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Update publications page#58

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poisso merged 2 commits into
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publications-2026-update
Jun 26, 2026
Merged

Update publications page#58
poisso merged 2 commits into
mainfrom
publications-2026-update

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@yann-Choho

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Update accepted and ongoing articles on the eki.lab publications page

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github-actions Bot commented Jun 22, 2026

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🧹 Preview deployment cleaned up

The preview deployment for this PR has been removed since the PR was closed.

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Just one small comment cocerning the \citep{} latex text

Comment thread src/pages/publications.mdx Outdated
date="January 2026"
authors="Duong Nguyen, Mohammed Jawhar, Nicolas Chesneau"
title="TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models"
description="Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance through in-context learning, but their inductive biases remain fixed at inference time. Adapting a pretrained TFM to a specific dataset or task typically requires either full fine-tuning, which is computationally expensive, or parameter-efficient tuning methods (PEFT) such as LoRA, which must be tailored to the internal architecture of each TFM. Furthermore, the evidence on whether weight-space fine-tuning improves accuracy or calibration is mixed \citep{tanna_exploring_2026,rubachev_finetuning_2025}. We introduce TFM-Retouche, a lightweight input-space residual adapter that is architecture-agnostic by design with respect to the frozen TFM backbone. TFM-Retouche learns a small residual correction in the input space to align the input data with the inductive biases of the pretrained model. The adapter is trained end-to-end through the frozen TFM, with a post-training identity guard that falls back to the unmodified TFM whenever adaptation does not help on held-out validation. On TabArena-Lite (51 datasets spanning binary classification, multiclass classification, and regression), TabICLv2-Retouche -- the framework instantiated on TabICLv2 -- is the top-ranked method on the leaderboard with light per-task tuning and ensembling, lifting aggregate Elo by +56 over the frozen TabICLv2 base and sitting on the Pareto front of predictive quality versus both training and inference time."

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the \citep{} will not render properly in an html page

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Thanks for this review,
I remove the \citep{}

@poisso poisso merged commit 17a2872 into main Jun 26, 2026
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2 participants