Sivaraman Balakrishnan, Daniel Hsu, Lujing Zhang
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lujingz/shaky_prepend | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| shaky_prepend | Not yet run | lujingz/shaky_prepend/src/shaky_prepend.py pointer only (licence: NONE) · get_code("3ea6042970881451") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose Shaky Prepend, a method that leverages tools inspired by differential privacy to obtain improved theoretical guarantees over existing approaches. Through numerical experiments, we demonstrate that Shaky Prepend adapts to both group structure and spatial heterogeneity. We provide practical guidance for deploying multi-group learning algorithms in real-world settings.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.07319")
get_code_for_paper("2603.07319")
have("2603.07319")
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