no code implementations • 8 Mar 2024 • Sanket Shah, Arun Suggala, Milind Tambe, Aparna Taneja
However, the availability and time of these health workers are limited resources.
no code implementations • 14 Feb 2024 • Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan
We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that we call $\kappa$-convex.
no code implementations • NeurIPS 2020 • Arun Suggala, Bingbin Liu, Pradeep Ravikumar
Using thorough empirical evaluation, we show that our learning algorithms have superior performance over traditional additive boosting algorithms, as well as existing greedy learning techniques for DNNs.
1 code implementation • NeurIPS 2019 • Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I. Inouye, Pradeep K. Ravikumar
We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods.
no code implementations • NeurIPS 2018 • Arun Suggala, Adarsh Prasad, Pradeep K. Ravikumar
We study the implicit regularization properties of optimization techniques by explicitly connecting their optimization paths to the regularization paths of ``corresponding'' regularized problems.
no code implementations • NeurIPS 2017 • Arun Suggala, Mladen Kolar, Pradeep K. Ravikumar
Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions.