Umang Bhatt

Orcid: 0000-0002-4611-1668

According to our database1, Umang Bhatt authored at least 45 papers between 2017 and 2024.

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Bibliography

2024
When Should Algorithms Resign?
CoRR, 2024

Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction.
CoRR, 2024

2023
Perspectives on incorporating expert feedback into model updates.
Patterns, July, 2023

Selective Concept Models: Permitting Stakeholder Customisation at Test-Time.
CoRR, 2023

Evaluating Language Models for Mathematics through Interactions.
CoRR, 2023

Learning Personalized Decision Support Policies.
CoRR, 2023

On the informativeness of supervision signals.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Human-in-the-Loop Mixup.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

GeValDi: Generative Validation of Discriminative Models.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023


FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines.
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Iterative Teaching by Data Hallucination.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Human Uncertainty in Concept-Based AI Systems.
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

Towards Robust Metrics for Concept Representation Evaluation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Approximating Full Conformal Prediction at Scale via Influence Functions.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
How transparency modulates trust in artificial intelligence.
Patterns, 2022

Web-based Elicitation of Human Perception on mixup Data.
CoRR, 2022

Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users.
CoRR, 2022

On the Utility of Prediction Sets in Human-AI Teams.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Eliciting and Learning with Soft Labels from Every Annotator.
Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

On the Fairness of Causal Algorithmic Recourse.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement.
CoRR, 2021

Do Concept Bottleneck Models Learn as Intended?
CoRR, 2021

δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates.
CoRR, 2021

A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms.
CoRR, 2021

Getting a CLUE: A Method for Explaining Uncertainty Estimates.
Proceedings of the 9th International Conference on Learning Representations, 2021

Fast conformal classification using influence functions.
Proceedings of the Conformal and Probabilistic Prediction and Applications, 2021

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

FIMAP: Feature Importance by Minimal Adversarial Perturbation.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.
CoRR, 2020

Machine Learning Explainability for External Stakeholders.
CoRR, 2020

Evaluating and Aggregating Feature-based Model Explanations.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

On Network Science and Mutual Information for Explaining Deep Neural Networks.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Explainable machine learning in deployment.
Proceedings of the FAT* '20: Conference on Fairness, 2020

You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods.
Proceedings of the Workshop on Artificial Intelligence Safety, 2020

2019
Towards Aggregating Weighted Feature Attributions.
CoRR, 2019

NIF: A Framework for Quantifying Neural Information Flow in Deep Networks.
CoRR, 2019

A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game.
Proceedings of the 28th IEEE International Conference on Robot and Human Interactive Communication, 2019

Diagnostic Model Explanations: A Medical Narrative.
Proceedings of the Symposium Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness co-located with Association for the Advancement of Artificial Intelligence 2019 Spring Symposium (AAAI-Spring Symposium 2019), 2019

Building Human-Machine Trust via Interpretability.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting.
CoRR, 2018

Maintaining the Humanity of Our Models.
Proceedings of the 2018 AAAI Spring Symposia, 2018

2017
Intelligent Pothole Detection and Road Condition Assessment.
CoRR, 2017


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