Datasets:

Vidushee
/

iclr-rejected-papers-with-code-1k

forum stringlengths 9 11	year int64 2.02k 2.03k	status stringclasses 1 value	title stringlengths 17 130	authors listlengths 1 25	authorids listlengths 1 25	authors_source stringclasses 1 value	abstract stringlengths 490 2.28k	decision stringclasses 1 value	meta_review stringclasses 2 values	num_reviews int64 2 6	reviews listlengths 2 6	openreview_url stringlengths 41 43	arxiv_id stringlengths 10 10 ⌀	paper_pdf stringlengths 20 22	paper_pdf_sha256 stringlengths 64 64	paper_pdf_bytes int64 146k 50.9M	paper_pdf_source stringclasses 1 value	code_url stringlengths 27 119	code_repository stringlengths 8 100	code_commit stringlengths 40 40	code_archive stringlengths 19 21	code_archive_sha256 stringlengths 64 64	code_archive_bytes int64 5.09k 15.1M	code_file_count int64 3 2.04k	code_extensions unknown	github_disk_usage_kb int64 10 9.98k	github_languages unknown	github_archived bool 2 classes	github_pushed_at timestamp[s]date 2017-08-07 21:42:44 2026-09-07 21:43:54	code_match dict	provenance dict
maQs92RfZ8	2,026	rejected	Exploring Federated Pruning for Large Language Models	[ "Pengxin Guo", "Yinong Wang", "Wei Li", "Mengting Liu", "Ming Li", "Jinkai Zheng", "Liangqiong Qu" ]	[ "~Pengxin_Guo1", "~Yinong_Wang1", "~Wei_Li82", "~Mengting_Liu6", "~Ming_Li21", "~Jinkai_Zheng1", "~Liangqiong_Qu2" ]	OpenReview API	LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which can be challenging to obtain in privacy-sensitive domains. To address this issue, we introduce FedPr...	Reject		4	[ { "id": "Ig2rLtyzjg", "reviewer_signature": [ "ICLR.cc/2026/Conference/Submission15594/Reviewer_y1jX" ], "rating": 2, "soundness": 2, "presentation": 3, "contribution": 2, "confidence": 4, "summary": "The paper introduces FedPrLLM, a federated pruning framework for large la...	https://openreview.net/forum?id=maQs92RfZ8	2505.13547	papers/maQs92RfZ8.pdf	0f832bd2d83e0aa83cc5bca4f46bcf23535fe22aacba18b90418dccb761e7e30	977,965	openreview	https://github.com/Pengxin-Guo/FedPrLLM	Pengxin-Guo/FedPrLLM	7e91d12e0e3250a2790e534d15140ba163cf7739	repos/maQs92RfZ8.zip	e199692b9595ba88bd1c0e513a67d2dcd63eaf197fe09894a93ff2ac59b8fbf3	11,259	5	{ ".py": 5 }	10	{ "Python": 48799 }	false	2025-05-15T13:33:09	{ "method": "exact_normalized_title", "pwc_official": true, "pwc_mentioned_in_paper": true, "pwc_url": "https://paperswithcode.com/paper/exploring-federated-pruning-for-large" }	{ "paper_reviews_decision": "OpenReview API", "paper_code_mapping": "Papers With Code archive", "repository_snapshot": "GitHub API commit-pinned ZIP" }
Q5CLpqbrFM	2,025	rejected	Learning Representations of Instruments for Partial Identification of Treatment Effects	[ "Jonas Schweisthal", "Dennis Frauen", "Maresa Schröder", "Konstantin Hess", "Niki Kilbertus", "Stefan Feuerriegel" ]	[ "~Jonas_Schweisthal1", "~Dennis_Frauen1", "~Maresa_Schröder1", "~Konstantin_Hess1", "~Niki_Kilbertus1", "~Stefan_Feuerriegel1" ]	OpenReview API	Reliable estimation of treatment effects from observational data is important in many disciplines, such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-dimensional) instru...	