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This paper proposes EMA-FS, an algorithm-level optimization that uses exponential moving averages of per-feature split gains to selectively construct histograms only for top-K features during GBDT training, achieving up to 2.61x speedups while maintaining or improving accuracy through implicit regularization.
EMAgnet introduces parameter-space exponential moving average regularization for policy gradient self-play in large two-player zero-sum games, achieving lower exploitability compared to uniform regularization targets.