About the position
About the research centre or Inria department The Inria Grenoble research center groups together almost 600 people in 27 research teams and 8 research support departments. Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (University Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area. Inria Grenoble is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world. Context Within the framework of a partnership (you can choose between) Collaboration with Kateek Alahari and David Salinas. Is regular travel foreseen for this post ? Yes, travel costs will be covered within the limits of the scale in force. Context and Objective: Tabular and Time-series foundational models have become very popular due to their high accuracy relying solely on In-Context Learning (ICL) [ 3 , 4 ]. However, one still requires training one model per ”modality”, e.g., one model for regression, one model for classification (although recent work showed that it is possible to reuse the TabPFN regression checkpoint for time-series [ 5 ]). One reason for this limitation is that models use a parametric projection to obtain predictions. This parametric projection has intrinsic limitations, forcing one to learn a model that can predict only up to the maximum dimension seen during training; it also requires padding. Two exceptions are recent works which instead propose to learn a model with an equivariant architecture, with either a non-parametric approach [ 6 ] or an equivariant architecture [ 1 ]. The project will build on this work and develop a single foundational model able to do high-dimensional classification and regression. The project will leverage previous work using non-parametric approaches to search for an efficient architecture and aim at having a single model able to perform well on TabArena [ 2 ]. If time permits, the student will look into multivariate extensions, e.g., predicting joint distributions instead of marginals as done in time-series forecasting with equivariant parametrization [ 8 ] or diffusion processes [ 7 ]. The student will also look into applications in tabular and time-series predictions. Assignment Assignments : The PhD project aims at developing foundation models for performing in-context learning on high-dimensional data. Responsibilities: The recruited person will take initiatives to formulate the problem and address it in a rigorous manner. Main activities Mains activities: Publish in top tier conferences and journals Produce high quility open-source software Participate to the research community (participation to conferences, workshops, etc) Skills Technical skills and level required : Excellent coding skills, strong mathematical background. Languages : English Interpersonal skills : Drive and perseverance
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