In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting arises in numerous fields including the design of materials, robots, DNA sequences, and proteins. Recent approaches train a deep neural network (DNN) on the static dataset to act as a proxy function, and then perform gradient ascent on the existing designs to obtain potentially high-scoring designs. This methodology frequently suffers from the out-of-distribution problem where the proxy function often returns poor designs. To mitigate this problem, we propose BiDirectional learning for offline Infinite-width model-based optimization} (BDI). BDI consists of two mappings: the forward mapping leverages the static dataset to predict the scores of the high-scoring designs, and the backward mapping leverages the high-scoring designs to predict the scores of the static dataset. The backward mapping, neglected in previous work, can distill more information from the static dataset into the high-scoring designs, which effectively mitigates the out-of-distribution problem. For a finite-width DNN model, the loss function of the backward mapping is intractable and only has an approximate form, which leads to a significant deterioration of the design quality. We thus adopt an infinite-width DNN model, and propose to employ the corresponding neural tangent kernel to yield a closed-form loss for more accurate design updates. Experiments on {various} tasks verify the effectiveness of BDI. The code is available at https://github.com/GGchen1997/BDI.
翻译:在离线模型优化中,我们努力通过仅利用设计和分数的静态数据集来最大限度地优化黑箱目标功能。 这个问题的设置出现在多个领域, 包括材料、 机器人、 DNA序列和蛋白质的设计。 最近的方法在静态数据集上训练了深神经网络( DNN) 以作为代理功能, 然后在现有的设计上进行梯度上升, 以获得潜在的高分数设计。 这个方法经常受到分配外问题的影响, 代用功能通常会返回较差的设计。 为了缓解这个问题, 我们提议为离线的离线设计进行双轨学习。 包括材料、 机器人、 DNA序列和蛋白质优化( BDI) 。 远方绘图利用静态数据集来预测高分数, 然后利用现有的高分数设计 。 后向后方位绘图, 将固定数据元代码中的更多信息插入到高分数的代码中 。 远端数据元数据元值的轨迹校验, 有效减少内值的内值和内值的内值优化优化 设计 。 因此, D 的内值的内值的内置的内置的内向性设计, 正在变变的变换的D- 格式, 。