Search Results for author: Kristian Kersting

Found 68 papers, 21 papers with code

Do Not Trust Prediction Scores for Membership Inference Attacks

1 code implementation17 Nov 2021 Dominik Hintersdorf, Lukas Struppek, Kristian Kersting

Membership inference attacks (MIAs) aim to determine whether a specific sample was used to train a predictive model.

The Causal Loss: Driving Correlation to Imply Causation

no code implementations22 Oct 2021 Moritz Willig, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance.

On the Tractability of Neural Causal Inference

no code implementations22 Oct 2021 Matej Zečević, Devendra Singh Dhami, Kristian Kersting

Roth (1996) proved that any form of marginal inference with probabilistic graphical models (e. g. Bayesian Networks) will at least be NP-hard.

Causal Inference

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

no code implementations19 Oct 2021 Athresh Karanam, Saurabh Mathur, Predrag Radivojac, Kristian Kersting, Sriraam Natarajan

We consider the problem of explaining a tractable deep probabilistic model, the Sum-Product Networks (SPNs). To this effect, we define the notion of a context-specific independence tree and present an iterative algorithm that converts an SPN to a CSI-tree.

Neuro-Symbolic Forward Reasoning

no code implementations18 Oct 2021 Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

NSFR factorizes the raw inputs into the object-centric representations, converts them into probabilistic ground atoms, and finally performs differentiable forward-chaining inference using weighted rules for inference.

Inferring Offensiveness In Images From Natural Language Supervision

1 code implementation8 Oct 2021 Patrick Schramowski, Kristian Kersting

Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined with image data.

Fine-tuning

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming

no code implementations7 Oct 2021 Arseny Skryagin, Wolfgang Stammer, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

The probability estimates resulting from NPPs act as the binding element between the logical program and raw input data, thereby allowing SLASH to answer task-dependent logical queries.

Probabilistic Programming

Structural Causal Interpretation Theorem

no code implementations5 Oct 2021 Matej Zečević, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting

By defining a metric space on SCM, we provide a theoretical perspective on the comparison of mental models and thereby conclude that interpretations can be used for guiding a learning system towards true causality.

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

no code implementations14 Sep 2021 Zhongjie Yu, Devendra Singh Dhami, Kristian Kersting

Probabilistic circuits (PCs) have become the de-facto standard for learning and inference in probabilistic modeling.

Relating Graph Neural Networks to Structural Causal Models

no code implementations9 Sep 2021 Matej Zečević, Devendra Singh Dhami, Petar Veličković, Kristian Kersting

Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations.

Causal Inference

Generative Adversarial Neural Cellular Automata

no code implementations19 Jul 2021 Maximilian Otte, Quentin Delfosse, Johannes Czech, Kristian Kersting

Motivated by the interaction between cells, the recently introduced concept of Neural Cellular Automata shows promising results in a variety of tasks.

Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression

1 code implementation16 Jun 2021 Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting

Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale multi-output regression using single-output GP experts.

Gaussian Processes

RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting

no code implementations8 Jun 2021 Nils Thoma, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting

Time series forecasting is a relevant task that is performed in several real-world scenarios such as product sales analysis and prediction of energy demand.

Time Series Time Series Forecasting

Intriguing Parameters of Structural Causal Models

1 code implementation26 May 2021 Matej Zečević, Devendra Singh Dhami, Kristian Kersting

In recent years there has been a lot of focus on adversarial attacks, especially on deep neural networks.

Combinatorial Optimization

User Label Leakage from Gradients in Federated Learning

no code implementations19 May 2021 Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, Max Mühlhäuser

Specifically, we propose Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users' training data from their shared gradients.

Federated Learning

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation

no code implementations2 Apr 2021 Karl Stelzner, Kristian Kersting, Adam R. Kosiorek

We present ObSuRF, a method which turns a single image of a scene into a 3D model represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to a different object.

Semantic Segmentation

Language Models have a Moral Dimension

1 code implementation8 Mar 2021 Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin Rothkopf, Kristian Kersting

While this is well established, we show that recent improvements of LMs also store ethical and moral norms of the society and actually bring a "moral direction" to surface.

Fine-tuning

Deep Rational Reinforcement Learning

3 code implementations18 Feb 2021 Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting

Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.

Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)

Atari Games General Reinforcement Learning +1

Monte-Carlo Graph Search for AlphaZero

2 code implementations20 Dec 2020 Johannes Czech, Patrick Korus, Kristian Kersting

The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games.

Board Games

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

3 code implementations CVPR 2021 Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space.

TUDataset: A collection of benchmark datasets for learning with graphs

2 code implementations16 Jul 2020 Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann

We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.

Graph Classification

Fitted Q-Learning for Relational Domains

no code implementations10 Jun 2020 Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting

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

Q-Learning

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

no code implementations ICML 2020 Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines.

CryptoSPN: Privacy-preserving Sum-Product Network Inference

no code implementations3 Feb 2020 Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting

AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e. g., the European GDPR.

Structured Object-Aware Physics Prediction for Video Modeling and Planning

1 code implementation ICLR 2020 Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions.

Meta-Learning Runge-Kutta

no code implementations25 Sep 2019 Nadine Behrmann, Patrick Schramowski, Kristian Kersting

However, by studying the characteristics of the local error function we show that including the partial derivatives of the initial value problem is favorable.

