Zobrazeno 1 - 10
of 1 973
pro vyhledávání: '"Gresele P"'
Autor:
Roberto Cangemi, Valeria Raparelli, Giovanni Talerico, Stefania Basili, Francesco Violi, Palasciano Giuseppe, D’Alitto Felicia, Palmieri Vincenzo Ostilio, Santovito Daniela, Di Michele Dario, Croce Giuseppe, Sacerdoti David, Brocco Silvia, Fasolato Silvano, Cecchetto Lara, Bombonato Giancarlo, Bertoni Michele, Restuccia Tea, Andreozzi Paola, Liguori Maria Livia, Perticone Francesco, Caroleo Benedetto, Perticone Maria, Staltari Orietta, Manfredini Roberto, De Giorgi Alfredo, Averna Maurizio, Giammanco Antonina, Granito Alessandro, Pettinari Irene, Marinelli Sara, Bolondi Luigi, Falsetti Lorenzo, Salvi Aldo, Durante-Mangoni Emanuele, Cesaro Flavio, Farinaro Vincenza, Ragone Enrico, Morana Ignazio, Andriulli Angelo, Ippolito Antonio, Iacobellis Angelo, Niro Grazia, Merla Antonio, Raimondo Giovanni, Maimone Sergio, Cacciola Irene, Varvara Doriana, Drenaggi Davide, Staffolani Silvia, Picardi Antonio, Vespasiani-Gentilucci Umberto, Galati Giovanni, Gallo Paolo, Davì Giovanni, Schiavone Cosima, Santilli Francesca, Tana Claudio, Licata Anna, Soresi Maurizio, Bianchi Giovanni Battista, Carderi Isabella, Pinto Antonio, Tuttolomondo Antonino, Ferrari Giovanni, Gresele Paolo, Fierro Tiziana, Morelli Olivia, Laffi Giacomo, Romanelli Roberto Giulio, Arena Umberto, Stasi Cristina, Gasbarrini Antonio, Gargovich Matteo, Zocco Maria Assunta, Riccardi Laura, Ainora Maria Elena, Capeci William, Martino Giuseppe Pio, Nobili Lorenzo, Cavallo Maurizio, Frugiuele Pierluigi, Greco Antonio, Pietrangelo Antonello, Ventura Paolo, Cuoghi Chiara, Marcacci Matteo, Serviddio Gaetano, Vendemiale Gianluigi, Villani Rosanna, Gargano Ruggiero, Vidili Gianpaolo, Di Cesare Valentina, Masala Maristella, Delitala Giuseppe, Invernizzi Pietro, Di Minno Giovanni, Tufano Antonella, Purrello Francesco, Privitera Graziella, Forgione Alessandra, Curigliano Valentina, Senzolo Marco, Rodríguez-Castro Kryssia Isabel, Giannelli Gianluigi, Serra Carla, Neri Sergio, Pignataro Pietro, Rizzetto Mario, Debernardi Venon Wilma, Svegliati Baroni Gianluca, Bergamaschi Gaetano, Masotti Michela, Costanzo Filippo, Corazza Gino Roberto, Caldwell Stephen Hugh, Angelico Francesco, Del Ben Maria, Napoleone Laura, Polimeni Licia, Proietti Marco, Raparelli Valeria, Romiti Giulio Francesco, Ruscio Eleonora, Severoni Andrea, Talerico Giovanni, Toriello Filippo, Vestri Annarita
Publikováno v:
Gastro Hep Advances, Vol 3, Iss 5, Pp 646-653 (2024)
Background and Aims: Hypoalbuminemia, as defined by serum albumin (SA) levels ≤35 g/L, is associated to venous and arterial thrombosis in general population and in patients at risk of cardiovascular disease. It is unknown if SA ≤35 g/L is also as
Externí odkaz:
https://doaj.org/article/178a1a7c8e1047f2ac0da978dac4c2b4
Autor:
Ghosh, Shubhangi, Gresele, Luigi, von Kügelgen, Julius, Besserve, Michel, Schölkopf, Bernhard
Independent Mechanism Analysis (IMA) seeks to address non-identifiability in nonlinear Independent Component Analysis (ICA) by assuming that the Jacobian of the mixing function has orthogonal columns. As typical in ICA, previous work focused on the c
Externí odkaz:
http://arxiv.org/abs/2312.13438
Autor:
Jin, Zhijing, Chen, Yuen, Leeb, Felix, Gresele, Luigi, Kamal, Ojasv, Lyu, Zhiheng, Blin, Kevin, Adauto, Fernando Gonzalez, Kleiman-Weiner, Max, Sachan, Mrinmaya, Schölkopf, Bernhard
The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing work in natural language proces
Externí odkaz:
http://arxiv.org/abs/2312.04350
Autor:
von Kügelgen, Julius, Besserve, Michel, Wendong, Liang, Gresele, Luigi, Kekić, Armin, Bareinboim, Elias, Blei, David M., Schölkopf, Bernhard
We study causal representation learning, the task of inferring latent causal variables and their causal relations from high-dimensional mixtures of the variables. Prior work relies on weak supervision, in the form of counterfactual pre- and post-inte
Externí odkaz:
http://arxiv.org/abs/2306.00542
Autor:
Wendong, Liang, Kekić, Armin, von Kügelgen, Julius, Buchholz, Simon, Besserve, Michel, Gresele, Luigi, Schölkopf, Bernhard
Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof. Causal Representation Learning (CRL) aims instead to infer causally related (thus often statistically dependent) latent variables, toget
Externí odkaz:
http://arxiv.org/abs/2305.17225
Autor:
Kekić, Armin, Dehning, Jonas, Gresele, Luigi, von Kügelgen, Julius, Priesemann, Viola, Schölkopf, Bernhard
Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups. Evaluating vaccine allocation is therefore a crucial element of pandemics response. In the present work, we develop a model to retro
Externí odkaz:
http://arxiv.org/abs/2212.08498
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases. A recently proposed approach termed Independent Mechanism Analysis (IMA) postulates t
Externí odkaz:
http://arxiv.org/abs/2207.06137
Autor:
Reizinger, Patrik, Gresele, Luigi, Brady, Jack, von Kügelgen, Julius, Zietlow, Dominik, Schölkopf, Bernhard, Martius, Georg, Brendel, Wieland, Besserve, Michel
Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; they can be efficiently trained via variational inference by maximizing the evidence lower bound (ELBO), at the expense of a gap to the exact (log-)margi
Externí odkaz:
http://arxiv.org/abs/2206.02416
Autor:
Ghosh, Shubhangi, Gresele, Luigi, von Kügelgen, Julius, Besserve, Michel, Schölkopf, Bernhard
Model identifiability is a desirable property in the context of unsupervised representation learning. In absence thereof, different models may be observationally indistinguishable while yielding representations that are nontrivially related to one an
Externí odkaz:
http://arxiv.org/abs/2202.06844
Autor:
Gresele, Luigi, von Kügelgen, Julius, Kübler, Jonas M., Kirschbaum, Elke, Schölkopf, Bernhard, Janzing, Dominik
Publikováno v:
International Conference on Machine Learning (ICML 2022), 7793-7824
We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: given a collection of marginal structural causal models (SCMs) over dist
Externí odkaz:
http://arxiv.org/abs/2202.01300