PSD Representations for Effective Probability Models
Authors: Alessandro Rudi, Carlo Ciliberto
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we show that a recently proposed class of positive semidefinite (PSD) models for non-negative functions is particularly suited to this end. In particular, we characterize both approximation and generalization capabilities of PSD models, showing that they enjoy strong theoretical guarantees. Moreover, we show that we can perform efficiently both sum and product rule in closed form via matrix operations, enjoying the same versatility of mixture models. Our results open the way to applications of PSD models to density estimation, decision theory and inference. |
| Researcher Affiliation | Academia | Alessandro Rudi Inria, École normale supérieure, CNRS, PSL Research University, Paris, France alessandro.rudi@inria.fr Carlo Ciliberto Department of Computer Science University College London, London, UK c.ciliberto@ucl.ac.uk |
| Pseudocode | Yes | Algorithm 1 PSD Hidden Markov Model |
| Open Source Code | No | The paper states, 'we plan to develop a library for operations with PSD models and make it available to the community' in the 'Future Directions' section, indicating future availability, not current release, and no specific link is provided for the current work. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific, named datasets for which public availability information would be relevant. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with training, validation, and test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameter values or training configurations. |