Vedant Nanda

Orcid: 0009-0002-7158-5627

According to our database1, Vedant Nanda authored at least 26 papers between 2017 and 2025.

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Bibliography

2025
Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity.
Trans. Mach. Learn. Res., 2025

Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction.
Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

The Impact of Inference Acceleration on Bias of LLMs.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Lawma: The Power of Specialization for Legal Annotation.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Foundations of Trustworthy Deep Learning: Fairness, robustness, and Explainability.
PhD thesis, 2024

Understanding the Role of Invariance in Transfer Learning.
Trans. Mach. Learn. Res., 2024

The Impact of Inference Acceleration Strategies on Bias of LLMs.
CoRR, 2024

Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications.
CoRR, 2024

Lawma: The Power of Specialization for Legal Tasks.
CoRR, 2024

Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction.
CoRR, 2024

2023
Pointwise Representational Similarity.
CoRR, 2023

Diffused Redundancy in Pre-trained Representations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Do Invariances in Deep Neural Networks Align with Human Perception?
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Measuring Representational Robustness of Neural Networks Through Shared Invariances.
Proceedings of the International Conference on Machine Learning, 2022

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
Exploring Alignment of Representations with Human Perception.
CoRR, 2021

Technical Challenges for Training Fair Neural Networks.
CoRR, 2021

Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

2020
Unifying Model Explainability and Robustness via Machine-Checkable Concepts.
CoRR, 2020

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Stop the KillFies! Using Deep Learning Models to Identify Dangerous Selfies.
Proceedings of the Companion of the The Web Conference 2018 on The Web Conference 2018, 2018

2017
An Empirical Analysis of Facebook's Free Basics.
Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Urbana-Champaign, IL, USA, June 05, 2017

Leveraging Facebook's Free Basics Engine for Web Service Deployment in Developing Regions.
Proceedings of the Ninth International Conference on Information and Communication Technologies and Development, 2017


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