Learning from small data sets is difficult in the absence of specific domain knowledge. We present a regularized linear model called STEW that benefits from a generic and prevalent form of prior knowledge: feature directions. STEW shrinks weights toward each other, converging to an equal-weights solution in the limit of infinite regularization. We provide theoretical results on the equal-weights solution that explains how STEW can productively trade-off bias and variance. Across a wide range of learning problems, including Tetris, STEW outperformed existing linear models, including ridge regression, the Lasso, and the non-negative Lasso, when feature directions were known. The model proved to be robust to unreliable (or absent) feature directions, still outperforming alternative models under diverse conditions. Our results in Tetris were obtained by using a novel approach to learning in sequential decision environments based on multinomial logistic regression.
|Publication status||Published - 15 Jun 2019|
|Event||Thirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, USA United States|
Duration: 9 Jun 2019 → 15 Jun 2019
Conference number: 36
|Conference||Thirty-sixth International Conference on Machine Learning|
|Country||USA United States|
|Period||9/06/19 → 15/06/19|
- Machine learning
- Reinforcement learning
- Equal weights
Lichtenberg, J., & Şimşek, Ö. (2019). Regularization in Directable Environments with Application to Tetris. 3953-3962. Paper presented at Thirty-sixth International Conference on Machine Learning, Long Beach, USA United States.