (1) (2022)
Robert C. Williamson, Zac Cranko
Information Processing Equalities and the Information-Risk Bridge. CoRR (2022)
Rabanus Derr, Robert C. Williamson
Fairness and Randomness in Machine Learning: Statistical Independence and Relativization. CoRR (2022)
Christian Fröhlich, Robert C. Williamson
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. CoRR (2022)
Christian Fröhlich, Robert C. Williamson
Risk Measures and Upper Probabilities: Coherence and Stratification. CoRR (2022)
Yishay Mansour, Richard Nock, Robert C. Williamson
What killed the Convex Booster ? CoRR (2022)
Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
PAC-Bayesian Bound for the Conditional Value at Risk. CoRR (2020)
Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
PAC-Bayesian Bound for the Conditional Value at Risk. NeurIPS (2020)
Robert C. Williamson, Aditya Krishna Menon
Fairness risk measures. CoRR (2019)
Hisham Husain, Richard Nock, Robert C. Williamson
Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. CoRR (2019)
Richard Nock, Robert C. Williamson
Lossless or Quantized Boosting with Integer Arithmetic. ICML (2019)
Hisham Husain, Richard Nock, Robert C. Williamson
A Primal-Dual link between GANs and Autoencoders. NeurIPS (2019)
Robert C. Williamson, Aditya Krishna Menon
Fairness risk measures. ICML (2019)
Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
Costs and Benefits of Fair Representation Learning. AIES (2019)
Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang
Exp-Concavity of Proper Composite Losses. CoRR (2018)
Parameswaran Kamalaruban, Robert C. Williamson
Minimax Lower Bounds for Cost Sensitive Classification. CoRR (2018)
Zakaria Mhammedi, Robert C. Williamson
Constant Regret, Generalized Mixability, and Mirror Descent. CoRR (2018)
Aditya Krishna Menon, Robert C. Williamson
The cost of fairness in binary classification. FAT (2018)
Zakaria Mhammedi, Robert C. Williamson
Constant Regret, Generalized Mixability, and Mirror Descent. NeurIPS (2018)
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
f-GANs in an Information Geometric Nutshell. NIPS (2017)
Aditya Krishna Menon, Robert C. Williamson
The cost of fairness in classification. CoRR (2017)
Brendan van Rooyen, Robert C. Williamson
A Theory of Learning with Corrupted Labels. J. Mach. Learn. Res. 18 (2017)
Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
f-GANs in an Information Geometric Nutshell. CoRR (2017)
Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
Provably Fair Representations. CoRR (2017)
Robert C. Williamson, Elodie Vernet, Mark D. Reid
Composite Multiclass Losses. J. Mach. Learn. Res. 17 (2016)
Daniel McNamara, Cheng Soon Ong, Robert C. Williamson
A Modular Theory of Feature Learning. CoRR (2016)
Aditya Krishna Menon, Robert C. Williamson
Bipartite Ranking: a Risk-Theoretic Perspective. J. Mach. Learn. Res. 17 (2016)
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson
Fast rates in statistical and online learning. J. Mach. Learn. Res. 16 (2015)
Brendan van Rooyen, Robert C. Williamson
A Theory of Feature Learning. CoRR (2015)
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta
Generalized Mixability via Entropic Duality. COLT (2015)
Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang
Exp-Concavity of Proper Composite Losses. COLT (2015)
Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson
Learning with Symmetric Label Noise: The Importance of Being Unhinged. CoRR (2015)
Brendan van Rooyen, Robert C. Williamson
Learning in the Presence of Corruption. CoRR (2015)
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson
Fast rates in statistical and online learning. CoRR (2015)
Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson
Learning with Symmetric Label Noise: The Importance of Being Unhinged. NIPS (2015)
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson
Generalised Mixability, Constant Regret, and Bayesian Updating. CoRR (2014)
Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. COLT (2014)
Nishant A. Mehta, Robert C. Williamson
From Stochastic Mixability to Fast Rates. NIPS (2014)
Nishant A. Mehta, Robert C. Williamson
From Stochastic Mixability to Fast Rates. CoRR (2014)
Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang
Elicitation and Identification of Properties. COLT (2014)
Aditya Krishna Menon, Robert C. Williamson
Bayes-Optimal Scorers for Bipartite Ranking. COLT (2014)
