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Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Rajat Sen · kirthevasan kandasamy · Sanjay Shakkottai

Fri Jul 13 01:00 AM -- 01:10 AM (PDT) @ A5

Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function. Multi-fidelity techniques have become popular for applications where exact function evaluations are expensive, but coarse (biased) approximations are available at much lower cost. A canonical example is that of hyper-parameter selection in a learning algorithm. The learning algorithm can be trained for fewer iterations -- this results in a lower cost, but its validation error is only coarsely indicative of the same if the algorithm had been trained till completion. We incorporate the multi-fidelity setup into the powerful framework of black-box optimization through hierarchical partitioning. We develop tree-search based multi-fidelity algorithms with theoretical guarantees on simple regret. We finally demonstrate the performance gains of our algorithms on both real and synthetic datasets.

Author Information

Rajat Sen (University of Texas at Austin)

I am a 4th year PhD. student in WNCG, UT Austin. I am advised by [Dr. Sanjay Shakkottai](http://users.ece.utexas.edu/~shakkott/Sanjay_Shakkottai/Contact.html). I received my Bachelors degree in ECE, IIT Kharagpur in 2013. I have spent most of my childhood in Durgapur and Kolkata, West Bengal, India. My research interests include online learning (especially Multi-Armed Bandit problems), causality, learning in queueing systems, recommendation systems and social networks. I like to work on real-world problems that allow rigorous theoretical analysis.

kirthevasan kandasamy (CMU)
Sanjay Shakkottai (University of Texas at Austin)

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