Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning. - Divyansh Jhunjhunwala; Advait Gadhikar; Gauri Joshi; Yonina C. Eldar (ICASSP, 2021)

Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation. - Divyansh Jhunjhunwala; Ankur Mallick; Advait Gadhikar; Swanand Kadhe; Gauri Joshi (NeurIPS, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Dynamical Isometry for Residual Networks. - Advait Gadhikar; Rebekka Burkholz (CoRR, 2022)

How Erdös and Rényi Win the Lottery. - Advait Gadhikar; Sohom Mukherjee; Rebekka Burkholz (CoRR, 2022)

Why Random Pruning Is All We Need to Start Sparse. - Advait Harshal Gadhikar; Sohom Mukherjee; Rebekka Burkholz (ICML, 2023)

Masks, Signs, And Learning Rate Rewinding. - Advait Harshal Gadhikar; Rebekka Burkholz (ICLR, 2024)

Cyclic Sparse Training: Is it Enough? - Advait Gadhikar; Sree Harsha Nelaturu; Rebekka Burkholz (CoRR, 2024)

Attention Is All You Need For Mixture-of-Depths Routing. - Advait Gadhikar; Souptik Kumar Majumdar; Niclas Popp; Piyapat Saranrittichai; Martin Rapp; Lukas Schott (CoRR, 2024)

Sign-In to the Lottery: Reparameterizing Sparse Training From Scratch. - Advait Gadhikar; Tom Jacobs; Chao Zhou; Rebekka Burkholz (CoRR, 2025)

HAM: A Hyperbolic Step to Regulate Implicit Bias. - Tom Jacobs; Advait Gadhikar; Celia Rubio-Madrigal; Rebekka Burkholz (CoRR, 2025)

Pay Attention to Small Weights. - Chao Zhou; Tom Jacobs; Advait Gadhikar; Rebekka Burkholz (CoRR, 2025)