Learning Graphical Models
Reinforcement Learning with L TL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le
In this work, we study the relationship between these objectives.
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Multiple types of inference are available for probabilistic graphical models, e.g.,
Label Noise: Ignorance Is Bliss
We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptation, in particular, domain adaptation under posterior drift.
A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts
Recent evidence suggests that, in some problems, NeSy models can achieve high accuracy on the reasoning task by learning concepts with incorrect semantics .
Agent Planning with World Knowledge Model
Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric W orld K nowledge M odel ( WKM) to facilitate agent
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
See more details in our project page: https://sites.google.com/view/be-cause.
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Critical in this context is the prior knowledge accumulated from related tasks.
N-Agent Ad Hoc Teamwork
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings.