Reject		4	[ { "id": "TMRkMRwzzF", "reviewer_signature": [ "ICLR.cc/2025/Conference/Submission6977/Reviewer_hFWX" ], "rating": 8, "soundness": 3, "presentation": 4, "contribution": 3, "confidence": 3, "summary": "This paper provides a method for the partial identification of treatment e...	https://openreview.net/forum?id=Q5CLpqbrFM	2410.08976	papers/Q5CLpqbrFM.pdf	78ed1fb7e0c8058d17c09de55c23dba0815eb61958e93f49b3121f194d09c6fe	2,027,941	openreview	https://github.com/JSchweisthal/ComplexPartialIdentif	JSchweisthal/ComplexPartialIdentif	260547060aa0f2b9e71f0179768f8a534bf6fd19	repos/Q5CLpqbrFM.zip	833358fb96d33775e0a634fb59489f4a4f0e0163187deb4dc1cc534cce81ddbf	11,992	7	{ ".py": 7 }	10	{ "Python": 36573 }	false	2024-10-11T16:02:21	{ "method": "exact_normalized_title", "pwc_official": true, "pwc_mentioned_in_paper": true, "pwc_url": "https://paperswithcode.com/paper/learning-representations-of-instruments-for" }	{ "paper_reviews_decision": "OpenReview API", "paper_code_mapping": "Papers With Code archive", "repository_snapshot": "GitHub API commit-pinned ZIP" }
qYb0CANLGC	2,024	rejected	Auto-Regressive Next-Token Predictors are Universal Learners	[ "eran malach" ]	[ "~eran_malach1" ]	OpenReview API	Large language models display remarkable capabilities in logical and mathematical reasoning, allowing them to solve complex tasks. Interestingly, these abilities emerge in networks trained on the simple task of next-token prediction. In this work, we present a theoretical framework for studying auto-regressive next-tok...	Reject		3	[ { "id": "qphMWZRIUp", "reviewer_signature": [ "ICLR.cc/2024/Conference/Submission3681/Reviewer_RXBR" ], "rating": "5: marginally below the acceptance threshold", "soundness": "2 fair", "presentation": "3 good", "contribution": "2 fair", "confidence": "4: You are confident in yo...	https://openreview.net/forum?id=qYb0CANLGC	2309.06979	papers/qYb0CANLGC.pdf	ebe78d35f4d70fbb451c6e62b818867e5bc4cc9c62b6562566f0c26d4f1b2d26	363,218	openreview	https://github.com/emalach/LinearLM	emalach/LinearLM	11fb93e06b9738a91b3463b7b0b7b63095928827	repos/qYb0CANLGC.zip	d5957a399d05f42c6edeb12417cb7a03d2c6949a12da9f08796bc5e978751112	7,725	3	{ ".py": 3 }	11	{ "Python": 14953 }	false	2024-07-29T20:36:18	{ "method": "exact_normalized_title", "pwc_official": true, "pwc_mentioned_in_paper": true, "pwc_url": "https://paperswithcode.com/paper/auto-regressive-next-token-predictors-are" }	{ "paper_reviews_decision": "OpenReview API", "paper_code_mapping": "Papers With Code archive", "repository_snapshot": "GitHub API commit-pinned ZIP" }
LgjKqSjDzr	2,022	rejected	SALT : Sharing Attention between Linear layer and Transformer for tabular dataset	[ "Juseong Kim", "Jinsun Park", "Giltae Song" ]	[ "~Juseong_Kim2", "~Jinsun_Park1", "gsong@pusan.ac.kr" ]	OpenReview API	Handling tabular data with deep learning models is a challenging problem despite their remarkable success in vision and language processing applications. Therefore, many practitioners still rely on classical models such as gradient boosting decision trees (GBDTs) rather than deep networks due to their superior performa...	Reject	null	4	[ { "id": "e0CrrbNMzTH", "reviewer_signature": [ "ICLR.cc/2022/Conference/Paper2498/Reviewer_eW8f" ], "rating": "", "confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission...	https://openreview.net/forum?id=LgjKqSjDzr	null	papers/LgjKqSjDzr.pdf	e51405d2a961863fea10f81c16e63b1564adf13a5f116e713f6a9337c8e0c1a2	999,515	openreview	https://github.com/Juseong03/SALT	Juseong03/SALT	8d95159be073be64d1ba1af1017ae8870a35005a	repos/LgjKqSjDzr.zip	1f5edbaadaeb7ad970dcf27b46a64c5559d3962ce2206a0c50a4ca9b4ecf0b04	9,616	4	{ ".py": 4 }	10	{ "Python": 35982 }	false	2021-10-28T02:33:04	{ "method": "exact_normalized_title", "pwc_official": true, "pwc_mentioned_in_paper": true, "pwc_url": "https://paperswithcode.com/paper/salt-sharing-attention-between-linear-layer" }	{ "paper_reviews_decision": "OpenReview API", "paper_code_mapping": "Papers With Code archive", "repository_snapshot": "GitHub API commit-pinned ZIP" }
