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Robust and stable black box explanations

WebApr 30, 2024 · The explanation consists of two parts: (i) a set of exemplars and counter-exemplars images illustrating, respectively, instances classified with the same label and with a different label than the instance to explain, which may be visually analyzed to understand the reasons for the classification, and (ii) a saliency map highlighting the areas of … WebDec 10, 2024 · Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders,...

Robust and Stable Black Box Explanations - Harvard …

Web§However, post hoc explanations have been shown to be unstable and unreliable §Small perturbations to input can substantially change the explanations; running same algorithm … WebApr 12, 2024 · BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning ... Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning Jinwoo Kim · Janghyuk Choi · Ho-Jin Choi · Seon Joo Kim ... Spatial-temporal Concept based Explanation of 3D ConvNets nyct transit adjudication bureau https://leesguysandgals.com

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http://proceedings.mlr.press/v119/lakkaraju20a/lakkaraju20a.pdf WebApr 10, 2024 · Shapley additive explanations, applied to four protected areas across the species range, were valuable for understanding which climatic predictors drove predicted ocelot habitat at a local scale, with the protected areas with the highest predicted values having consistently more precipitation and higher temperatures. WebRobust and Stable Black Box Explanations Hima Lakkaraju · Nino Arsov · Osbert Bastani Virtual Keywords: [ Supervised Learning ] [ Accountability, Transparency and Interpretability ] [ Abstract ] [ Slides ] Wed 15 Jul 5 a.m. PDT — 5:45 a.m. PDT Wed 15 Jul 4 … nyct transit forum

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Robust and stable black box explanations

[2011.06169] Robust and Stable Black Box Explanations - arXiv.org

WebNov 12, 2024 · We propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax … WebFeb 24, 2024 · The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept.

Robust and stable black box explanations

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WebRobust and Stable Black Box Explanations By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani As machine learning black boxes are increasingly being deployed in real-world … WebWe propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax objective that …

http://proceedings.mlr.press/v119/lakkaraju20a.html WebJul 13, 2024 · We propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax …

WebAs machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the …

WebAug 16, 2024 · Recently, I came across the paper Robust and Stable Black Box Explanations, which discusses a nice framework for global model-agnostic explanations. I was thinking …

WebRobust and Stable Black Box Explanations. Lakkaraju et. al., 2024 pdf Evaluating Interpretability Evaluate interpretability (does the explanations make sense to human or not). Towards A Rigorous Science of Interpretable Machine Learning. Doshi-Velez & Kim. 2024 pdf nyc t shirt printersWebApr 17, 2024 · In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering … nyctv shortsWebAug 11, 2024 · This work makes the first attempt at addressing several critical issues with popular explanation methods in one shot, thereby generating consistent, stable, and reliable explanations with guarantees in a computationally efficient manner. nyc tv listings on tonightWebRobust Rule Based Explanations §Approximate the objective using sampling §Adjust learning algorithm to handle maximum over finite set §For rule lists and decision sets, … nyc trips from myrtle beachWebWe propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax objective that … nyct winthrop stationWebJul 13, 2024 · We propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax objective that aims to construct the highest fidelity explanation with respect to the worst-case over a set of adversarial perturbations. nyc tunnels and bridgesWebNov 11, 2024 · We propose a novel framework for generating robust and stable explanations of black box models based on adversarial training. Our framework optimizes a minimax … nyc twitter 911