Sparse Kernel PCA for Outlier Detection. - Rudrajit Das; Aditya Golatkar; Suyash P. Awate (ICMLA, 2018)

Nonlinear Blind Compressed Sensing Under Signal-Dependent Noise. - Rudrajit Das; Ajit Rajwade 001 (ICIP, 2019)

On the Separability of Classes with the Cross-Entropy Loss Function. - Rudrajit Das; Subhasis Chaudhuri (CoRR, 2019)

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization. - Abolfazl Hashemi; Anish Acharya; Rudrajit Das; Haris Vikalo; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2020)

Improved Convergence Rates for Non-Convex Federated Learning with Compression. - Rudrajit Das; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2020)

DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning. - Rudrajit Das; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2021)

DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation. - Anish Acharya; Rudrajit Das (CoRR, 2021)

Faster non-convex federated learning via global and local momentum. - Rudrajit Das; Anish Acharya; Abolfazl Hashemi; Sujay Sanghavi; Inderjit S. Dhillon; Ufuk Topcu (UAI, 2022)

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. - Abolfazl Hashemi; Anish Acharya; Rudrajit Das; Haris Vikalo; Sujay Sanghavi; Inderjit S. Dhillon (IEEE Trans. Parallel Distributed Syst., 2022)

Beyond Uniform Lipschitz Condition in Differentially Private Optimization. - Rudrajit Das; Satyen Kale; Zheng Xu 002; Tong Zhang 001; Sujay Sanghavi (ICML, 2023)

Understanding Self-Distillation in the Presence of Label Noise. - Rudrajit Das; Sujay Sanghavi (ICML, 2023)

Understanding the Training Speedup from Sampling with Approximate Losses. - Rudrajit Das; Xi Chen; Bertram Ieong; Parikshit Bansal; Sujay Sanghavi (ICML, 2024)

Towards Quantifying the Preconditioning Effect of Adam. - Rudrajit Das; Naman Agarwal; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2024)

Retraining with Predicted Hard Labels Provably Increases Model Accuracy. - Rudrajit Das; Inderjit S. Dhillon; Alessandro Epasto; Adel Javanmard; Jieming Mao; Vahab Mirrokni; Sujay Sanghavi; Peilin Zhong (CoRR, 2024)

On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. - Jianyu Wang; Rudrajit Das; Gauri Joshi; Satyen Kale; Zheng Xu 002; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting. - Sunny Sanyal; Hayden Prairie; Rudrajit Das; Ali Kavis; Sujay Sanghavi (CoRR, 2025)

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing. - Adel Javanmard; Rudrajit Das; Alessandro Epasto; Vahab Mirrokni (CoRR, 2025)