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RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty · Xujiang Zhao · Feng Chen · Rishabh Iyer

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ Virtual
Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes. However, the current state-of-the-art SSL algorithms are computationally expensive and entail significant compute time and energy requirements. This can prove to be a huge limitation for many smaller companies and academic groups. Our main insight is that training on a subset of unlabeled data instead of entire unlabeled data enables the current SSL algorithms to converge faster, significantly reducing computational costs. In this work, we propose RETRIEVE, a coreset selection framework for efficient and robust semi-supervised learning. RETRIEVE selects the coreset by solving a mixed discrete-continuous bi-level optimization problem such that the selected coreset minimizes the labeled set loss. We use a one-step gradient approximation and show that the discrete optimization problem is approximately submodular, enabling simple greedy algorithms to obtain the coreset. We empirically demonstrate on several real-world datasets that existing SSL algorithms like VAT, Mean-Teacher, FixMatch, when used with RETRIEVE, achieve a) faster training times, b) better performance when unlabeled data consists of Out-of-Distribution (OOD) data and imbalance. More specifically, we show that with minimal accuracy degradation, RETRIEVE achieves a speedup of around $3\times$ in the traditional SSL setting and achieves a speedup of $5\times$ compared to state-of-the-art (SOTA) robust SSL algorithms in the case of imbalance and OOD data. RETRIEVE is available as a part of the CORDS toolkit: https://github.com/decile-team/cords.

Author Information

Krishnateja Killamsetty (University of Texas, Dallas)
Xujiang Zhao (The University of Texas at Dallas)
Feng Chen (UT Dallas)
Rishabh Iyer (University of Texas, Dallas)

Bio: Prof. Rishabh Iyer is currently an Assistant Professor at the University of Texas, Dallas, where he leads the CARAML Lab. He is also a Visiting Assistant Professor at the Indian Institute of Technology, Bombay. He completed his Ph.D. in 2015 from the University of Washington, Seattle. He is excited in making ML more efficient (both computational and labeling efficiency), robust, and fair. He has received the best paper award at Neural Information Processing Systems (NeurIPS/NIPS) in 2013, the International Conference of Machine Learning (ICML) in 2013, and an Honorable Mention at CODS-COMAD in 2021. He has also won a Microsoft Research Ph.D. Fellowship, a Facebook Ph.D. Fellowship, and the Yang Award for Outstanding Graduate Student from the University of Washington.

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