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Multi-task Bias-Variance Trade-off Through Functional Constraints
In this paper we draw intuition from the two extreme learning scenarios -- a single function for all tasks, and a task-specific function that ignores the other tasks dependencies -- to propose a bias-variance trade-off.
Multi-task Supervised Learning via Cross-learning
In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks.
Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes
To that end we compute unbiased stochastic gradients of the value function which we use as ascent directions to update the policy.
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