1 code implementation • 29 Jan 2024 • Ian Covert, Chanwoo Kim, Su-In Lee, James Zou, Tatsunori Hashimoto
Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and can be intractable for large datasets.
1 code implementation • 5 Jun 2023 • Soham Gadgil, Ian Covert, Su-In Lee
Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions.
1 code implementation • 2 Jan 2023 • Ian Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan White, Su-In Lee
Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets.
2 code implementations • ICCV 2023 • Sarah Pratt, Ian Covert, Rosanne Liu, Ali Farhadi
Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of categories specified with natural language during inference.
2 code implementations • 10 Jun 2022 • Ian Covert, Chanwoo Kim, Su-In Lee
Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem.
4 code implementations • ICLR 2022 • Neil Jethani, Mukund Sudarshan, Ian Covert, Su-In Lee, Rajesh Ranganath
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations.
no code implementations • ICML Workshop AML 2021 • Ivan Evtimov, Ian Covert, Aditya Kusupati, Tadayoshi Kohno
When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes.
3 code implementations • 2 Dec 2020 • Ian Covert, Su-In Lee
The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, particularly in the model-agnostic setting.
3 code implementations • 21 Nov 2020 • Ian Covert, Scott Lundberg, Su-In Lee
We describe a new unified class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature's influence.
1 code implementation • 6 Nov 2020 • Ian Covert, Scott Lundberg, Su-In Lee
Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another.
3 code implementations • NeurIPS 2020 • Ian Covert, Scott Lundberg, Su-In Lee
Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability.
no code implementations • 25 Sep 2019 • Ian Covert, Uygar Sumbul, Su-In Lee
Unsupervised feature selection involves finding a small number of highly informative features, in the absence of a specific supervised learning task.
no code implementations • 3 May 2019 • Ian Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po
Commonly used deep learning models for time series don't offer a way to leverage structural information, but this would be desirable in a model for structural time series.
3 code implementations • 16 Feb 2018 • Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, Emily Fox
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.