多任务学习作为讨价还价游戏
Multi-Task Learning as a Bargaining Game
摘要：
在多任务学习（MTL）中，联合模型被训练为同时对多个任务进行预测。联合训练降低了计算成本并提高了数据效率;然而，由于这些不同任务的梯度可能会冲突，因此训练MTL的联合模型通常会比相应的单任务模型产生更低的性能。缓解此问题的常用方法是使用特定的启发式将每个任务的梯度组合到联合更新方向中。在本文中，我们建议将梯度组合步骤视为一个讨价还价的游戏，其中任务进行协商，以达成一项协议的参数更新的联合方向。在某些假设下，讨价还价问题有一个独特的解决方案，称为纳什讨价还价解决方案，我们建议使用它作为多任务学习的原则方法。我们描述了一个新的MTL优化过程，纳什MTL，并推导出其收敛性的理论保证。从经验上讲，我们表明，纳什MTL在多个领域的MTL基准测试中取得了最先进的结果。
英文摘要：
In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Joint training reduces computation costs and improves data efficiency; however, since the gradients of these different tasks may conflict, training a joint model for MTL often yields lower performance than its corresponding single-task counterparts. A common method for alleviating this issue is to combine per-task gradients into a joint update direction using a particular heuristic. In this paper, we propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update. Under certain assumptions, the bargaining problem has a unique solution, known as the Nash Bargaining Solution, which we propose to use as a principled approach to multi-task learning. We describe a new MTL optimization procedure, Nash-MTL, and derive theoretical guarantees for its convergence. Empirically, we show that Nash-MTL achieves state-of-the-art results on multiple MTL benchmarks in various domains.
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