b'Been Kim'

research

∙ 05/29/2023

Gaussian Process Probes (GPP) for Uncertainty-Aware Probing

Understanding which concepts models can and cannot represent has been fu...

0 Zi Wang, et al. ∙

research

∙ 05/24/2023

Model evaluation for extreme risks

Current approaches to building general-purpose AI systems tend to produc...

0 Toby Shevlane, et al. ∙

research

∙ 01/10/2023

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models

Language models are known to learn a great quantity of factual informati...

2 Peter Hase, et al. ∙

research

∙ 12/22/2022

Impossibility Theorems for Feature Attribution

Despite a sea of interpretability methods that can produce plausible exp...

3 Blair Bilodeau, et al. ∙

research

∙ 12/13/2022

On the Relationship Between Explanation and Prediction: A Causal View

Explainability has become a central requirement for the development, dep...

0 Amir-Hossein Karimi, et al. ∙

research

∙ 12/09/2022

Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation

We investigate whether three types of post hoc model explanations–featur...

0 Julius Adebayo, et al. ∙

research

∙ 06/17/2022

Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis

Each year, expert-level performance is attained in increasingly-complex ...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 02/25/2022

Human-Centered Concept Explanations for Neural Networks

Understanding complex machine learning models such as deep neural networ...

0 Chih-Kuan Yeh, et al. ∙

research

∙ 01/11/2022

Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

Intelligent decision support (IDS) systems leverage artificial intellige...

0 Devleena Das, et al. ∙

research

∙ 11/17/2021

Acquisition of Chess Knowledge in AlphaZero

What is learned by sophisticated neural network agents such as AlphaZero...

80 Thomas McGrath, et al. ∙

research

∙ 06/16/2021

Best of both worlds: local and global explanations with human-understandable concepts

Interpretability techniques aim to provide the rationale behind a model'...

8 Jessica Schrouff, et al. ∙

research

∙ 05/31/2021

DISSECT: Disentangled Simultaneous Explanations via Concept Traversals

Explaining deep learning model inferences is a promising venue for scien...

7 Asma Ghandeharioun, et al. ∙

research

∙ 11/10/2020

Debugging Tests for Model Explanations

We investigate whether post-hoc model explanations are effective for dia...

10 Julius Adebayo, et al. ∙

research

∙ 07/09/2020

Concept Bottleneck Models

We seek to learn models that we can interact with using high-level conce...

14 Pang Wei Koh, et al. ∙

research

∙ 10/17/2019

On Concept-Based Explanations in Deep Neural Networks

Deep neural networks (DNNs) build high-level intelligence on low-level r...

21 Chih-Kuan Yeh, et al. ∙

research

∙ 07/23/2019

BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth

Interpretability is rising as an important area of research in machine l...

4 Mengjiao Yang, et al. ∙

research

∙ 07/22/2019

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Machine learning based decision making systems are increasingly affectin...

3 Shalmali Joshi, et al. ∙

research

∙ 07/16/2019

Explaining Classifiers with Causal Concept Effect (CaCE)

How can we understand classification decisions made by deep neural nets?...

0 Yash Goyal, et al. ∙

research

∙ 06/06/2019

Visualizing and Measuring the Geometry of BERT

Transformer architectures show significant promise for natural language ...

13 Andy Coenen, et al. ∙

research

∙ 03/04/2019

Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure

One characteristic of human visual perception is the presence of "Gestal...

8 Been Kim, et al. ∙

research

∙ 02/08/2019

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval syst...

0 Carrie J. Cai, et al. ∙

research

∙ 02/07/2019

Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks

Interpretability has become an important topic of research as more machi...

40 Amirata Ghorbani, et al. ∙

research

∙ 01/31/2019

An Evaluation of the Human-Interpretability of Explanation

Recent years have seen a boom in interest in machine learning systems th...

16 Isaac Lage, et al. ∙

research

∙ 10/23/2018

Interpreting Black Box Predictions using Fisher Kernels

Research in both machine learning and psychology suggests that salient e...

5 Rajiv Khanna, et al. ∙

research

∙ 10/08/2018

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values

Explaining the output of a complicated machine learning model like a dee...

2 Julius Adebayo, et al. ∙

research

∙ 10/08/2018

Sanity Checks for Saliency Maps

Saliency methods have emerged as a popular tool to highlight features in...

2 Julius Adebayo, et al. ∙

research

∙ 07/03/2018

Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)

This is the Proceedings of the 2018 ICML Workshop on Human Interpretabil...

2 Been Kim, et al. ∙

research

∙ 06/28/2018

Evaluating Feature Importance Estimates

Estimating the influence of a given feature to a model prediction is cha...

4 Sara Hooker, et al. ∙

research

∙ 06/22/2018

xGEMs: Generating Examplars to Explain Black-Box Models

This work proposes xGEMs or manifold guided exemplars, a framework to un...

0 Shalmali Joshi, et al. ∙

research

∙ 05/30/2018

To Trust Or Not To Trust A Classifier

Knowing when a classifier's prediction can be trusted is useful in many ...

8 Heinrich Jiang, et al. ∙

research

∙ 05/29/2018

Human-in-the-Loop Interpretability Prior

We often desire our models to be interpretable as well as accurate. Prio...

0 Isaac Lage, et al. ∙

research

∙ 02/02/2018

How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

Recent years have seen a boom in interest in machine learning systems th...

0 Menaka Narayanan, et al. ∙

research

∙ 11/30/2017

TCAV: Relative concept importance testing with Linear Concept Activation Vectors

Neural networks commonly offer high utility but remain difficult to inte...

0 Been Kim, et al. ∙

research

∙ 11/02/2017

The (Un)reliability of saliency methods

Saliency methods aim to explain the predictions of deep neural networks....

0 Pieter-Jan Kindermans, et al. ∙

research

∙ 08/08/2017

Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)

This is the Proceedings of the 2017 ICML Workshop on Human Interpretabil...

0 Been Kim, et al. ∙

research

∙ 06/12/2017

SmoothGrad: removing noise by adding noise

Explaining the output of a deep network remains a challenge. In the case...

0 Daniel Smilkov, et al. ∙

research

∙ 05/16/2017

Learning how to explain neural networks: PatternNet and PatternAttribution

DeConvNet, Guided BackProp, LRP, were invented to better understand deep...

0 Pieter-Jan Kindermans, et al. ∙

research

∙ 02/28/2017

Towards A Rigorous Science of Interpretable Machine Learning

As machine learning systems become ubiquitous, there has been a surge of...

0 Finale Doshi-Velez, et al. ∙

research

∙ 07/08/2016

Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)

This is the Proceedings of the 2016 ICML Workshop on Human Interpretabil...

0 Been Kim, et al. ∙

research

∙ 03/03/2015

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

We present the Bayesian Case Model (BCM), a general framework for Bayesi...

0 Been Kim, et al. ∙

research

∙ 06/05/2013

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous syst...

0 Been Kim, et al. ∙

Been Kim

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