Meta-Learning Numerical Integration

DeepDB: Learn from Data, not from Queries!

1 code implementation2 Sep 2019 Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig

The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model.

Databases

Neural Networks for Relational Data

1 code implementation28 Aug 2019 Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan

While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domains such as those arising in applications like social network analysis.

Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

2 code implementations19 Aug 2019 Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, Johannes Fürnkranz

Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo.

Board Games

Random Sum-Product Forests with Residual Links

no code implementations8 Aug 2019 Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting

Tractable yet expressive density estimators are a key building block of probabilistic machine learning.

Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

5 code implementations ICLR 2020 Alejandro Molina, Patrick Schramowski, Kristian Kersting

The performance of deep network learning strongly depends on the choice of the non-linear activation function associated with each neuron.

Declarative Learning-Based Programming as an Interface to AI Systems

no code implementations18 Jun 2019 Parisa Kordjamshidi, Dan Roth, Kristian Kersting

Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry.

Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning

no code implementations22 May 2019 Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, Paolo Torroni

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.

Component Classification Link Prediction +2

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

no code implementations21 May 2019 Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting

In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.

Image Classification

Was ist eine Professur fuer Kuenstliche Intelligenz?

no code implementations17 Feb 2019 Kristian Kersting, Jan Peters, Constantin Rothkopf

The Federal Government of Germany aims to boost the research in the field of Artificial Intelligence (AI).

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

1 code implementation11 Jan 2019 Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting

We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).

Model-based Approximate Query Processing

no code implementations15 Nov 2018 Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting

However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations.

Automatic Bayesian Density Analysis

no code implementations24 Jul 2018 Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera

Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.

Anomaly Detection Bayesian Inference +1

Probabilistic Deep Learning using Random Sum-Product Networks

no code implementations5 Jun 2018 Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani

The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods.

Probabilistic Deep Learning

Neural Conditional Gradients

no code implementations12 Mar 2018 Patrick Schramowski, Christian Bauckhage, Kristian Kersting

The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers.

Lifted Filtering via Exchangeable Decomposition

no code implementations31 Jan 2018 Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste

We present a model for exact recursive Bayesian filtering based on lifted multiset states.

Sum-Product Networks for Hybrid Domains

no code implementations9 Oct 2017 Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting

While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult.

Coresets for Dependency Networks

no code implementations9 Oct 2017 Alejandro Molina, Alexander Munteanu, Kristian Kersting

Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables.

Global Weisfeiler-Lehman Graph Kernels

no code implementations7 Mar 2017 Christopher Morris, Kristian Kersting, Petra Mutzel

Specifically, we introduce a novel graph kernel based on the $k$-dimensional Weisfeiler-Lehman algorithm.

General Classification Graph Classification

A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels

no code implementations2 Mar 2017 Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel

On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.

Faster Kernels for Graphs with Continuous Attributes via Hashing

no code implementations1 Oct 2016 Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel

While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.

Lifted Convex Quadratic Programming

no code implementations14 Jun 2016 Martin Mladenov, Leonard Kleinhans, Kristian Kersting

Symmetry is the essential element of lifted inference that has recently demon- strated the possibility to perform very efficient inference in highly-connected, but symmetric probabilistic models models.

How is a data-driven approach better than random choice in label space division for multi-label classification?

no code implementations7 Jun 2016 Piotr Szymański, Tomasz Kajdanowicz, Kristian Kersting

We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning.

Community Detection General Classification +1

The Symbolic Interior Point Method

no code implementations26 May 2016 Martin Mladenov, Vaishak Belle, Kristian Kersting

A recent trend in probabilistic inference emphasizes the codification of models in a formal syntax, with suitable high-level features such as individuals, relations, and connectives, enabling descriptive clarity, succinctness and circumventing the need for the modeler to engineer a custom solver.

Decision Making

Propagation Kernels

1 code implementation13 Oct 2014 Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data.

Relational Linear Programs

no code implementations12 Oct 2014 Kristian Kersting, Martin Mladenov, Pavel Tokmakov

A relational linear program (RLP) is a declarative LP template defining the objective and the constraints through the logical concepts of objects, relations, and quantified variables.

Mind the Nuisance: Gaussian Process Classification using Privileged Noise

no code implementations NeurIPS 2014 Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto

That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function.

Classification General Classification

Efficient Information Theoretic Clustering on Discrete Lattices

no code implementations26 Oct 2013 Christian Bauckhage, Kristian Kersting

We consider the problem of clustering data that reside on discrete, low dimensional lattices.

Semantic Segmentation

Dimension Reduction via Colour Refinement

no code implementations22 Jul 2013 Martin Grohe, Kristian Kersting, Martin Mladenov, Erkal Selman

We demonstrate empirically that colour refinement can indeed greatly reduce the cost of solving linear programs.

Dimensionality Reduction

Symbolic Dynamic Programming for Continuous State and Observation POMDPs

no code implementations NeurIPS 2012 Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting

In recent years, point- based value iteration methods have proven to be extremely effective techniques for finding (approximately) optimal dynamic programming solutions to POMDPs when an initial set of belief states is known.

Decision Making

Bayesian Logic Programs

no code implementations23 Nov 2001 Kristian Kersting, Luc De Raedt

Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.

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