Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah, Andrew Ilyas, Aleksander Mądry

International Conference on Machine Learning
(ICML), 2024

H. Shah, A. Ilyas, A. Mądry

ICML 2024

How does the internal computation of a machine learning model transform inputs into predictions? In this paper, we introduce a task called component modeling that aims to address this question. The goal of component modeling is to decompose an ML model's prediction in terms of its components -- simple functions (e.g., convolution filters, attention heads) that are the "building blocks" of model computation. We focus on a special case of this task, component attribution, where the goal is to estimate the counterfactual impact of individual components on a given prediction. We then present COAR, a scalable algorithm for estimating component attributions; we demonstrate its effectiveness across models, datasets, and modalities. Finally, we show that component attributions estimated with COAR directly enable model editing across five tasks, namely: fixing model errors, ``forgetting'' specific classes, boosting subpopulation robustness, localizing backdoor attacks, and improving robustness to typographic attacks. We provide code for COAR at github.com/MadryLab/modelcomponents

@inproceedings{shah2024decomposing,
title={Decomposing and Editing Predictions by Modeling Model Computation},
author={Harshay Shah and Andrew Ilyas and Aleksander Madry},
booktitle={Arxiv preprint arXiv:2404.11534},
year={2024}
}

ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah*, Sung Min Park*, Andrew Ilyas*, Aleksander Mądry

International Conference on Machine Learning
(ICML), 2023

+ Workshop on Spurious Correlations, Invariance, and Stability
(ICML SCIS), 2023

H. Shah*, S. M. Park*, A. Ilyas*, A. Mądry

ICML 2023

ICML SCIS, 2023

We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present ModelDiff, a method that leverages the datamodels framework (Ilyas et al., 2022) to compare learning algorithms based on how they use their training data. We demonstrate ModelDiff through three case studies, comparing models trained with/without data augmentation, with/without pre-training, and with different SGD hyperparameters. Our code is available at github.com/MadryLab/modeldiff

@inproceedings{shah2023modeldiff,
title={Modeldiff: A framework for comparing learning algorithms},
author={Shah, Harshay and Park, Sung Min and Ilyas, Andrew and Madry, Aleksander},
booktitle={International Conference on Machine Learning},
pages={30646--30688},
year={2023},
organization={PMLR}
}

The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

Neural Information Processing Systems
(NeurIPS), 2020

+ Workshop on Uncertainty and Robustness in Deep Learning
(ICML UDL), 2020

H. Shah, K. Tamuly, A. Raghunathan, P. Jain, P. Netrapalli

NeurIPS 2020

ICML UDL, 2020

Several works have proposed Simplicity Bias (SB)—the tendency of standard training procedures such as Stochastic Gradient Descent (SGD) to find simple models—to justify why neural networks generalize well [Arpit et al. 2017, Nakkiran et al. 2019, Soudry et al. 2018]. However, the precise notion of simplicity remains vague. Furthermore, previous settings that use SB to theoretically justify why neural networks generalize well do not simultaneously capture the non-robustness of neural networks—a widely observed phenomenon in practice [Goodfellow et al. 2014, Jo and Bengio 2017]. We attempt to reconcile SB and the superior standard generalization of neural networks with the non-robustness observed in practice by designing datasets that (a) incorporate a precise notion of simplicity, (b) comprise multiple predictive features with varying levels of simplicity, and (c) capture the non-robustness of neural networks trained on real data. Through theory and empirics on these datasets, we make four observations: (i) SB of SGD and variants can be extreme: neural networks can exclusively rely on the simplest feature and remain invariant to all predictive complex features. (ii) The extreme aspect of SB could explain why seemingly benign distribution shifts and small adversarial perturbations significantly degrade model performance. (iii) Contrary to conventional wisdom, SB can also hurt generalization on the same data distribution, as SB persists even when the simplest feature has less predictive power than the more complex features. (iv) Common approaches to improve generalization and robustness—ensembles and adversarial training—can fail in mitigating SB and its pitfalls. Given the role of SB in training neural networks, we hope that the proposed datasets and methods serve as an effective testbed to evaluate novel algorithmic approaches aimed at avoiding the pitfalls of SB; code and data available at github.com/harshays/simplicitybiaspitfalls.

@article{shah2020pitfalls,
title={The Pitfalls of Simplicity Bias in Neural Networks},
author={Shah, Harshay and Tamuly, Kaustav and Raghunathan, Aditi and Jain, Prateek and Netrapalli, Praneeth},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}

Growing Attributed Networks through Local Processes
Harshay Shah, Suhansanu Kumar, Hari Sundaram

World Wide Web Conference
(WWW), 2019

H. Shah, S. Kumar, H. Sundaram

WWW, 2019

This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of rich structural properties found in real-world networks. We make three contributions. First, we propose a parsimonious and accurate model of attributed network growth that jointly explains the emergence of in-degree distributions, local clustering, clustering-degree relationship and attribute mixing patterns. Second, our model is based on biased random walks and uses local processes to form edges without recourse to global network information. Third, we account for multiple sociological phenomena: bounded rationality, structural constraints, triadic closure, attribute homophily, and preferential attachment. Our experiments indicate that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of six real-world networks; it improves upon the performance of eight state-of-the-art models by a statistically significant margin of 2.5-10x.

@inproceedings{shah2019growing,
title={Growing Attributed Networks through Local Processes},
author={Shah, Harshay and Kumar, Suhansanu and Sundaram, Hari},
booktitle={The World Wide Web Conference},
pages={3208--3214},
year={2019},
organization={ACM}
}

Number of Connected Components in a Graph: Estimation via Counting Patterns
Ashish Khetan, Harshay Shah, Sewoong Oh

Preprint: arXiv:1812.00139, 2018

A. Khetan, H. Shah, S. Oh

Due to resource constraints and restricted access to large-scale graph datasets, it is often necessary to work with a sampled subgraph of a larger original graph. The task of inferring a global property of the original graph from the sampled subgraph is of fundamental interest. In this work, we focus on estimating the number of connected components. Due to the inherent difficulty of the problem for general graphs, little is known about the connection between the number of connected components in the observed subgraph and the original graph. To make this connection, we propose a highly redundant motif-based representation of the observed subgraph, which, at first glance, may seem counter-intuitive. However, the proposed representation is crucial in introducing a novel estimator for the number of connected components in general graphs. The connection is made precise via the Schatten \(k\)-norms of the graph Laplacian and the spectral representation of the number of connected components. We provide a guarantee on the resulting mean squared error that characterizes the bias-variance trade-off. Furthermore, our experiments on synthetic and real-world graphs show that we improve upon competing algorithms for graphs with spectral gaps bounded away from zero.

@article{khetan2018number,
title={Number of Connected Components in a Graph: Estimation via Counting Patterns},
author={Khetan, Ashish and Shah, Harshay and Oh, Sewoong},
journal={arXiv preprint arXiv:1812.00139},
year={2018}
}