Adeline Fermanian, Linus Bleistein, Agathe Guilloux, Van-Tuan Nguyen
We lifted 13 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| linusbleistein/signature_survival | canonical | 8 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| construct_df | Ran | linusbleistein/signature_survival/competing_methods/dynamic_deephit_ext.py pointer only (licence: NONE) · get_code("a99010f4674fd3e8") |
| convert_surv_label_structarray | Ran | linusbleistein/signature_survival/src/utils.py pointer only (licence: NONE) · get_code("22f2706ab78b8d50") |
| f_get_fc_mask1 | Ran | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/get_main_CF.py pointer only (licence: NONE) · get_code("8524522ce3f3bcf9") |
| get_log_intensity | Ran | linusbleistein/signature_survival/src/likelihood.py pointer only (licence: NONE) · get_code("881a27a187189828") |
| get_log_likelihood | Ran | linusbleistein/signature_survival/src/likelihood.py pointer only (licence: NONE) · get_code("576664b652c8956a") |
| get_log_survival | Ran | linusbleistein/signature_survival/src/likelihood.py pointer only (licence: NONE) · get_code("ce35d7295f480726") |
| get_seq_length | Ran | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/class_DeepLongitudinal.py pointer only (licence: NONE) · get_code("b74d071133b9bae6") |
| train_test_split | Ran | linusbleistein/signature_survival/src/utils.py pointer only (licence: NONE) · get_code("b405eaa827fab72a") |
| div | Not yet run | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/class_DeepLongitudinal.py pointer only (licence: NONE) · get_code("f612aa615ba6f44d") |
| div | Not yet run | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/get_main_CF.py pointer only (licence: NONE) · get_code("4e83c88decd2d5ae") |
| f_get_risk_predictions | Not yet run | linusbleistein/signature_survival/competing_methods/dynamic_deephit_ext.py pointer only (licence: NONE) · get_code("c5e4df5840058218") |
| log | Not yet run | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/class_DeepLongitudinal.py pointer only (licence: NONE) · get_code("9f47548505fcb421") |
| log | Not yet run | linusbleistein/signature_survival/competing_methods/Dynamic_DeepHit/get_main_CF.py pointer only (licence: NONE) · get_code("f7d44a32801617e6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We consider the task of learning individualspecific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel modelization approach in which the intensity is the solution to a controlled differential equation. We first design a neural estimator by building on neural controlled differential equations. In a second time, we show that our model can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator which we call CoxSig. We provide theoretical learning guarantees for both estimators, before showcasing the performance of our models on a vast array of simulated and real-world datasets from finance, predictive maintenance and food supply chain management.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.17077")
get_code_for_paper("2401.17077")
have("2401.17077")
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