Tal Ellinson, Hadi Afshar, Sally Cripps
We lifted 18 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| shap/shap | canonical | 4 of 8 |
| rajesh-lab/realx | — | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| REBAR_Bernoulli_Sampler | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("425adb9d23ca6ceb") |
| Random_Bernoulli_Sampler | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("4512df516921e82a") |
| ResizeMask | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("18c9a5305359f35c") |
| compute_output_dims | Ran | shap/shap/shap/_explanation.py code served (permissive licence) · get_code("9a8714c1d8f19c47") |
| concrete_relaxation | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("562fcc810be4f558") |
| identity | Ran | shap/shap/shap/links.py code served (permissive licence) · get_code("db29607e6029d39f") |
| link_reweighting | Ran | shap/shap/shap/utils/_masked_model.py code served (permissive licence) · get_code("2ce33a90fabc6cd6") |
| logit | Ran | shap/shap/shap/links.py code served (permissive licence) · get_code("ff39ee5daf145263") |
| reparameterize | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("f588900884b4d801") |
| safe_clip | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("93a4d2f4585b5a6d") |
| safe_log_prob | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("9a8deb7fca8e94e3") |
| v_from_u | Ran | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("06d38afda92dc17c") |
| REALX | Not yet run | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("7ecc1cea6485cad5") |
| SELECTOR | Not yet run | rajesh-lab/realx/realx.py code served (permissive licence) · get_code("3f2c6b3f8325f12c") |
| coef | Not yet run | shap/shap/shap/benchmark/methods.py code served (permissive licence) · get_code("ac9715771309b34c") |
| linear_shap_corr | Not yet run | shap/shap/shap/benchmark/methods.py code served (permissive licence) · get_code("30c80905de82e540") |
| linear_shap_ind | Not yet run | shap/shap/shap/benchmark/methods.py code served (permissive licence) · get_code("82235fa109121626") |
| make_masks | Not yet run | shap/shap/shap/utils/_masked_model.py code served (permissive licence) · get_code("66df016e1814153a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important features, and a predictor, which uses these to make predictions. However, these pioneering methods face challenges including information leakage and lack of differentiability, which can slow training. In this paper, we present Hide&Seek, an end-to-end differentiable model for instance-wise feature selection. We jointly learn feature selection and prediction under a single objective without information leakage. Hide&Seek outperforms existing state-of-the-art models across a range of experiments and is fast to train. We achieve this by reformulating feature removal as a differentiable operation where instead of discretely removing features, we replace a proportion of each feature. Training is further stabilized via a parsimony-weight annealing framework.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2608.16689")
get_code_for_paper("2608.16689")
have("2608.16689")
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