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Belief-State Query Policies for User-Aligned POMDPs

Daniel Bramblett, Siddharth Srivastava

Published in Advances in Neural Information Processing Systems, 2024

This paper studies query polices for user-aligned decision making in partially observable Markov decision processes.

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Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks

Rushang Karia, Daniel Bramblett, Daksh Dobhal, Siddharth Srivastava

Published in International Conference on Learning Representations, 2025

This paper evaluates large language models on truth maintenance on translating formal language and reasoning tasks.

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Safety Beyond Verification: The Need for Continual, User-Driven Assessment of AI Systems

Siddharth Srivastava, Georgios Fainekos, Pulkit Verma, Daniel Bramblett

Published in IJCAI Workshop on User-Aligned Assessment of Adaptive AI Systems, 2025

Examines the existing researching on assessing the safety and functionality of taskable AI systems that are designed to continually learn and solving user-desired tasks in user-specific environments.

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Discovering and Learning Probabilistic Models of Black-Box AI Capabilities

Daniel Bramblett, Rushang Karia, Adrian Ciotinga, Ruthvick Suresh, Pulkit Verma, YooJung Choi, Siddharth Srivastava

Manuscript under review. This manuscript studies methods for discovering and learning probabilistic models of black-box AI system capabilities.

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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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