Zixuan Wu, Yating Liu, Jin Yeo, Jung, So Jeong, Claire Donnat
We lifted 15 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 |
|---|---|---|
| yeojin-jung/speedcp | canonical | 0 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| add_node_feats | Not yet run | yeojin-jung/speedcp/experiments/common/moleculenet_helpers.py code served (permissive licence) · get_code("07af09a57ea1d5b5") |
| clr | Not yet run | yeojin-jung/speedcp/speedcp/utils.py code served (permissive licence) · get_code("30de34a50a7281c6") |
| compute_coverage | Not yet run | yeojin-jung/speedcp/experiments/common/moleculenet_helpers.py code served (permissive licence) · get_code("562210c7f6aec45c") |
| fcp_predict_inner | Not yet run | yeojin-jung/speedcp/experiments/real_data/run_mri.py code served (permissive licence) · get_code("1f48dc69f99d66e4") |
| fcp_scores | Not yet run | yeojin-jung/speedcp/experiments/real_data/run_mri.py code served (permissive licence) · get_code("fd151645cecab3f8") |
| kernel | Not yet run | yeojin-jung/speedcp/speedcp/utils.py code served (permissive licence) · get_code("62ef2c7c0e6f7e9d") |
| lambda_init | Not yet run | yeojin-jung/speedcp/speedcp/lambda_trace.py code served (permissive licence) · get_code("c643ffaaf3eaece0") |
| load_or_fit_reference_pca | Not yet run | yeojin-jung/speedcp/experiments/molecular/run_esol.py code served (permissive licence) · get_code("a910623f46443974") |
| load_or_fit_reference_pca | Not yet run | yeojin-jung/speedcp/experiments/molecular/run_qm7b.py code served (permissive licence) · get_code("cf54cfcea83766df") |
| load_or_fit_reference_pca | Not yet run | yeojin-jung/speedcp/experiments/molecular/run_qm9.py code served (permissive licence) · get_code("a49dada588c51ca8") |
| make_loader | Not yet run | yeojin-jung/speedcp/experiments/molecular/run_esol.py code served (permissive licence) · get_code("a0a653b31952f3d9") |
| onehot | Not yet run | yeojin-jung/speedcp/experiments/molecular/run_qm7b.py code served (permissive licence) · get_code("29f2999901c7b6d6") |
| pinball | Not yet run | yeojin-jung/speedcp/speedcp/utils.py code served (permissive licence) · get_code("49d2ca0ba2052164") |
| select_target | Not yet run | yeojin-jung/speedcp/experiments/common/moleculenet_helpers.py code served (permissive licence) · get_code("5692fbd2e6078394") |
| vanilla_scores | Not yet run | yeojin-jung/speedcp/experiments/real_data/run_mri.py code served (permissive licence) · get_code("2e2e6b1fb2dc45b5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. RKHS-based frameworks-while promising for complex covariate shifts-suffer from prohibitive computational costs. To guarantee conditional validity under such shifts while ensuring feasibility, we build upon the framework of Gibbs et al. [2025] by introducing a stable and efficient algorithm that computes the full solution path of the regularized RKHS conformal optimization problem, at essentially the same cost as a single kernel quantile fit. Our approach provides simultaneous hyperparameter tuning for smoothness control and data-adaptive calibration. To extend the method to high-dimensional settings, we further integrate our approach with low-rank latent embeddings that capture conditional validity in a data-driven latent space. Empirically, our method provides reliable conditional coverage across a variety of modern black-box predictors, improving the interval length of Gibbs et al. [2025] by 30%, while achieving a 40-fold speedup.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2509.24100")
get_code_for_paper("2509.24100")
have("2509.24100")
Connect an agent — have() is free.