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Found 8 papers, 5 papers with code
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making.
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks.
Bridging the Gap Between Target Networks and Functional Regularization
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
Iterative Amortized Policy Optimization
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
However, introduction of corrupt or stochastic rewards can yield high variance in learning.
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
In this paper, we adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform sampling strategy based on block duality gaps.
Bayesian Nonparametric Modeling of Heterogeneous Groups of Censored Data
Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics.
