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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) 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

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