Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are not available. Practitioners often have a fixed budget for the number of function evaluations and the performance of an optimization algorithm is measured by its simple regret. In this paper, we study the class of "Optimistic Optimization" algorithms for black-box optimization that use a partitioning scheme for the domain. We develop algorithms that learn a good partitioning scheme and use flexible surrogate models such as neural networks in the optimization procedure. For multi-index functions on an m-dimensional subspace within d dimensions, our algorithm attains \tildeO(n^-β/ d) regret, where β= 1 + \fracd-m2m-1, as opposed to \tildeO(n^-1/d) for SequOOL, a state-of-the-art optimistic optimization algorithm. Our approach is competitive across a wide range of numerical benchmarks. Additionally, we introduce weight quantization in a large language model as a novel task for black-box optimization.
@inproceedings{sunkara2025adaptive,title={Adaptive Partitioning Schemes for Optimistic Optimization},author={Sunkara, Raja and Tripathy, Ardhendu},booktitle={Proceedings of the 42nd International Conference on Machine Learning},series={Proceedings of Machine Learning Research},volume={267},pages={58029--58064},year={2025},publisher={PMLR},}
2023
YOGA: Deep object detection in the wild with lightweight feature learning and multiscale attention
Raja Sunkara, and Tie Luo
Pattern Recognition, 2023
Computer Vision in Adverse Conditions: Small Objects, Low-Resolution Images, and Edge Deployment
Raja Sunkara
2023
2022
ECML-PKDD
No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects
Raja Sunkara, and Tie Luo
In Joint European conference on machine learning and knowledge discovery in databases, 2022
@inproceedings{sunkara2022no,title={No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects},author={Sunkara, Raja and Luo, Tie},booktitle={Joint European conference on machine learning and knowledge discovery in databases},pages={443--459},year={2022},organization={Springer},}
Extraction of key features and enhanced prediction framework of breast cancer occurrence
Praveen Sahu, Pragatheiswar Giri, Raja Sunkara, and 1 more author
In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 2022
