| # Reproduction: Private Learning with Public Feature Conditioning (ICML 2026) |
|
|
| Independent reproduction of **"Private Learning with Public Feature Conditioning"** |
| (Shuli Jiang, Walid Krichene, Nicolas Mayoraz; ICML 2026, OpenReview `SDyesowNUa`, |
| [arXiv:2606.18773](https://arxiv.org/abs/2606.18773)) for the Hugging Face x AlphaXiv |
| ICML 2026 reproducibility challenge. |
|
|
| **Logbook (results, figures, verdicts):** |
| https://huggingface.co/spaces/dwahdany/repro-private-learning-public-feature-conditioning |
|
|
| ## Layout |
|
|
| - `condp/` — re-implementation: datasets (`data.py`), DPSGD/Cond-DP training with |
| Opacus noisy Adam (`train.py`), RR-on-Bins baseline (`rronbins.py`, Ghazi et al. 2023), |
| Lemma 4.13 bound formulas (`bounds.py`), Criteo pipeline (`criteo.py`). |
| - `modal_app.py` — Modal app: CPU batch worker, Criteo download/preprocess, |
| A100 GPU worker, server-side sweep driver. |
| - `scripts/` — sweep/refine orchestration per claim, probes, bound verification. |
| - `analysis/` — paper target numbers (appendix tables) + figure generation. |
| - `tests/` — unit tests for Claims 1–2. |
| - `figdata/` — data extracted from the paper's vector-PDF figures (spectra, |
| synthetic-experiment curves) used to pin down unstated preprocessing. |
| - `outputs/` — all experiment results (JSONL/JSON/CSV), comparison tables, figures. |
| - `poster/` — reproduction poster (posterly). |
|
|
| ## Reproducing |
|
|
| ```bash |
| uv sync |
| uv run pytest tests/ # Claims 1-2 unit verification |
| uv run python scripts/claim3_bounds.py # Lemma 4.13 numeric check |
| # Modal experiments (requires modal token): |
| uv run modal run scripts/sweep.py::main --claim 4 --algos dpsgd,conddp |
| uv run modal run scripts/refine.py::main --claim 4 --algos dpsgd,conddp |
| uv run modal run modal_app.py::prepare # Criteo (~2GB download, cached in Volume) |
| uv run modal run --detach modal_app.py::criteo_launch --model linear --phase tune --numeric-mode log_standard |
| ``` |
|
|
| Datasets: Boston (CMU), wine/energy (UCI), CA housing (sklearn) download automatically. |
| Criteo Sponsored Search Conversion: the official link is dead; we use Criteo's own |
| Azure blob `https://criteostorage.blob.core.windows.net/criteo-research-datasets/Criteo_Conversion_Search.tar.gz`. |
|
|
| The author's official (unlinked) code, found via GitHub search and used to reconcile |
| protocol details: https://github.com/11hifish/cond-dp |
|
|
