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Pascal Sturmfels

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JMLR Journal 2021 Journal Article

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Joseph D. Janizek
Pascal Sturmfels
Su-In Lee

Recent work has shown great promise in explaining neural network behavior. In particular, feature attribution methods explain the features that are important to a model's prediction on a given input. However, for many tasks, simply identifying significant features may be insufficient for understanding model behavior. The interactions between features within the model may better explain not only the model, but why certain features outrank others in importance. In this work, we present Integrated Hessians, an extension of Integrated Gradients that explains pairwise feature interactions in neural networks. Integrated Hessians overcomes several theoretical limitations of previous methods, and unlike them, is not limited to a specific architecture or class of neural network. Additionally, we find that our method is faster than existing methods when the number of features is large, and outperforms previous methods on existing quantitative benchmarks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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TCS Journal 2019 Journal Article

Select and permute: An improved online framework for scheduling to minimize weighted completion time

Samir Khuller
Jingling Li
Pascal Sturmfels
Kevin Sun
Prayaag Venkat

In this paper, we introduce a new online scheduling framework for minimizing total weighted completion time in a general setting. The framework is inspired by the work of Hall et al. (1997) [11] and Garg et al. (2007) [9], who show how to convert an offline approximation to an online scheme. Our framework uses two offline approximation algorithms—one for the simpler problem of scheduling without release times, and another for the minimum unscheduled weight problem—to create an online algorithm with provably good competitive ratios. We illustrate multiple applications of this method that yield improved competitive ratios. Our framework gives algorithms with the best or only-known competitive ratios for the concurrent open shop, coflow, and concurrent cluster models. We also introduce a randomized variant of our framework based on the ideas of Chakrabarti et al. (1996) [3] and use it to achieve improved competitive ratios for these same problems.

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