15 citations · 21 across the 4 of their papers we have counts for
5 papers
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
Lucio M. Dery, Abram L. Friesen, Nando De Freitas +2
As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has n…
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan +6
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control…
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram L. Friesen, Andrea Banino +13
Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has se…
On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani +7
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learn…
Modular Meta-Learning with Shrinkage
Yutian Chen, Abram L. Friesen, Feryal Behbahani +4
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components…