Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Zečević, Matej"'
Autor:
Dycke, Nils, Zečević, Matej, Kuznetsov, Ilia, Suess, Beatrix, Kersting, Kristian, Gurevych, Iryna
Diagnostic reasoning is a key component of expert work in many domains. It is a hard, time-consuming activity that requires expertise, and AI research has investigated the ways automated systems can support this process. Yet, due to the complexity of
Externí odkaz:
http://arxiv.org/abs/2409.05367
$\chi$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Autor:
Poonia, Harsh, Willig, Moritz, Yu, Zhongjie, Zečević, Matej, Kersting, Kristian, Dhami, Devendra Singh
Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and propose Characteristic Interventional Sum-Product Network ($\chi$SPN) that
Externí odkaz:
http://arxiv.org/abs/2408.07545
Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the number of variables and complexity of interactions between them does, too
Externí odkaz:
http://arxiv.org/abs/2310.08377
Publikováno v:
Transactions in Machine Learning Research (08/2023)
Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why sometimes we might feel otherwise. To this end, we define and exemplify a
Externí odkaz:
http://arxiv.org/abs/2308.13067
This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history
Externí odkaz:
http://arxiv.org/abs/2212.12575
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in any other growing subfield, patience seems to be a virtue since signifi
Externí odkaz:
http://arxiv.org/abs/2212.12570
Autor:
Didi, Kieran, Zečević, Matej
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaw
Externí odkaz:
http://arxiv.org/abs/2212.12560
Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the ot
Externí odkaz:
http://arxiv.org/abs/2206.10591
Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exist solvers for even high-dimensional LPs, understanding said high-dime
Externí odkaz:
http://arxiv.org/abs/2206.07203
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques. In this paper, we propose a new approach in an attempt
Externí odkaz:
http://arxiv.org/abs/2206.07196