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Found 8 papers, 0 papers with code
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.
Constrained Learning with Non-Convex Losses
In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic.
Towards Safe Continuing Task Reinforcement Learning
However, to transfer from learning safety to learning safely, there are two hurdles that need to be overcome: (i) it has to be possible to learn the policy without having to re-initialize the system; and (ii) the rollouts of the system need to be in themselves safe.
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.
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.
The empirical duality gap of constrained statistical learning
This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information processing.
Safe Policies for Reinforcement Learning via Primal-Dual Methods
The advantages of the proposed relaxation are threefold.
Constrained Reinforcement Learning Has Zero Duality Gap
The later is generally addressed by formulating the conflicting requirements as a constrained RL problem and solved using Primal-Dual methods.
