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6 results for transition-based

Paper estimates risks in MDPs using state lumping and SAT, showing its effectiveness.

problem Estimating risks in Markov decision processes with state augmentation.
method State augmentation transformation, isotopic states, and state lumping.
result SAT and state lumping effectively estimate mean-variance and exponential utility risks.

CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.

problem Inefficient global Koopman operator approximation for distinct local dynamics.
method Cluster-Weighted EDMD (CW-EDMD) learns a soft phase-space partition and per-cluster EDMD operators using EM objective.
result CW-EDMD significantly reduces prediction errors across various systems and configurations.

Concept modulation models unify identifiability and extrapolation in conditional latent variable models.

problem Reliable generalization in conditional latent variable models
method Concept modulation models (CMMs) with structure AoΛoCoXA o Λ o C o X
result Lifts identifiability to conditional settings and controls extrapolation through attribute potentials.

CEA augments reinforcement learning by generating counterfactual experiences.

problem Challenges in reinforcement learning, especially out-of-distribution and inefficient exploration.
method CEA uses variational autoencoders to model state transitions and introduces randomness for non-stationarity. It expands learning data through counterfactual inference.
result CEA outperforms SOTA algorithms in diverse environments.

Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has achieved promising results and presented a theoretical guarantee on performance in the case of class-conditional noise. However, this type of a…

2019-03-06abs ↗pdf ↗