Sinha, Koustuv

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

Pineau, J., Vincent-Lamarre, P., Sinha, K., Larivière, V., Beygelzimer, A., d'Alché-Buc, F., Fox, E., & Larochelle, H. (2020) · arXiv

Scientific Computing and Data ManagementAcademic Publishing and Open Access

Cited by 232

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Sinha, K., Sodhani, S., Dong, J., Pineau, J., & Hamilton, W. L. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 110

Brittlebench: Quantifying LLM robustness via prompt sensitivity

Romanou, A., Ibrahim, M., Ross, C., Oktar, K., Shaib, C., Bell, S. J., Ovalle, A., Bosselut, A., Dodge, J., Sinha, K., et al. (2026) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 24

ML Reproducibility Challenge 2022

Sinha, K., Bleeker, M., Bhargav, S., Forde, J. Z., Raparthy, S. C., Dodge, J., Pineau, J., & Stojnic, R. (2023) · Zenodo

Scientific Computing and Data ManagementMachine Learning and Data Classification

Cited by 10

Sometimes We Want Ungrammatical Translations

Parthasarathi, P., Sinha, K., Pineau, J., & Williams, A. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Natural Language Processing TechniquesTopic Modeling

Cited by 9

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., et al. (2025) · arXiv

Human Pose and Action RecognitionMultimodal Machine Learning Applications

Cited by 8

The ART of LLM Refinement: Ask, Refine, and Trust

Shridhar, K., Sinha, K., Cohen, A., Wang, T., Yu, P., Pasunuru, R., Sachan, M., Weston, J., & Celikyilmaz, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs

Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., & Assran, M. (2025) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 0