Author name cluster

Andisheh Amrollahi

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

SHAP values via sparse Fourier representation

Ali Gorji
Andisheh Amrollahi
Andreas Krause

SHAP (SHapley Additive exPlanations) values are a widely used method for local feature attribution in interpretable and explainable AI. We propose an efficient two-stage algorithm for computing SHAP values in both black-box setting and tree-based models. Motivated by spectral bias in real-world predictors, we first approximate models using compact Fourier representations, exactly for trees and approximately for black-box models. In the second stage, we introduce a closed-form formula for {\em exactly} computing SHAP values using the Fourier representation, that ``linearizes'' the computation into a simple summation and is amenable to parallelization. As the Fourier approximation is computed only once, our method enables amortized SHAP value computation, achieving significant speedups over existing methods and a tunable trade-off between efficiency and precision.

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UAI Conference 2023 Conference Paper

A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks

Ali Gorji
Andisheh Amrollahi
Andreas Krause 0001

Despite the capacity of neural nets to learn arbitrary functions, models trained through gradient descent often exhibit a bias towards “simpler” functions. Various notions of simplicity have been introduced to characterize this behavior. Here, we focus on the case of neural networks with discrete (zero-one) inputs through the lens of their Fourier (Walsh-Hadamard) transforms, where the notion of simplicity can be captured through the degree of the Fourier coefficients. We empirically show that neural networks have a tendency to learn lower-degree frequencies. We show how this spectral bias towards simpler features can in fact hurt the neural network’s generalization on real-world datasets. To remedy this we propose a new scalable functional regularization scheme that aids the neural network to learn higher degree frequencies. Our regularizer also helps avoid erroneous identification of low-degree frequencies, which further improves generalization. We extensively evaluate our regularizer on synthetic datasets to gain insights into its behavior. Finally, we show significantly improved generalization on four different datasets compared to standard neural networks and other relevant baselines.

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AAAI Conference 2021 Conference Paper

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

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. In this work, we present a new family of algorithms for learning Fourier-sparse set functions. They require at most nk − k log2 k + k queries (set function evaluations), under mild conditions on the Fourier coefficients, where n is the size of the ground set and k the number of non-zero Fourier coefficients. In contrast to other work that focused on the orthogonal Walsh-Hadamard transform (WHT), our novel algorithms operate with recently introduced nonorthogonal Fourier transforms that offer different notions of Fourier-sparsity. These naturally arise when modeling, e. g. , sets of items forming substitutes and complements. We demonstrate effectiveness on several real-world applications.

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NeurIPS Conference 2019 Conference Paper

Efficiently Learning Fourier Sparse Set Functions

Andisheh Amrollahi
Amir Zandieh
Michael Kapralov
Andreas Krause

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. 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. This is a wide class, that includes graph and hypergraph cut functions, decision trees and more. Our central contribution is the first algorithm that allows learning functions whose Fourier support only contains low degree (say degree $d=o(n)$) polynomials using $O(k d \log n)$ sample complexity and runtime $O( kn \log^2 k \log n \log d)$. This implies that sparse graphs with $k$ edges can, for the first time, be learned from $O(k \log n)$ observations of cut values and in linear time in the number of vertices. Our algorithm can also efficiently learn (sums of) decision trees of small depth. The algorithm exploits techniques from the sparse Fourier transform literature and is easily implementable. Lastly, we also develop an efficient robust version of our algorithm and prove $\ell_2/\ell_2$ approximation guarantees without any statistical assumptions on the noise.

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