collaborators

7 papers

cs.LG2026

: Transformer-based inference from genomic contact maps

Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel +2

Locating genomic features from genomic contact maps, such as centromere identification from genome-wide chromosome conformation capture techniques, notably Hi-C, can be formulated…

stat.ML2026

Semiparametric Efficient Bilevel Gradient Estimation

Fares El Khoury, Houssam Zenati, Nathan Kallus +2

Functional bilevel methods estimate a lower-level function and plug it into a hypergradient, but this plug-in gradient can retain first-order bias when the lower-level problem is l…

cs.LG2026

PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment

Michael Arbel, Basile Terver, Jean Ponce

Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effective) methods such as SimSiam, BY…

cs.CV2026

Beyond MMSE: Enhancing PnP Restoration with ProxiMAP

Kenta Vert, Giacomo Meanti, Scott Pesme +2

Plug-and-Play (PnP) methods have become standard tools for solving imaging inverse problems by replacing the intractable maximum a posteriori (MAP) denoiser with the MMSE one. Whil…

stat.ML2025

Flow Matching Calibration for Simulation-Based Inference under Model Misspecification

Pierre-Louis Ruhlmann, Michael Arbel, Florence Forbes +1

Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simulated data. A persistent challenge, ho…

stat.ML2025

Simulation-based inference of yeast centromeres

Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel +2

The chromatin folding and the spatial arrangement of chromosomes in the cell play a crucial role in DNA replication and genes expression. An improper chromatin folding could lead t…

cs.LG2025

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks

Michael Arbel, David Salinas, Frank Hutter

Recent foundational models for tabular data, such as TabPFN, excel at adapting to new tasks via in-context learning, but remain constrained to a fixed, pre-defined number of target…