Robert C. Williamson
The Geometry of Losses. COLT (2014)
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta
Generalized Mixability via Entropic Duality. CoRR (2014)
Robert C. Williamson
Loss Functions. Empirical Inference (2013)
Mark D. Reid, Robert C. Williamson, Peng Sun
The Convexity and Design of Composite Multiclass Losses CoRR (2012)
Dario García-García, Robert C. Williamson
Divergences and Risks for Multiclass Experiments. COLT (2012)
Ayman Ghoneim, Robert C. Williamson
Strategy-Proof Prediction Markets CoRR (2012)
Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson
Mixability in Statistical Learning. NIPS (2012)
Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon
Clustering: Science or Art? ICML Unsupervised and Transfer Learning (2012)
Mark D. Reid, Robert C. Williamson, Peng Sun
The Convexity and Design of Composite Multiclass Losses. ICML (2012)
Tim van Erven, Mark D. Reid, Robert C. Williamson
Mixability is Bayes Risk Curvature Relative to Log Loss. J. Mach. Learn. Res. 13 (2012)
Tim van Erven, Mark D. Reid, Robert C. Williamson
Mixability is Bayes Risk Curvature Relative to Log Loss. COLT (2011)
Mark D. Reid, Robert C. Williamson
Information, Divergence and Risk for Binary Experiments. J. Mach. Learn. Res. 12 (2011)
Elodie Vernet, Robert C. Williamson, Mark D. Reid
Composite Multiclass Losses. NIPS (2011)
Mark D. Reid, Robert C. Williamson
Composite Binary Losses. J. Mach. Learn. Res. 11 (2010)
Mark D. Reid, Robert C. Williamson
Convexity of Proper Composite Binary Losses. AISTATS (2010)
Mark D. Reid, Robert C. Williamson
Generalised Pinsker Inequalities CoRR (2009)
Mark D. Reid, Robert C. Williamson
Generalised Pinsker Inequalities. COLT (2009)
Mark D. Reid, Robert C. Williamson
Surrogate regret bounds for proper losses. ICML (2009)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
Correction to "The Importance of Convexity in Learning With Squared Loss". IEEE Trans. Inf. Theory 54 (9) (2008)
Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8 (2007)
Eric A. Lehmann, Robert C. Williamson
Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments. EURASIP J. Adv. Signal Process. 2006 (2006)
Omri Guttman, S. V. N. Vishwanathan, Robert C. Williamson
Learnability of Probabilistic Automata via Oracles. ALT (2005)
Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
Learning the Kernel with Hyperkernels. J. Mach. Learn. Res. 6 (2005)
Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson
Online learning with kernels. IEEE Trans. Signal Process. 52 (8) (2004)
Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson
Online Bayes Point Machines. PAKDD (2003)
Darren B. Ward, Eric A. Lehmann, Robert C. Williamson
Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Trans. Speech Audio Process. 11 (6) (2003)
James A. McGowan, Robert C. Williamson
Loop removal from LDPC codes. ITW (2003)
Eric A. Lehmann, Darren B. Ward, Robert C. Williamson
Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room. ICASSP (5) (2003)
Terence Betlehem, Robert C. Williamson
Acoustic beamforming exploiting directionality of human speech sources. ICASSP (5) (2003)
Edward Harrington, Jyrki Kivinen, Robert C. Williamson
Channel equalization and the Bayes point machine. ICASSP (4) (2003)
Richard K. Martin, William A. Sethares, Robert C. Williamson, C. Richard Johnson Jr.
Exploiting sparsity in adaptive filters. IEEE Trans. Signal Process. 50 (8) (2002)
Shahar Mendelson, Robert C. Williamson
Agnostic Learning Nonconvex Function Classes. COLT (2002)
Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson
Large Margin Classification for Moving Targets. ALT (2002)
Darren B. Ward, Robert C. Williamson
Particle filter beamforming for acoustic source localization in a reverberant environment. ICASSP (2002)
Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
Hyperkernels. NIPS (2002)
Ralf Herbrich, Robert C. Williamson
Algorithmic Luckiness. J. Mach. Learn. Res. 3 (2002)
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson
Covering numbers for support vector machines. IEEE Trans. Inf. Theory 48 (1) (2002)
Robert E. Mahony, Robert C. Williamson
Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. J. Mach. Learn. Res. 1 (2001)
Adam Kowalczyk, Alexander J. Smola, Robert C. Williamson
Kernel Machines and Boolean Functions. NIPS (2001)

Robert C. Williamson.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Robert C. Williamson