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Found 6 papers, 6 papers with code
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
We conclude by introducing a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows.
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees.
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them.
The balancing principle for parameter choice in distance-regularized domain adaptation
Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems.
A Dataset Perspective on Offline Reinforcement Learning
The dataset characteristics are determined by the behavioral policy that samples this dataset.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks.
General Reinforcement Learning
Multiple Sequence Alignment
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