Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
The Geometry and Calculus of Losses
Authors: Robert C. Williamson, Zac Cranko
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper contains no new algorithms and no experimental results. What it does contain is a new way of looking at loss functions which 1) illustrates the close connection between losses and norms and anti-norms; 2) presents the new idea of an antipolar loss; 3) develops a calculus for loss functions that allows multiple proper loss functions to be combined in a manner that the resulting loss is guaranteed proper; and 4) shows how the geometrical perspective can be used to design loss functions. |
| Researcher Affiliation | Academia | Robert C. Williamson EMAIL University of Tübingen and Tübingen AI Center, Germany; Zac Cranko EMAIL Sydney, Australia |
| Pseudocode | No | The paper contains no new algorithms and no experimental results. What it does contain is a new way of looking at loss functions... |
| Open Source Code | No | The paper contains no new algorithms and no experimental results. |
| Open Datasets | No | The paper contains no new algorithms and no experimental results. |
| Dataset Splits | No | The paper contains no new algorithms and no experimental results. |
| Hardware Specification | No | The paper contains no new algorithms and no experimental results. |
| Software Dependencies | No | The paper contains no new algorithms and no experimental results. |
| Experiment Setup | No | The paper contains no new algorithms and no experimental results. |