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sh-islam
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ciln-bench-adult

Tasks:
Tabular Classification
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Languages:
English
Size:
100K - 1M
Tags:
label-noise
instance-dependent-noise
benchmark
corruption
tabular
adult
Libraries:
Datasets
Dask
Polars
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  2. README.md +100 -0
  3. settings/gaussian_noise_sev1/noisy_label_train/adult_corrupted.parquet +3 -0
  4. settings/gaussian_noise_sev1/noisy_label_train/avg_softmax.npy +3 -0
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  21. settings/gaussian_noise_sev1/noisy_label_valid/softmax_tabpfn.npy +3 -0
  22. settings/gaussian_noise_sev1/noisy_label_valid/softmax_xgboost_dummyna.npy +3 -0
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  28. settings/gaussian_noise_sev3/noisy_label_train/softmax_catboost.npy +3 -0
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  50. settings/gaussian_noise_sev5/noisy_label_train/softmax_mlp.npy +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - tabular-classification
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+ language:
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+ - en
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+ tags:
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+ - label-noise
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+ - instance-dependent-noise
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+ - benchmark
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+ - corruption
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+ - tabular
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+ - adult
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+ - reproducibility
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+ size_categories:
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+ - 100K<n<1M
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+ pretty_name: "CILN-Bench: Adult"
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+ ---
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+
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+ # CILN-Bench: Adult
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+
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+ Instance-dependent label noise benchmarks built from controlled tabular corruptions.
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+
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+ This dataset is the **Adult** component of CILN-Bench. We corrupt the UCI Adult Income *noisy-label-train* (NLT) split with 5 standard tabular corruptions (Jenga family — 3 missingness mechanisms + 2 value perturbations, 3 severities each → 15 settings), then let a 5-voter pool classify the corrupted rows. The resulting soft labels are released as a noisy-label benchmark.
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+
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+ * **Code, examples, and reproducibility tests:** <https://github.com/sh-islam/ciln-bench>
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+ * **Companion datasets:** [ciln-bench-cifar10](https://huggingface.co/datasets/sh-islam/ciln-bench-cifar10), [ciln-bench-mnist](https://huggingface.co/datasets/sh-islam/ciln-bench-mnist)
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+
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+ ## Settings released
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+
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+ 5 corruption types × 3 severities = **15 settings**.
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+
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+ | Family | Corruptions |
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+ |---|---|
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+ | Missingness | missing_mcar, missing_mar, missing_mnar |
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+ | Value perturbation | gaussian_noise, scaling |
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+
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+ Severity controls the corrupted-row fraction (sev 1 → 5%, sev 3 → 25%, sev 5 → 50%). For `missing_mar`, corruption probability depends on the observed `sex` attribute; for `missing_mnar`, it depends on the values being corrupted.
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+
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+ Noise rate ranges from 14.7% to 26.3% across the 15 settings.
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+
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+ ## Voter pool
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+
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+ 5 voters: **XGBoost**, **CatBoost**, **RTDL-MLP**, **FT-Transformer**, **TabPFN**.
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+
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+ ## Repository layout
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+
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+ ```
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+ settings/
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+ ├── gaussian_noise_sev1/
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+ │ ├── noisy_label_train/
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+ │ │ ├── adult_corrupted.parquet # corrupted feature rows
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+ │ │ ├── labels.npy # (N,) int — ground-truth income label
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+ │ │ ├── softmax_xgboost_dummyna.npy
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+ │ │ ├── softmax_catboost.npy
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+ │ │ ├── softmax_mlp.npy
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+ │ │ ├── softmax_ft_transformer.npy
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+ │ │ ├── softmax_tabpfn.npy
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+ │ │ ├── avg_softmax.npy
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+ │ │ ├── manifest.json
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+ │ │ └── params.jsonl
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+ │ └── noisy_label_valid/
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+ │ └── ... (same structure)
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+ └── ... (15 settings total)
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+ ```
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+
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+ ## How to load
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+
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+ ```python
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+ import numpy as np
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+ import pandas as pd
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+ from huggingface_hub import snapshot_download
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+
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+ local = snapshot_download(
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+ repo_id="sh-islam/ciln-bench-adult",
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+ repo_type="dataset",
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+ allow_patterns=["settings/missing_mar_sev3/noisy_label_train/*"],
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+ )
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+
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+ features = pd.read_parquet(f"{local}/settings/missing_mar_sev3/noisy_label_train/adult_corrupted.parquet")
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+ labels = np.load(f"{local}/settings/missing_mar_sev3/noisy_label_train/labels.npy")
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+ print(features.shape, labels.shape)
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+ ```
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+
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+ `allow_patterns` is a filter that limits which files get downloaded. Pass a glob (or a list of globs) and only matching files come down. Omit it to download the full dataset.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @inproceedings{cilnbench2027,
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+ title = {CILN-Bench: A Benchmark for Corruption-Induced Label Noise},
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+ author = {Islam, Shadman and Kristiadi, Agustinus and Milani, Mostafa},
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+ booktitle = {ICDE},
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+ year = {2027}
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+ }
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+ ```
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+
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+ ## License
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+
100
+ MIT.
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