Publications

Preprints and in submission

• Lou, M., Verchand, K.A., and Pananjady, A. (2024), Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing ( preprint)

• Chandrasekher, K.A., Lou, M., and Pananjady, A. (2022), Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization (preprint). Extended abstract at Algorithmic Learning Theory (ALT) 2024.

• Chandrasekher, K.A., El Alaoui, A., and Montanari, A. (2020), Imputation for High-Dimensional Linear Regression (preprint).

• Mardia, J., Asi, H., Chandrasekher, K.A., Finding Planted Cliques in Sublinear Time (preprint).

Published

• Mardia, J., Verchand, K.A., and Wein, Alexander S. (2024), Low-degree phase transitions for detecting a planted clique in sublinear time, Conference on Learning Theory (COLT).

• Chandrasekher, K.A., Pananjady, A., and Thrampoulidis, C. (2023), Sharp global convergence guarantees for iterative nonconvex optimization: A Gaussian process perspective, Annals of Statistics.
  • Runner-up: Best paper prize for young researchers in continuous optimization (ICCOPT 2022)

• Lee, K., Chandrasekher, K.A., Pedarsani, R., and Ramchandran, K. (2019), SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes, IEEE Transactions on Signal Processing.

• Cheng, G., Chandrasekher, K.A., and Walrand, J. (2019), Static & Dynamic Appointment Scheduling with Stochastic Gradient Descent, American Control Conference (ACC).

• Lazar, D., Chandrasekher, K.A., Pedarsani, R., and Sadigh, D. (2018), Maximizing Road Capacity Using Cars that Influence People, Conference on Decision and Control (CDC).

• Chandrasekher, K.A., Lee, K., Kairouz, P., Pedarsani, R., and Ramchandran, K. (2017), Asynchronous and Noncoherent Neighbor Discovery for the IoT Using Sparse-Graph Codes, International Conference on Communications (ICC).

• Chandrasekher, K.A., Ocal, O., and Ramchandran, K. (2017), Density Evolution on a Class of Smeared Random Graphs: A Theoretical Framework for Fast MRI, International Symposium on Information Theory (ISIT).