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Batch Active Learning at Scale
Gui Citovsky · Giulia DeSalvo · Claudio Gentile · Lazaros Karydas · Anand Rajagopalan · Afshin Rostamizadeh · Sanjiv Kumar

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources. Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addressing this problem. The practical benefits of batch sampling come with the downside of less adaptivity and the risk of sampling redundant examples within a batch -- a risk that grows with the batch size. In this work, we analyze an efficient active learning algorithm, which focuses on the large batch setting. In particular, we show that our sampling method, which combines notions of uncertainty and diversity, easily scales to batch sizes (100K-1M) several orders of magnitude larger than used in previous studies and provides significant improvements in model training efficiency compared to recent baselines. Finally, we provide an initial theoretical analysis, proving label complexity guarantees for a related sampling method, which we show is approximately equivalent to our sampling method in specific settings.

Author Information

Gui Citovsky (Google)
Giulia DeSalvo (Google Research)
Claudio Gentile (Google Research)
Lazaros Karydas (Google)
Anand Rajagopalan (Google)
Afshin Rostamizadeh (Google Research)
Sanjiv Kumar (Google Research)

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