Ioannis Mitliagkas

Publications

Learning to Defer for Causal Discovery with Imperfect Experts. Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vazquez, João Monteiro. At NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets), 2024.

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Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones. Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, João Monteiro, Valentina Zantedeschi. At Workshop at the International Conference of Machine Learning (ICML), 2024.

CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning. Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, João Monteiro. At Conference on Neural Information Processing Systems (NeurIPS), 2023.

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On Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants. Ryan D'Orazio, Nicolas Loizou, Issam H. Laradji, Ioannis Mitliagkas. At Transactions on Machine Learning Research (TMLR), 2023.

Contrastive Self-supervision Defines General-Purpose Similarity Functions. Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, João Monteiro. At Workshop at the Neural Information Processing Systems (NeurIPS), 2022.

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In search of robust measures of generalization. Gintare Karolina Dziugaite, Alexandre Drouin, Brayden (Brady) Neal, Nitarshan Rajkumar, Ethan Victor Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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