Zhao-Rong Lai, Yuanchao Wang, Tianqi Zhong
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 |
|---|---|---|
| laizhr/OOD-TV-IRM | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| Irmv1_Multi_Class_TVL1 | Not yet run | laizhr/OOD-TV-IRM/OOD-TV/OOD-TV_House_Price/algorithms/irmv1_multi_class_tvl1.py pointer only (licence: NONE) · get_code("ccbd4bc1b6c51f21") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Invariant risk minimization is an important general machine learning framework that has recently been interpreted as a total variation model (IRM-TV). However, how to improve out-of-distribution (OOD) generalization in the IRM-TV setting remains unsolved. In this paper, we extend IRM-TV to a Lagrangian multiplier model named OOD-TV-IRM. We find that the autonomous TV penalty hyperparameter is exactly the Lagrangian multiplier. Thus OOD-TV-IRM is essentially a primal-dual optimization model, where the primal optimization minimizes the entire invariant risk and the dual optimization strengthens the TV penalty. The objective is to reach a semi-Nash equilibrium where the balance between the training loss and OOD generalization is maintained. We also develop a convergent primaldual algorithm that facilitates an adversarial learning scheme. Experimental results show that OOD-TV-IRM outperforms IRM-TV in most situations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.19665")
get_code_for_paper("2502.19665")
have("2502.19665")
Connect an agent — have() is free.