Pratyush Maini

According to our database1, Pratyush Maini authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Understanding Hallucinations in Diffusion Models through Mode Interpolation.
CoRR, 2024

LLM Dataset Inference: Did you train on my dataset?
CoRR, 2024

Rethinking LLM Memorization through the Lens of Adversarial Compression.
CoRR, 2024

TOFU: A Task of Fictitious Unlearning for LLMs.
CoRR, 2024

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling Laws for Data Filtering - Data Curation Cannot be Compute Agnostic.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023
Can Neural Network Memorization Be Localized?
Proceedings of the International Conference on Machine Learning, 2023

Model-tuning Via Prompts Makes NLP Models Adversarially Robust.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022
Perturbation type categorization for multiple adversarial perturbation robustness.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Characterizing Datapoints via Second-Split Forgetting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Dataset Inference: Ownership Resolution in Machine Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

Data-Free Model Extraction.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Adversarial Robustness Against the Union of Multiple Perturbation Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

Why and when should you pool? Analyzing Pooling in Recurrent Architectures.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020


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