In December 2024, CREST researchers and PhD students, will present their last papers during the 2024 NeurIPS conference that will be held in Vancouver, Canada.
About the NeurIPS Conference
Founded in 1987, the conference has evolved into a prominent annual interdisciplinary event, featuring multiple tracks that include invited talks, demonstrations, symposia, and oral and poster presentations of peer-reviewed papers.
In addition to the main program, the event hosts a professional exhibition highlighting real-world applications of machine learning, as well as tutorials and topical workshops designed to foster the exchange of ideas in a more informal setting.
NeurIPS, alongside ICML, ranks among the top three most prestigious international conferences in Artificial Intelligence.
CREST’s papers to be presented
The conference’s focus resonates with CREST’s contributions to AI, particularly in applying statistical and mathematical frameworks to emerging challenges. CREST researchers have developed innovative methods in areas like optimal transport, reinforcement learning, and auction theory.
19 papers from CREST researchers and PhD students will be presented during the conference.
| Title |
Authors |
| Progressive Entropic Optimal Transport Solvers |
P. Kassraie, AA. Pooladian, M. Klein, J. Thornton, J. Niles-Weed, M. Cuturi |
| Learning Elastic Costs to Shape Monge Displacements |
M. Klein, AA. Pooladian, P. Ablin, E. Ndiaye, J. Niles-Weed, M. Cuturi |
| GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics |
D. Klein, T. Uscidda, F. Theis, M. Cuturi |
| Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence |
C. Chazal, A. Korba, F. Bach |
| Constrained Sampling with Primal-Dual Langevin Monte Carlo |
L. F. O. Chamon, M. R. Karimi, A. Korba |
| Mirror and Preconditioned Gradient Descent in Wasserstein Space |
C. Bonet, T. Uscidda, A. David, P-C. Aubin-Frankowski, A. Korba |
| Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning, |
Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin |
| Wasserstein convergence of Čech persistence diagrams for samplings of submanifolds |
C. Arnal, D. Cohen-Steiner, V. Divol |
| The Value of Reward Lookahead in Reinforcement Learning |
Nadav Merlis, Dorian Baudry, Vianney Perchet |
| DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation |
Felipe Garrido, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet |
| Lookback Prophet Inequalities |
Ziyad Benomar, Dorian Baudry, Vianney Perchet |
| Addressing Bias in Online Selection with Limited Budget of Comparisons |
Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet |
| Local and Adaptive Mirror Descents in Extensive-Form Games |
Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko |
| Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting |
Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet |
| Improved learning rates in multi-unit uniform price auctions |
Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan |
| Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits |
Dorian Baudry, Hugo Richard, Maria Cherifa, Vianney Perchet, Clément Calauzènes |
| Improved Algorithms for Contextual Dynamic Pricing |
Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet |
| Learning-Augmented Priority Queues |
Ziyad Benomar · Christian Coester |
| Reinforcement Learning with Lookahead Information |
Nadav Merlis |
If you want to check papers to be presented during NeurIPS 2024, please visit: https://nips.cc/virtual/2024/papers.html?filter=titles
Source: https://neurips.cc/About