kmBFHJ5pr0o	2,021	rejected	Distributed Adversarial Training to Robustify Deep Neural Networks at Scale	[ "Gaoyuan Zhang", "Songtao Lu", "Sijia Liu", "Xiangyi Chen", "Pin-Yu Chen", "Lee Martie", "Lior Horesh", "Mingyi Hong" ]	[ "~Gaoyuan_Zhang1", "~Songtao_Lu1", "~Sijia_Liu1", "~Xiangyi_Chen1", "~Pin-Yu_Chen1", "lee.martie@ibm.com", "~Lior_Horesh1", "~Mingyi_Hong1" ]	OpenReview API	Current deep neural networks are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training, has been shown to mitigate the negative impact of adversarial attac...	Reject	null	4	[ { "id": "kqHMI0j7Aoi", "reviewer_signature": [ "ICLR.cc/2021/Conference/Paper2142/AnonReviewer2" ], "rating": "5: Marginally below acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct", "recommendation": "", "s...	https://openreview.net/forum?id=kmBFHJ5pr0o	2206.06257	papers/kmBFHJ5pr0o.pdf	479976bacaec51c73c97c58763ee6dad612254263f15ecbb62e51c0093883df0	647,562	openreview	https://github.com/dat-2022/dat	dat-2022/dat	3fac9f60fcf0213b14cc991b3c1a9b2cf26415ba	repos/kmBFHJ5pr0o.zip	ce7430d997a23c756e61aae1bb1a46b4ffbe2e2e9856edc3deb76f58df45d848	15,125	10	{ ".py": 10 }	12	{ "Python": 47403 }	false	2022-07-31T16:33:09	{ "method": "exact_normalized_title", "pwc_official": true, "pwc_mentioned_in_paper": true, "pwc_url": "https://paperswithcode.com/paper/distributed-adversarial-training-to-robustify-1" }	{ "paper_reviews_decision": "OpenReview API", "paper_code_mapping": "Papers With Code archive", "repository_snapshot": "GitHub API commit-pinned ZIP" }
HylNWkHtvB	2,020	rejected	Domain-Independent Dominance of Adaptive Methods	[ "Pedro Savarese", "David McAllester", "Sudarshan Babu", "Michael Maire" ]	[ "savarese@ttic.edu", "mcallester@ttic.edu", "sudarshan@ttic.edu", "mmaire@uchicago.edu" ]	OpenReview API	"From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperform(...TRUNCATED)	Reject	null	3	[{"id":"rklCY7VJ9r","reviewer_signature":["ICLR.cc/2020/Conference/Paper1540/AnonReviewer4"],"rating(...TRUNCATED)	https://openreview.net/forum?id=HylNWkHtvB	1912.01823	papers/HylNWkHtvB.pdf	69ee064c63d05aba1b8e8f60d445c7df6ebbd405434135b70034287016301d43	355,911	openreview	https://github.com/lolemacs/avagrad	lolemacs/avagrad	343733151f26a1fa8e504079c6d7934a6038a93b	repos/HylNWkHtvB.zip	4ae059a3b716f559ead8828fa2c4a0006b88eebdacaceb4066dc60c5be20255d	8,780	5	{ ".py": 5 }	10	{ "Python": 26004 }	false	2020-12-15T04:01:22	{"method":"exact_normalized_title","pwc_official":true,"pwc_mentioned_in_paper":true,"pwc_url":"http(...TRUNCATED)	{"paper_reviews_decision":"OpenReview API","paper_code_mapping":"Papers With Code archive","reposito(...TRUNCATED)
S1E64jC5tm	2,019	rejected	The Forward-Backward Embedding of Directed Graphs	[ "Thomas Bonald", "Nathan De Lara" ]	[ "thomas.bonald@telecom-paristech.fr", "nathan.delara@telecom-paristech.fr" ]	OpenReview API	"We introduce a novel embedding of directed graphs derived from the singular value decomposition (SV(...TRUNCATED)	null	Reject	3	[{"id":"SkxVfLSzaX","reviewer_signature":["ICLR.cc/2019/Conference/Paper43/AnonReviewer2"],"rating":(...TRUNCATED)	https://openreview.net/forum?id=S1E64jC5tm	null	papers/S1E64jC5tm.pdf	8544849615a647b537158912f5832b63bf3b6415d01ddb6c4f9f951d494c8c04	316,920	openreview	https://github.com/tbonald/directed	tbonald/directed	2d4b979f296bb0750ee1560ac07d70ff68f7c8dc	repos/S1E64jC5tm.zip	da3639ca90e6775705e265ab6256cb2cf997f0b08f0b91f7b7c6fb5455efc695	13,245	4	{ ".py": 3, ".ipynb": 1 }	17	{ "Python": 21945, "Jupyter Notebook": 17282 }	false	2018-11-08T09:38:07	{"method":"exact_normalized_title","pwc_official":true,"pwc_mentioned_in_paper":true,"pwc_url":"http(...TRUNCATED)	{"paper_reviews_decision":"OpenReview API","paper_code_mapping":"Papers With Code archive","reposito(...TRUNCATED)
