Understanding the Limits of Deep Tabular Methods with Temporal Shift
Authors: Haorun Cai, Han-Jia Ye
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that this temporal embedding, combined with the improved training protocol, provides a more effective and robust framework for learning from temporal tabular data. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Nanjing University, China 2National Key Laboratory for Novel Software Technology, Nanjing University, China. Correspondence to: Han-Jia Ye <EMAIL>. |
| Pseudocode | No | The paper describes methods verbally and mathematically, including equations for Fourier series expansion and temporal embedding, but does not present any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is now available at https://github.com/LAMDA-Tabular/Tabular-Temporal-Shift. |
| Open Datasets | Yes | We adopt the full Tab Re D dataset (Rubachev et al., 2025) without modification. ... Table 3. Overview of Datasets. Task descriptions from Rubachev et al. (2025). |
| Dataset Splits | Yes | Tab Re D (Rubachev et al., 2025) adopts a temporal split where the data is divided at Tval, such that Dtrain = S t Tval Dt and Dval = S Tval<t Ttrain Dt. ... The random split is subject to variability from both the split selection and the running seeds during the training phase. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware used for running the experiments, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | We tune hyperparameters using Optuna (Akiba et al., 2019)... For all deep learning methods, we use a batch size of 1024 and Adam W (Loshchilov & Hutter, 2019) as the optimizer... While specific tools (Optuna, Adam W) are mentioned with citations, explicit version numbers for core software components like Python, PyTorch/TensorFlow, or other libraries are not provided. |
| Experiment Setup | Yes | We tune hyperparameters using Optuna (Akiba et al., 2019), performing 100 trials for most methods... For all deep learning methods, we use a batch size of 1024 and Adam W (Loshchilov & Hutter, 2019) as the optimizer, with an early stopping patience of 16. |