Optimal Bounds between f-Divergences and Integral Probability Metrics
Authors: Rohit Agrawal, Thibaut Horel
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Starting from a tight variational representation of the f-divergence, we derive a generalization of the moment generating function, which we show exactly characterizes the best lower bound of the f-divergence as a function of a given IPM. Using this characterization, we obtain new bounds on IPMs defined by classes of unbounded functions, while also recovering in a unified manner well-known results for bounded and subgaussian functions (e.g. Pinsker s inequality and Hoeffding s lemma). |
| Researcher Affiliation | Academia | 1Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA 2Institute for Data, Systems, and Society, MIT, Cambridge, Massachusetts, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or describe datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |