| Fine-tuning language models with just forward passes S Malladi*, T Gao*, E Nichani, A Damian, JD Lee, D Chen, S Arora Advances in Neural Information Processing Systems 36, 53038-53075, 2023 | 201 | 2023 |
| LESS: Selecting influential data for targeted instruction tuning M Xia*, S Malladi*, S Gururangan, S Arora, D Chen International Conference on Machine Learning, 2024 | 157 | 2024 |
| On the validity of modeling SGD with stochastic differential equations (SDEs) Z Li, S Malladi, S Arora Advances in Neural Information Processing Systems 34, 12712-12725, 2021 | 99 | 2021 |
| A mathematical exploration of why language models help solve downstream tasks N Saunshi, S Malladi, S Arora International Conference on Learning Representations, 2021 | 89 | 2021 |
| A kernel-based view of language model fine-tuning S Malladi, A Wettig, D Yu, D Chen, S Arora International Conference on Machine Learning, 23610-23641, 2023 | 80 | 2023 |
| EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes S Nabavi, D Schmolze, M Maitituoheti, S Malladi, AH Beck Bioinformatics 32 (4), 533-541, 2016 | 77 | 2016 |
| On the SDEs and scaling rules for adaptive gradient algorithms S Malladi*, K Lyu*, A Panigrahi, S Arora Advances in Neural Information Processing Systems 35, 7697-7711, 2022 | 47 | 2022 |
| MUSE: Machine unlearning six-way evaluation for language models W Shi, J Lee, Y Huang, S Malladi, J Zhao, A Holtzman, D Liu, ... GenLaw Workshop at International Conference on Machine Learning, 2024 | 35 | 2024 |
| Systematic analysis of sex-linked molecular alterations and therapies in cancer J Ma*, S Malladi*, AH Beck Scientific reports 6 (1), 19119, 2016 | 27 | 2016 |
| Charxiv: Charting gaps in realistic chart understanding in multimodal llms Z Wang, M Xia, L He, H Chen, Y Liu, R Zhu, K Liang, X Wu, H Liu, ... Advances in Neural Information Processing Systems, 2024 | 25 | 2024 |
| Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging I Banerjee, S Malladi, D Lee, A Depeursinge, M Telli, J Lipson, D Golden, ... Journal of medical imaging 5 (1), 011008-011008, 2018 | 22 | 2018 |
| Trainable transformer in transformer A Panigrahi*, S Malladi*, M Xia, S Arora International Conference on Machine Learning, 2024 | 20 | 2024 |
| Preference Learning Algorithms Do Not Learn Preference Rankings A Chen, S Malladi, LH Zhang, X Chen, Q Zhang, R Ranganath, K Cho Advances in Neural Information Processing Systems, 2024 | 16 | 2024 |
| FastNorm: improving numerical stability of deep network training with efficient normalization S Malladi, I Sharapov Women in Machine Learning Workshop at International Conference on Machine …, 2018 | 11 | 2018 |
| The marginal value of momentum for small learning rate sgd R Wang, S Malladi, T Wang, K Lyu, Z Li International Conference on Learning Representations, 2024 | 10 | 2024 |
| Adaptive data optimization: Dynamic sample selection with scaling laws Y Jiang, A Zhou, Z Feng, S Malladi, JZ Kolter arXiv preprint arXiv:2410.11820, 2024 | 5 | 2024 |
| Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization N Razin, S Malladi, A Bhaskar, D Chen, S Arora, B Hanin Fine-Tuning in Modern Machine Learning Workshop at NeurIPS 2024, 2024 | 3 | 2024 |
| Metadata Conditioning Accelerates Language Model Pre-training T Gao, A Wettig, L He, Y Dong, S Malladi, D Chen arXiv preprint arXiv:2501.01956, 2025 | 2 | 2025 |
| Provable unlearning in topic modeling and downstream tasks S Wei, S Malladi, S Arora, A Sanyal arXiv preprint arXiv:2411.12600, 2024 | | 2024 |
| Predicting Treatment Response in Triple Negative Breast Cancer Through Quantitative Image Analysis in Perfusion MRI S Malladi, D Lee, A Depeursinge, DL Rubin 6th Annual Symposium of the Center for Biomedical Imaging at Stanford, 2014 | | 2014 |
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerifierad e-postadress på princeton.edu