Search Results for author: Andisheh Amrollahi

Found 4 papers, 3 papers with code

A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks

no code implementations16 May 2023 Ali Gorji, Andisheh Amrollahi, Andreas Krause

We show how this spectral bias towards low-degree frequencies can in fact hurt the neural network's generalization on real-world datasets.

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

3 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

Efficiently Learning Fourier Sparse Set Functions

1 code implementation NeurIPS 2019 Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause

In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size $n$ and that are sparse (say $k$-sparse) in the Fourier domain.

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