Repro: Public Feature Conditioning (ICML 2026)
Collection
Reproduction of ICML 2026 SDyesowNUa (arXiv:2606.18773): Trackio logbook + code/results bundle. • 2 items • Updated
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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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
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