Search Results for author: Chris Wendler

Found 5 papers, 3 papers with code

Causal Fourier Analysis on Directed Acyclic Graphs and Posets

no code implementations16 Sep 2022 Bastian Seifert, Chris Wendler, Markus Püschel

DAGs are the common model to capture causal relationships between data and our framework is causal in that shift, convolution, and Fourier transform are computed only from predecessors in the DAG.

Instance-wise algorithm configuration with graph neural networks

1 code implementation10 Feb 2022 Romeo Valentin, Claudio Ferrari, Jérémy Scheurer, Andisheh Amrollahi, Chris Wendler, Max B. Paulus

We pose this task as a supervised learning problem: First, we compile a large dataset of the solver performance for various configurations and all provided MILP instances.

Combinatorial Optimization

Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases

2 code implementations1 Oct 2020 Chris Wendler, Andisheh Amrollahi, Bastian Seifert, Andreas Krause, Markus Püschel

Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain.

Recommendation Systems

Discrete Signal Processing with Set Functions

no code implementations28 Jan 2020 Markus Püschel, Chris Wendler

Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have been used, for example, to formally describe or quantify loss functions for semantic image segmentation, the informativeness of sensors in sensor networks the utility of sets of items in recommender systems, cooperative games in game theory, or bidders in combinatorial auctions.

Image Segmentation Informativeness +2

Powerset Convolutional Neural Networks

1 code implementation NeurIPS 2019 Chris Wendler, Dan Alistarh, Markus Püschel

We present a novel class of convolutional neural networks (CNNs) for set functions, i. e., data indexed with the powerset of a finite set.

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