| # DropoutTS reproduction bundle |
|
|
| Reproduction of **DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting** |
| (arXiv:2601.21726, OpenReview 7sksHLUvhH) for the Hugging Face "Reproducing ICML 2026" challenge. |
|
|
| Logbook: https://huggingface.co/spaces/ancs21/repro-dropoutts |
| Paper code: https://github.com/CityMind-Lab/DropoutTS |
|
|
| ## What's here |
| - `smoke_claim4.py` — local check of Claim 4a/4b (param count = 2*num_features+2; eval-mode no-op). |
| - `modal_repro.py` — Modal GPU pipeline: Informer +/- DropoutTS on synthetic sweep (Claim 1), |
| ETTh2 (Claim 2), and the Selective Learning combo (Claim 5). Also times training for Claim 4c. |
| - `analyze_claim1.py` — computes MSE/MAE improvements + the Claim 4c timing. |
| - `claim1_results.json`, `claim2_ETTh2_results.json`, `claim5_results.json` — raw run outputs. |
| - `claim1_table.csv`, `claim1_plot.html` — per-cell synthetic results + figure. |
| |
| ## Outcome |
| Claims 3, 4a, 4b verified. Claim 2 (ETTh2) reproduced (up to +59% MSE). Claim 1 not reproduced |
| under default config (mean -7.5%). Claim 4c contradicted (1.3x slower, not faster). Claim 5 |
| contradicted (combo underperformed Selective Learning alone). See the logbook for details. |
| |
| Ran on Modal A10G GPUs, single seed, default hyperparameters. ~51 training runs. |
| |
