Publications
Preprints and in submission
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Ma, T., Verchand, K.A., Berrett, T.B., Wang, T., and Samworth, R.J. (2024), Estimation beyond Missing (Completely) at Random, (preprint)
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Verchand, K.A. and Montanari, A. (2024), High-dimensional logistic regression with missing data: Imputation, regularization, and universality (preprint)
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Ma, T., Verchand, K.A., and Samworth, R.J. (2024), High-probability minimax lower bounds (preprint)
- Lou, M., Verchand, K.A., and Pananjady, A. (2024), Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing ( preprint)
- Preliminary version at Workshop on High-dimensional Learning Dynamics, ICML 2023 (Oral)
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Chandrasekher, K.A., El Alaoui, A., and Montanari, A. (2020), Imputation for High-Dimensional Linear Regression (preprint).
- Mardia, J., Asi, H., Chandrasekher, K.A. (2020), Finding Planted Cliques in Sublinear Time (preprint).
Published
- Chandrasekher, K.A., Lou, M., and Panajady, A. (2024), Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization, Information and Inference: A Journal of the IMA. (Extended abstract at Algorithmic Learning Theory (ALT))
- Preliminary version at Workshop on The Benefits of Higher-Order Optimization in Machine Learning, Neurips 2022 (Oral).
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Mardia, J., Verchand, K.A., and Wein, A.S. (2024), Low-degree phase transitions for detecting a planted clique in sublinear time, Conference on Learning Theory (COLT).
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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, Mathematical Optimization Society
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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.
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Cheng, G., Chandrasekher, K.A., and Walrand, J. (2019), Static & Dynamic Appointment Scheduling with Stochastic Gradient Descent, American Control Conference (ACC).
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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).
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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).