Markov Models
Temporally Disentangled Representation Learning under Unknown Nonstationarity Xiangchen Song
However, in nonstationary setting, existing work only partially addressed the problem by either utilizing observed auxiliary variables (e.g., class labels and/or domain indexes) as side-information or assuming simplified latent causal dynamics. Both constrain the method to a limited range of scenarios.
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
In this section, we provide brief background on information theory and details on our notation.
Measuring Goal-Directedness
In order to build more useful AI systems, a natural inclination is to try to make them more agentic . But while agents built from language models are touted as the next big advance [Wang et al., 2024],
