Kristian Kersting

research

∙ 06/24/2022

HANF: Hyperparameter And Neural Architecture Search in Federated Learning

Automated machine learning (AutoML) is an important step to make machine...

Jonas Seng, et al. ∙

research

∙ 06/14/2022

Attributions Beyond Neural Networks: The Linear Program Case

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

Florian Peter Busch, et al. ∙

research

∙ 06/14/2022

Can Foundation Models Talk Causality?

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

Moritz Willig, et al. ∙

research

∙ 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...

Salahedine Youssef, et al. ∙

research

∙ 06/14/2022

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation

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

Jonas Seng, et al. ∙

research

∙ 06/14/2022

Machines Explaining Linear Programs

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

David Steinmann, et al. ∙

research

∙ 05/16/2022

Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

Counterfactual examples are an appealing class of post-hoc explanations ...

Xiaoting Shao, et al. ∙

research

∙ 05/03/2022

Adaptable Adapters

State-of-the-art pretrained NLP models contain a hundred million to tril...

Nafise Sadat Moosavi, et al. ∙

research

∙ 03/29/2022

Finding Structure and Causality in Linear Programs

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

Matej Zečević, et al. ∙

research

∙ 03/18/2022

Do Multilingual Language Models Capture Differing Moral Norms?

Massively multilingual sentence representations are trained on large cor...

Katharina Hämmerl, et al. ∙

research

∙ 03/04/2022

A Typology to Explore and Guide Explanatory Interactive Machine Learning

Recently, more and more eXplanatory Interactive machine Learning (XIL) m...

Felix Friedrich, et al. ∙

research

∙ 03/02/2022

Neuro-Symbolic Verification of Deep Neural Networks

Formal verification has emerged as a powerful approach to ensure the saf...

Xuan Xie, et al. ∙

research

∙ 02/14/2022

Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

Large datasets underlying much of current machine learning raise serious...

Patrick Schramowski, et al. ∙

research

∙ 02/01/2022

Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement

A key assumption of most statistical machine learning methods is that th...

Xiaoting Shao, et al. ∙

research

∙ 01/28/2022

Plug Play Attacks: Towards Robust and Flexible Model Inversion Attacks

Model inversion attacks (MIAs) aim to create synthetic images that refle...

Lukas Struppek, et al. ∙

research

∙ 12/04/2021

Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Learning visual concepts from raw images without strong supervision is a...

Wolfgang Stammer, et al. ∙

research

∙ 11/17/2021

Do Not Trust Prediction Scores for Membership Inference Attacks

Membership inference attacks (MIAs) aim to determine whether a specific ...

Dominik Hintersdorf, et al. ∙

research

∙ 11/12/2021

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash

Apple recently revealed its deep perceptual hashing system NeuralHash to...

Lukas Struppek, et al. ∙

research

∙ 10/22/2021

The Causal Loss: Driving Correlation to Imply Causation

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

Moritz Willig, et al. ∙

research

∙ 10/22/2021

On the Tractability of Neural Causal Inference

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

Matej Zečević, et al. ∙

research

∙ 10/19/2021

Explaining Deep Tractable Probabilistic Models: The sum-product network case

We consider the problem of explaining a tractable deep probabilistic mod...

Athresh Karanam, et al. ∙

research

∙ 10/18/2021

Neuro-Symbolic Forward Reasoning

Reasoning is an essential part of human intelligence and thus has been a...

Hikaru Shindo, et al. ∙

research

∙ 10/08/2021

Inferring Offensiveness In Images From Natural Language Supervision

Probing or fine-tuning (large-scale) pre-trained models results in state...

Patrick Schramowski, et al. ∙

research

∙ 10/07/2021

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming

The goal of combining the robustness of neural networks and the expressi...

Arseny Skryagin, et al. ∙

research

∙ 10/07/2021

CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability

What is the state of the art in continual machine learning? Although a n...