S1EwLkW0W	2,018	rejected	Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients	[ "Lukas Balles", "Philipp Hennig" ]	[ "lukas.balles@tuebingen.mpg.de", "ph@tue.mpg.de" ]	OpenReview API	"The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well,(...TRUNCATED)	Reject	null	3	[{"id":"S1urbvOgf","reviewer_signature":["ICLR.cc/2018/Conference/Paper482/AnonReviewer1"],"rating":(...TRUNCATED)	https://openreview.net/forum?id=S1EwLkW0W	1705.07774	papers/S1EwLkW0W.pdf	2c820b68da0c383b2903af5ebb1dbdd750ebd91c21a5918648a4350b627fe321	656,781	openreview	https://github.com/lballes/msvag	lballes/msvag	d2d467b6a9c6442d781ecb2f1d4b5c5769556363	repos/S1EwLkW0W.zip	cec7a33afea60c29789a2ee5ae6350da0f16dec1de490ba4dcf2a99486c1f23b	8,966	3	{ ".py": 3 }	12	{ "Python": 10920 }	false	2018-05-11T14:21:18	{"method":"exact_normalized_title","pwc_official":true,"pwc_mentioned_in_paper":true,"pwc_url":"http(...TRUNCATED)	{"paper_reviews_decision":"OpenReview API","paper_code_mapping":"Papers With Code archive","reposito(...TRUNCATED)
JtX6oaaJ2d	2,026	rejected	Improving LLM Unlearning Robustness via Random Perturbations	["Dang Huu-Tien","Hoang Thanh-Tung","Anh Tuan Bui","Phuong Minh Nguyen","Le-Minh Nguyen","Naoya Inou(...TRUNCATED)	["~Dang_Huu-Tien1","~Hoang_Thanh-Tung1","~Anh_Tuan_Bui2","~Phuong_Minh_Nguyen3","~Le-Minh_Nguyen1","(...TRUNCATED)	OpenReview API	"Here, we show that current state-of-the-art LLM unlearning methods inherently reduce models' robust(...TRUNCATED)	Reject		4	[{"id":"Y7wia56zs2","reviewer_signature":["ICLR.cc/2026/Conference/Submission1191/Reviewer_pZfT"],"r(...TRUNCATED)	https://openreview.net/forum?id=JtX6oaaJ2d	2501.19202	papers/JtX6oaaJ2d.pdf	047638bbcecfff54980a5e5c942fd1cddb60e523ef9129df9c8399213c0fa4d7	7,340,032	openreview	https://github.com/RebelsNLU-jaist/llmu-robustness	RebelsNLU-jaist/llmu-robustness	cdda05906725da2f6c1b7d5792eee7d780811541	repos/JtX6oaaJ2d.zip	2dbe319e41d72dbf0ff8662f7b10e0361be5d485a9612997ec042818202674a3	25,397	14	{ ".py": 8, ".sh": 6 }	16	{ "Python": 48251, "Shell": 6200 }	false	2026-06-02T05:43:33	{"method":"exact_normalized_title","pwc_official":true,"pwc_mentioned_in_paper":true,"pwc_url":"http(...TRUNCATED)	{"paper_reviews_decision":"OpenReview API","paper_code_mapping":"Papers With Code archive","reposito(...TRUNCATED)
22ywev7zMt	2,025	rejected	On the Out-of-Distribution Generalization of Self-Supervised Learning	[ "Wenwen Qiang", "Jingyao Wang", "Zeen Song", "Jiangmeng Li", "Changwen Zheng" ]	[ "~Wenwen_Qiang1", "~Jingyao_Wang1", "~Zeen_Song1", "~Jiangmeng_Li1", "~Changwen_Zheng1" ]	OpenReview API	"In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning(...TRUNCATED)	Reject		3	[{"id":"GcA3e5dnN5","reviewer_signature":["ICLR.cc/2025/Conference/Submission700/Reviewer_M58z"],"ra(...TRUNCATED)	https://openreview.net/forum?id=22ywev7zMt	2505.16675	papers/22ywev7zMt.pdf	80507b1b63d95900be2c420895a12d96d9270abac2fa06b894015b0b6cb03d1f	2,564,459	openreview	https://github.com/ML-TASA/PID-SSL	ML-TASA/PID-SSL	095b2be7fcfe206cbf2a103039a0529d12968846	repos/22ywev7zMt.zip	fd1087b1fecbe86c4aeab52c00f3c95107d8f1fb6b59b21d37b5b15360c84b76	7,332	3	{ ".py": 3 }	10	{ "Python": 18876 }	false	2025-06-04T08:40:48	{"method":"exact_normalized_title","pwc_official":true,"pwc_mentioned_in_paper":true,"pwc_url":"http(...TRUNCATED)	{"paper_reviews_decision":"OpenReview API","paper_code_mapping":"Papers With Code archive","reposito(...TRUNCATED)

