19 citations · 30 across the 6 of their papers we have counts for
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cs.AI2021
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
Yuexiang Zhai, Christina Baek, Zhengyuan Zhou +2
Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt…
cs.LG2021★ 19 cited
Assessing Generalization of SGD via Disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek +1
We empirically show that the test error of deep networks can be estimated by simply training the same architecture on the same training set but with a different run of Stochastic G…