Hi! PARIS at NeurIPS 2021
24 research papers accepted at the conference on Neural Information Processing Systems (NeurIPS) 2021
Hi! PARIS is pleased to present the work of its researchers and professors at the 35th Conference on Neural Information Processing Systems (NeurIPS) being held this week (Dec 6-14, 2021).
For this edition, 24 publications were accepted by the reviewers for their significant contribution in the field of machine learning. The NeurIPS conference is world-renowned as one of the largest and most important machine learning conferences in the world.
The accepted papers cover a range of topics of AI (missing values prediction, Optimization under adversarial noise, reinforcement learning, interpretable machine learning, compressibility of overparametrized neural networks, federated learning, neuroimaging, ….). HEC Paris and several schools of Institut Polytechnique de Paris are represented.
Congratulations to our Professors and Researchers!
École polytechnique, Institut Polytechnique de Paris: Aymeric Dieuleveut, Rémi Flamary, Eric Moulines, Charles Ollion, Constantin Philippenko, Erwan Scornet, Achille Thin ENSAE Paris, Institut Polytechnique de Paris: Arya Akhavan, Etienne Boursier, Cristina Butucea, Evrard Garcelon, Yann Issartel, Olga Klopp, Vianney Perchet, Flore Sentenac, Alexandre Tsybakov HEC Paris: Julien Grand-Clément Inria Saclay: Alexandre Gramfort, Federica Granese, Pedro L. C. Rodrigues, Marine Le Morvan, Thomas Moreau, Catuscia Palamidessi, Hugo Richard, Bertrand Thirion, Gael Varoquaux Telecom Paris, Institut Polytechnique de Paris: Florence D’Alché-Buc, Kamélia Daudel, Pavlo Mozharovskyi, Nathan Noiry, Jayneel Parekh, Gaël Richard, Milad Sefidgaran Telecom Sud Paris, Institut Polytechnique de Paris: Randal Douc, Yazid Janati, Sylvain Le CorffHere is the complete list of NeurIPS publications for Professors and Researchers in Hi! PARIS:
- DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida [link] SPOTLIGHT
Article available on https://proceedings.neurips.cc/paper/2021/file/2cb6b10338a7fc4117a80da24b582060-Paper.pdf - What’s a good imputation to predict with missing values?
Marine Le Morvan, Julie Josse, Erwan Scornet, Gael Varoquaux [link] SPOTLIGHT
Article available on https://proceedings.neurips.cc/paper/2021/file/5fe8fdc79ce292c39c5f209d734b7206-Paper.pdf - Decentralized Learning in Online Queuing Systems
Flore Sentenac, Etienne Boursier, Vianney Perchet [link] SPOTLIGHT
Article available on https://proceedings.neurips.cc/paper/2021/file/99ef04eb612baf0e86671a5109e22154-Paper.pdf - Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
Reda Ouhamma, Rémy Degenne, Vianney Perchet, Pierre Gaillard [link] SPOTLIGHT
Article available on https://papers.neurips.cc/paper/2021/file/9a3f54913bf27e648d1759c18d007165-Paper.pdf - Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvarinen [link]
Article available on https://arxiv.org/pdf/2106.09620.pdf - Preserved central model for faster bidirectional compression in distributed settings
Constantin Philippenko, Aymeric Dieuleveut [link]
Article available on https://papers.nips.cc/paper/2021/file/13d63838ef1fb6f34ca2dc6821c60e49-Paper.pdf - A New Theoretical Framework for Fast and Accurate Online Decision-Making
Nicolò Cesa-Bianchi, Tom Cesari, Yishay Mansour, Vianney Perchet [link]
Article available on https://papers.nips.cc/paper/2021/file/4c4ea5258ef3fb3fb1fc48fee9b4408c-Paper.pdf - Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving
Julien Grand-Clément, Christian Kroer [link]
Article available on https://papers.nips.cc/paper/2021/file/4f87658ef0de194413056248a00ce009-Paper.pdf - Distributed Zero-Order Optimization under Adversarial Noise
Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov [link]
Article available on https://papers.nips.cc/paper/2021/file/5487e79fa0ccd0b79e5d4a4c8ced005d-Paper.pdf - Local Differential Privacy for Regret Minimization in Reinforcement Learning
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta [link]
Article available on https://papers.nips.cc/paper/2021/file/580760fb5def6e2ca8eaf601236d5b08-Paper.pdf - Making the most of your day: online learning for optimal allocation of time
Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini [link]
Article available on https://papers.nips.cc/paper/2021/file/5d2c2cee8ab0b9a36bd1ed7196bd6c4a-Supplemental.pdf - NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian Robert [link]
Article available on https://papers.nips.cc/paper/2021/file/8dd291cbea8f231982db0fb1716dfc55-Paper.pdf - Optimality of variational inference for stochasticblock model with missing links
Solenne Gaucher, Olga Klopp [link]
Article available on https://papers.nips.cc/paper/2021/file/a5e308070bd6dd3cc56283f2313522de-Paper.pdf - Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm
Nathan Noiry, Vianney Perchet, Flore Sentenac [link]
Article available on https://papers.nips.cc/paper/2021/file/b294504229c668e750dfcc4ea9617f0a-Supplemental.pdf - Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, Gilles Gasso [link]
Article available on https://papers.neurips.cc/paper/2021/file/c3c617a9b80b3ae1ebd868b0017cc349-Paper.pdf - A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence D’Alché-Buc [link]
Article available on https://proceedings.neurips.cc/paper/2021/file/cbb6a3b884f4f88b3a8e3d44c636cbd8-Paper.pdf - Stochastic Online Linear Regression: the Forward Algorithm to Replace RidgeReda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet [link]
Article available on https://proceedings.neurips.cc/paper/2021/file/cca289d2a4acd14c1cd9a84ffb41dd29-Paper.pdf - Locally differentially private estimation of nonlinear functionals of discrete distributions
Cristina Butucea, Yann Issartel [link]
Article available on https://papers.nips.cc/paper/2021/file/cf8c9be2a4508a24ae92c9d3d379131d-Paper.pdf - Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
Melih Barsbey, Seyedmilad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Simsekli [link]
Article available on https://proceedings.neurips.cc/paper/2021/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf - Federated-EM with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin [link]
Article available on https://proceedings.neurips.cc/paper/2021/file/f740c8d9c193f16d8a07d3a8a751d13f-Paper.pdf - Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai [link]
Article available on https://papers.nips.cc/paper/2021/file/fc95fa5740ba01a870cfa52f671fe1e4-Paper.pdf - Shared Independent Component Analysis for Multi-Subject Neuroimaging
Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen [link]
Article available on https://proceedings.neurips.cc/paper/2021/file/fb508ef074ee78a0e58c68be06d8a2eb-Paper.pdf - HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Pedro Luiz Coelho Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort [link]
Article available on https://hal.archives-ouvertes.fr/hal-03139916/document - Mixture weights optimisation for Alpha-Divergence Variational Inference
Kamélia Daudel, Randal Douc [link]
Article available on https://papers.nips.cc/paper/2021/file/233f1dd0f3f537bcb7a338ea74d63483-Paper.pdf