7 citations
5 papers
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow +5
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding…
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation
Yuan Zhang, Xiaoran Xu, Hanning Zhou +1
Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and fl…
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
Michael Wojnowicz, Di Zhang, Glenn Chisholm +2
For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component ana…
A Formal Approach to the Problem of Logical Non-Omniscience
Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch +2
We present the logical induction criterion for computable algorithms that assign probabilities to every logical statement in a given formal language, and refine those probabilities…
A Formal Solution to the Grain of Truth Problem
Jan Leike, Jessica Taylor, Benya Fallenstein
A Bayesian agent acting in a multi-agent environment learns to predict the other agents' policies if its prior assigns positive probability to them (in other words, its prior conta…