Himabindu Lakkaraju

research

∙ 08/08/2023

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

Machine learning models are increasingly utilized across impactful domai...

0 Catherine Huang, et al. ∙

research

∙ 07/27/2023

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability

With the increased deployment of machine learning models in various real...

0 Usha Bhalla, et al. ∙

research

∙ 07/26/2023

Efficient Estimation of the Local Robustness of Machine Learning Models

Machine learning models often need to be robust to noisy input data. The...

0 Tessa Han, et al. ∙

research

∙ 07/25/2023

Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions

Chain-of-thought (CoT) prompting has been shown to empirically improve t...

0 Skyler Wu, et al. ∙

research

∙ 06/11/2023

On Minimizing the Impact of Dataset Shifts on Actionable Explanations

The Right to Explanation is an important regulatory principle that allow...

0 Anna P. Meyer, et al. ∙

research

∙ 06/09/2023

Consistent Explanations in the Face of Model Indeterminacy via Ensembling

This work addresses the challenge of providing consistent explanations f...

0 Dan Ley, et al. ∙

research

∙ 02/08/2023

Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten

The Right to Explanation and the Right to be Forgotten are two important...

0 Satyapriya Krishna, et al. ∙

research

∙ 11/10/2022

On the Privacy Risks of Algorithmic Recourse

As predictive models are increasingly being employed to make consequenti...

0 Martin Pawelczyk, et al. ∙

research

∙ 09/18/2022

Towards Robust Off-Policy Evaluation via Human Inputs

Off-policy Evaluation (OPE) methods are crucial tools for evaluating pol...

0 Harvineet Singh, et al. ∙

research

∙ 08/19/2022

Evaluating Explainability for Graph Neural Networks

As post hoc explanations are increasingly used to understand the behavio...

9 Chirag Agarwal, et al. ∙

research

∙ 07/08/2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues

Machine Learning (ML) models are increasingly used to make critical deci...

0 Dylan Slack, et al. ∙

research

∙ 06/22/2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations

While several types of post hoc explanation methods (e.g., feature attri...

32 Chirag Agarwal, et al. ∙

research

∙ 06/14/2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks

The highly non-linear nature of deep neural networks causes them to be s...

0 Suraj Srinivas, et al. ∙

research

∙ 06/06/2022

A Human-Centric Take on Model Monitoring

Predictive models are increasingly used to make various consequential de...

0 Murtuza N. Shergadwala, et al. ∙

research

∙ 06/02/2022

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

Despite the plethora of post hoc model explanation methods, the basic pr...

0 Tessa Han, et al. ∙

research

∙ 05/15/2022

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

As post hoc explanation methods are increasingly being leveraged to expl...

0 Jessica Dai, et al. ∙

research

∙ 03/14/2022

Rethinking Stability for Attribution-based Explanations

As attribution-based explanation methods are increasingly used to establ...

11 Chirag Agarwal, et al. ∙

research

∙ 03/13/2022

Algorithmic Recourse in the Face of Noisy Human Responses

As machine learning (ML) models are increasingly being deployed in high-...

13 Martin Pawelczyk, et al. ∙

research

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

5 Himabindu Lakkaraju, et al. ∙

research

∙ 02/03/2022

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

As various post hoc explanation methods are increasingly being leveraged...

38 Satyapriya Krishna, et al. ∙

research

∙ 06/24/2021

What will it take to generate fairness-preserving explanations?

In situations where explanations of black-box models may be useful, the ...

0 Jessica Dai, et al. ∙

research

∙ 06/23/2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated

As machine learning models are increasingly used in critical decision-ma...

0 Dylan Slack, et al. ∙

research

∙ 06/18/2021

On the Connections between Counterfactual Explanations and Adversarial Examples

Counterfactual explanations and adversarial examples have emerged as cri...

16 Martin Pawelczyk, et al. ∙

research

∙ 06/16/2021

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations

As Graph Neural Networks (GNNs) are increasingly employed in real-world ...

6 Chirag Agarwal, et al. ∙

research

∙ 06/04/2021

Counterfactual Explanations Can Be Manipulated

Counterfactual explanations are emerging as an attractive option for pro...

1 Dylan Slack, et al. ∙

research

∙ 03/29/2021

Learning Under Adversarial and Interventional Shifts

Machine learning models are often trained on data from one distribution ...

0 Harvineet Singh, et al. ∙

research

∙ 02/26/2021

Towards Robust and Reliable Algorithmic Recourse

As predictive models are increasingly being deployed in high-stakes deci...

0 Sohini Upadhyay, et al. ∙

research

∙ 02/25/2021

Towards a Unified Framework for Fair and Stable Graph Representation Learning

As the representations output by Graph Neural Networks (GNNs) are increa...

16 Chirag Agarwal, et al. ∙

research

∙ 02/21/2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

As machine learning black boxes are increasingly being deployed in criti...

10 Sushant Agarwal, et al. ∙

research

∙ 12/22/2020

Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses

As predictive models are being increasingly deployed to make a variety o...

0 Kaivalya Rawal, et al. ∙

research

∙ 12/01/2020

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Ranking algorithms are being widely employed in various online hiring pl...

0 Tom Sühr, et al. ∙

research

∙ 11/12/2020

Robust and Stable Black Box Explanations

As machine learning black boxes are increasingly being deployed in real-...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 11/12/2020

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

As machine learning (ML) models are increasingly being employed to assis...

0 Sean McGrath, et al. ∙

research

∙ 11/12/2020

Ensuring Actionable Recourse via Adversarial Training

As machine learning models are increasingly deployed in high-stakes doma...

0 Alexis Ross, et al. ∙

research

∙ 10/21/2020

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Domains where supervised models are deployed often come with task-specif...

0 Wanqian Yang, et al. ∙

research

∙ 09/15/2020

Interpretable and Interactive Summaries of Actionable Recourses

As predictive models are increasingly being deployed in high-stakes deci...

18 Kaivalya Rawal, et al. ∙

research

∙ 08/11/2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations

As local explanations of black box models are increasingly being employe...

24 Dylan Slack, et al. ∙

research

∙ 06/14/2020

Fair Influence Maximization: A Welfare Optimization Approach

Several social interventions (e.g., suicide and HIV prevention) leverage...

10 Aida Rahmattalabi, et al. ∙

research

∙ 11/15/2019

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations

As machine learning black boxes are increasingly being deployed in criti...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 11/06/2019

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods

As machine learning black boxes are increasingly being deployed in domai...

20 Dylan Slack, et al. ∙

research

∙ 07/04/2017

Interpretable & Explorable Approximations of Black Box Models

We propose Black Box Explanations through Transparent Approximations (BE...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 10/28/2016

Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Predictive models deployed in the real world may assign incorrect labels...

0 Himabindu Lakkaraju, et al. ∙

research

∙ 10/21/2016

Learning Cost-Effective Treatment Regimes using Markov Decision Processes

Decision makers, such as doctors and judges, make crucial decisions such...

0 Himabindu Lakkaraju, et al. ∙

Himabindu Lakkaraju

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