Building Conformal Prediction Intervals with Approximate Message Passing

Autor: Clarté, Lucas, Zdeborová, Lenka

Conformal prediction has emerged as a powerful tool for building prediction intervals that are valid in a distribution-free way. However, its evaluation may be computationally costly, especially in the high-dimensional setting where the dimensionalit

Externí odkaz: http://arxiv.org/abs/2410.16493

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

Autor: Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Kaser, Tanja, Bosselut, Antoine

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.

Externí odkaz: http://arxiv.org/abs/2408.11841

The phase diagram of compressed sensing with $\ell_0$-norm regularization

Autor: Barbier, Damien, Lucibello, Carlo, Saglietti, Luca, Krzakala, Florent, Zdeborová, Lenka

Noiseless compressive sensing is a two-steps setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, based on $\lone$ regularization, provides an efficient and robust to addre

Externí odkaz: http://arxiv.org/abs/2408.08319

Optimal thresholds and algorithms for a model of multi-modal learning in high dimensions

Autor: Keup, Christian, Zdeborová, Lenka

This work explores multi-modal inference in a high-dimensional simplified model, analytically quantifying the performance gain of multi-modal inference over that of analyzing modalities in isolation. We present the Bayes-optimal performance and weak

Externí odkaz: http://arxiv.org/abs/2407.03522

Fundamental computational limits of weak learnability in high-dimensional multi-index models

Autor: Troiani, Emanuele, Dandi, Yatin, Defilippis, Leonardo, Zdeborová, Lenka, Loureiro, Bruno, Krzakala, Florent

Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating feature learning with neural nets. This paper examines the theoretical

Externí odkaz: http://arxiv.org/abs/2405.15480