Martin Mundt, et al. ∙

research

∙ 10/05/2021

Structural Causal Interpretation Theorem

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

Matej Zečević, et al. ∙

research

∙ 09/14/2021

Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits

Probabilistic circuits (PCs) have become the de-facto standard for learn...

Zhongjie Yu, et al. ∙

research

∙ 09/13/2021

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection

Object detection is a fundamental task in computer vision. While approac...

Steven Lang, et al. ∙

research

∙ 09/09/2021

Relating Graph Neural Networks to Structural Causal Models

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

Matej Zečević, et al. ∙

research

∙ 07/19/2021

Generative Adversarial Neural Cellular Automata

Motivated by the interaction between cells, the recently introduced conc...

Maximilian Otte, et al. ∙

research

∙ 06/16/2021

Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression

Inspired by recent advances in the field of expert-based approximations ...

Zhongjie Yu, et al. ∙

research

∙ 06/08/2021

RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting

Time series forecasting is a relevant task that is performed in several ...

Nils Thoma, et al. ∙

research

∙ 05/26/2021

Intriguing Parameters of Structural Causal Models

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

Matej Zečević, et al. ∙

research

∙ 05/19/2021

User Label Leakage from Gradients in Federated Learning

Federated learning enables multiple users to build a joint model by shar...

Aidmar Wainakh, et al. ∙

research

∙ 04/02/2021

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation

We present ObSuRF, a method which turns a single image of a scene into a...

Karl Stelzner, et al. ∙

research

∙ 03/08/2021

Language Models have a Moral Dimension

Artificial writing is permeating our lives due to recent advances in lar...

Patrick Schramowski, et al. ∙

research

∙ 02/20/2021

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

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

Matej Zečević, et al. ∙

research

∙ 02/18/2021

Recurrent Rational Networks

Latest insights from biology show that intelligence does not only emerge...

Quentin Delfosse, et al. ∙

research

∙ 12/20/2020

Monte-Carlo Graph Search for AlphaZero

The AlphaZero algorithm has been successfully applied in a range of disc...

Johannes Czech, et al. ∙

research

∙ 12/15/2020

Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation

Most deep neural networks are considered to be black boxes, meaning thei...

Sophie Burkhardt, et al. ∙

research

∙ 11/25/2020

Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations

Most explanation methods in deep learning map importance estimates for a...

Wolfgang Stammer, et al. ∙

research

∙ 09/11/2020

Alfie: An Interactive Robot with a Moral Compass

This work introduces Alfie, an interactive robot that is capable of answ...

Cigdem Turan, et al. ∙

research

∙ 07/16/2020

TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning...

Christopher Morris, et al. ∙

research

∙ 06/10/2020

Fitted Q-Learning for Relational Domains

We consider the problem of Approximate Dynamic Programming in relational...

Srijita Das, et al. ∙

research

∙ 04/13/2020

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...

Robert Peharz, et al. ∙

research

∙ 02/03/2020

CryptoSPN: Privacy-preserving Sum-Product Network Inference

AI algorithms, and machine learning (ML) techniques in particular, are i...

Amos Treiber, et al. ∙

research

∙ 01/15/2020

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

Deep neural networks have shown excellent performances in many real-worl...

Patrick Schramowski, et al. ∙

research

∙ 12/11/2019

BERT has a Moral Compass: Improvements of ethical and moral values of machines

Allowing machines to choose whether to kill humans would be devastating ...

Patrick Schramowski, et al. ∙

research

∙ 10/06/2019

Structured Object-Aware Physics Prediction for Video Modeling and Planning

When humans observe a physical system, they can easily locate objects, u...

Jannik Kossen, et al. ∙

research

∙ 09/02/2019

DeepDB: Learn from Data, not from Queries!

The typical approach for learned DBMS components is to capture the behav...

Benjamin Hilprecht, et al. ∙

Kristian Kersting

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