Papers @ALT, @AISTATS, @ICLR, @WWW, @JMLR, and more

By: Criteo AI Lab / 15 Feb 2022

Foundation and Trends in ML:

  • Learning in repeated auctions (Foundation and Trends in ML) – Thomas Nedelec, Clément Calauzènes, Noureddine El Karoui, Vianney Perchet, https://arxiv.org/abs/2011.09365

JMLR

  • Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism With Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos, JMLR, https://arxiv.org/abs/2012.11579

WWW 2022

  • Lessons from the AdKDD’21 Privacy-Preserving ML Challenge (WWW), Alexandre Gilotte, Jeremie Mary, Romain Fabre, Ugo Tanielian, Eustache Diemert, Basile Leparmentier, Qu Zhonghua, Fei Jia, Hui Yang. https://arxiv.org/pdf/2201.13123.pdf

AISTATS 2022

  • Efficient Kernelized UCB for Contextual Bandits (AISTATS), Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard. https://arxiv.org/abs/2202.05638
  • Jointly Efficient and Optimal Algorithms for Logistic Bandits (AISTATS), Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes. https://arxiv.org/abs/2201.01985
  • Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications (AISTATS) – Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi. Link: https://arxiv.org/pdf/2201.08105.pdf
  • Encrypted Linear Contextual Bandit (AISTATS), Evrard Garcelon, Vianney Perchet,  Matteo Pirotta. https://arxiv.org/pdf/2103.09927.pdf
  • QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning (AISTATS) – Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut and Eric Moulines. Arxiv link: https://arxiv.org/pdf/2106.00797.pdf
  • Convergent Working Set Algorithm for Lasso with Non-Convex Sparse Regularizers. Alain Rakotomamonjy, Gilles Gasso, Rémi Flamary and Joseph Salmon. https://arxiv.org/pdf/2006.13533.pdf

ALT 2022

ICLR 2022