My primary area of interest in research concerns computational theories of inference and learning in biological and artificial systems.

Questions that motivate me include: How can we construct artificial systems that analyze complex, cluttered environments with the ease and accuracy of natural systems? How can novel visual items be processed efficiently and how do representations and processing change as items become more familiar?

Learning fair representations. Richard Zemel, Toni Pitassi, Yu Wu, Kevin Swersky, Cynthia Dwork. ICML-2013: The 30th International Conference on Machine Learning (2013).

Stochastic k-neighborhood selection for supervised and unsupervised learning. Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard Zemel. . ICML-2013: The 30th International Conference on Machine Learning (2013).

Exploring compositional high order pattern potentials for structured output learning. Yujia Li, Daniel Tarlow, Richard Zemel. CVPR-2013: The 26th IEEE Conference on Computer Vision and Pattern Recognition (2013). [pdf, notes]

Collaborative ranking with 17 parameters. Maksims Volkovs & Richard Zemel. NIPS 2012: Advances in Neural Information Processing Systems (2012). [pdf]

Probabilistic n-choose-k models for classification and ranking. Kevin Swersky, Daniel Tarlow, Ryan Adams, Richard Zemel,& Brendan Frey. NIPS-2012: Advances in Neural Information Processing Systems (2012). [pdf, poster, notes]

Efficient sampling for bipartite matching problems. Maksims Volkovs & Richard Zemel. NIPS-2012: Advances in Neural Information Processing Systems (2012). [pdf, notes]

Cardinality restricted Boltzmann machines . Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Richard Zemel, Ruslan Salakhutdinov, & Ryan Adams. NIPS-2012: Advances in Neural Information Processing Systems. (2012). [pdf, poster]

Fast exact inference for recursive cardinality models. Daniel Tarlow, Kevin Swersky, Richard Zemel, Ryan Adams, and Brendan Frey. UAI-2012: The 28th Conference on Uncertainty in Artificial Intelligence (2012). [pdf]

Learning to rank by aggregating expert preferences. Maksims Volkovs, Hugo Larochelle, and Richard Zemel. CIKM-2012: International Conference on Information and Knowledge Management (2012). [pdf]

Randomized optimum models for structured prediction. Daniel Tarlow, Ryan Adams, and Richard Zemel. AIStats-2012: Fifteenth International Conference on Artificial Intelligence and Statistics (2012). [pdf]

Structured output learning with high order loss functions. Daniel Tarlow and Richard Zemel. AIStats-2012: Fifteenth International Conference on Artificial Intelligence and Statistics (2012). [pdf]

A flexible generative model for preference aggregation. Maksims Volkovs and Richard Zemel. WWW 2012: 21st International World Wide Web Conference (2012). [pdf]

Fairness through awareness. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel. Proceedings of Innovations of Theoretical Computer Science (2012). [pdf]

Active learning for matching problems. Laurent Charlin, Richard Zemel, and Craig Boutilier. ICML-2012: Proceedings of the 29th International Conference on Machine Learning (2012). [pdf]