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Rejected ICLR Papers with Reviews and Code

This dataset contains 1,000 rejected ICLR submissions from 2018–2026. Each row has the OpenReview submission metadata and reviews, the rejected submission PDF, and a commit-pinned archive of a matched public GitHub repository.

This collection was built directly from OpenReview. It does not use a third-party ICLR review dataset.

Project repository: TheAppliedScientist

1,000 unique rejected OpenReview submissions
1,000 unique canonical GitHub repositories
3,740 complete official-review records
1,000 PDFs downloaded from immutable OpenReview attachment paths
1,000 commit-pinned GitHub repository ZIP archives
SHA-256 checksums for every PDF and repository archive

ICLR year	Papers
2018	30
2019	56
2020	104
2021	138
2022	137
2023	137
2024	138
2025	136
2026	124

Files

dataset.jsonl: full records, including all review content
manifest.jsonl: lightweight records without the large reviews arrays
report.json: final integrity and distribution report
papers/<forum>.pdf: OpenReview submission PDFs
repos/<forum>.zip: commit-pinned GitHub source archives

Paths in each row are relative to the dataset repository.

Example

from datasets import load_dataset

full = load_dataset(
    "Vidushee/iclr-rejected-papers-with-code-1k",
    "full",
)
manifest = load_dataset(
    "Vidushee/iclr-rejected-papers-with-code-1k",
    "manifest",
)

Licensing and responsible use

This repository is an aggregation of materials with mixed upstream terms. It does not relicense individual papers, reviews, or code repositories. Paper and review rights remain governed by OpenReview and the authors' terms; source-code archives retain the licenses (if any) specified by their upstream repositories; and the Papers With Code mapping archive is CC BY-SA 4.0. Users are responsible for checking the applicable upstream license before redistributing or modifying an individual artifact.

Use the OpenReview forum URL and GitHub repository URL stored in each row when citing individual works.

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Rejected ICLR Papers with Reviews and Code

Contents

Files

Example

Licensing and responsible use