Matej Zečević | DeepAI

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∙ 08/24/2023

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal

Some argue scale is all what is needed to achieve AI, covering even caus...

0 Matej Zečević, et al. ∙

∙ 12/23/2022

Continual Causal Abstractions

This short paper discusses continually updated causal abstractions as a ...

0 Matej Zečević, et al. ∙

∙ 12/23/2022

Pearl Causal Hierarchy on Image Data: Intricacies Challenges

Many researchers have voiced their support towards Pearl's counterfactua...

20 Matej Zečević, et al. ∙

∙ 12/23/2022

On How AI Needs to Change to Advance the Science of Drug Discovery

Research around AI for Science has seen significant success since the ri...

0 Kieran Didi, et al. ∙

∙ 06/14/2022

Attributions Beyond Neural Networks: The Linear Program Case

Linear Programs (LPs) have been one of the building blocks in machine le...

13 Florian Peter Busch, et al. ∙

∙ 06/14/2022

Can Foundation Models Talk Causality?

Foundation models are subject to an ongoing heated debate, leaving open ...

11 Moritz Willig, et al. ∙

∙ 06/14/2022

Towards a Solution to Bongard Problems: A Causal Approach

To date, Bongard Problems (BP) remain one of the few fortresses of AI hi...

0 Salahedine Youssef, et al. ∙

∙ 06/14/2022

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation

Simulations are ubiquitous in machine learning. Especially in graph lear...

4 Jonas Seng, et al. ∙

∙ 06/14/2022

Machines Explaining Linear Programs

There has been a recent push in making machine learning models more inte...

10 David Steinmann, et al. ∙

∙ 03/29/2022

Finding Structure and Causality in Linear Programs

Linear Programs (LP) are celebrated widely, particularly so in machine l...

7 Matej Zečević, et al. ∙

∙ 10/22/2021

The Causal Loss: Driving Correlation to Imply Causation

Most algorithms in classical and contemporary machine learning focus on ...

9 Moritz Willig, et al. ∙

∙ 10/22/2021

On the Tractability of Neural Causal Inference

Roth (1996) proved that any form of marginal inference with probabilisti...

9 Matej Zečević, et al. ∙

∙ 10/05/2021

Structural Causal Interpretation Theorem

Human mental processes allow for qualitative reasoning about causality i...

19 Matej Zečević, et al. ∙

∙ 09/09/2021

Relating Graph Neural Networks to Structural Causal Models

Causality can be described in terms of a structural causal model (SCM) t...

34 Matej Zečević, et al. ∙

∙ 05/26/2021

Intriguing Parameters of Structural Causal Models

In recent years there has been a lot of focus on adversarial attacks, es...

3 Matej Zečević, et al. ∙

∙ 02/20/2021

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

While probabilistic models are an important tool for studying causality,...

16 Matej Zečević, et al. ∙

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