Latest Semi-Supervised Learning Research Papers

The newest Semi-Supervised Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Semi-Supervised Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
  Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan · arXiv (Cornell University) · Jun 3, 2026

  Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Semi-supervised learning with max-margin graph cuts
  Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang · arXiv · Apr 29, 2026

  This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to exist…

• F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
  Yutong Shen, Ruizhe Xia, Jingyi Liu, Yinqi Liu · arXiv · Apr 22, 2026

  Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by significant computational overhead and reliance on strong homophily assumptions. Traditi…

• VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
  Jean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang et al. · arXiv · Apr 21, 2026

  We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines…

• Class-aware temporal and contextual contrastive framework for semi-supervised automated fault detection and diagnosis in air handling units
  Seunghyeon Wang · Energy and Buildings · Feb 25, 2026

  Automated Fault Detection and Diagnosis (AFDD) for Air Handling Units (AHUs) has largely relied on supervised learning, which is difficult to deploy when labeled data are scarce and fault classes are imbalanced. Existing label-efficient AFD…

• In Context Semi-Supervised Learning
  Jiashuo Fan, Paul Rosu, Aaron T Wang, Lawrence Carin et al. · ICLR 2026 Poster · Jan 26, 2026

  There has been significant recent interest on understanding the capacity of Transformers for in-context learning (ICL), yet most theory focuses on supervised settings with explicitly labeled pairs. In practice, Transformers often perform we…

• Active semi-supervised learning for multi-target regression
  Maira Farias Andrade Lira, Luisa Cavalcante, Celine Vens, Ricardo Prudencio et al. · BNAIC/BeNeLearn 2025 Oral · Oct 15, 2025

  Recent works have proposed the combination of active and semi-supervised learning techniques to efficiently incorporate unlabeled data. The so-called active semi-supervised learning (ASSL) investigates methods to efficiently construct predi…

• Semi-Supervised Contrastive Learning with Orthonormal Prototypes
  Huanran Li, Manh Nguyen, Daniel L. Pimentel-Alarcón · Submitted to ICLR 2026 · Sep 19, 2025

  Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. However, dimensional collapse, where embeddings converge into …

• Continuous Partitioning for Graph-Based Semi-Supervised Learning
  Chester Holtz, Pengwen Chen, Zhengchao Wan, Chung-Kuan Cheng et al. · NeurIPS 2024 poster · Sep 25, 2024

  Laplace learning algorithms for graph-based semi-supervised learning have been shown to produce degenerate predictions at low label rates and in imbalanced class regimes, particularly near class boundaries. We propose CutSSL: a framework fo…

• Reinforcement Learning Guided Semi-Supervised Learning
  Marzi Heidari, Hanping Zhang, Yuhong Guo · NeurIPS 2024 poster · Sep 25, 2024

  In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL …

• Semi-Supervised One Shot Imitation Learning
  Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du, Igor Mordatch et al. · RLC 2024 · May 15, 2024

  One-shot Imitation Learning (OSIL) aims to imbue AI agents with the ability to learn a new task from a single demonstration. To supervise the learning, OSIL requires a prohibitively large number of paired expert demonstrations: trajectories…

• Semi-supervised Learning under Self-training via $f$-Divergence
  Gholamali Aminian, Amirhossein Bagheri, Radmehr Karimian, Mahyar JafariNodeh et al. · Tiny Papers @ ICLR 2024 Archive · Mar 19, 2024

  This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspiration from $f$-divergences. In the pseudo-labeling and entrop…

• A Unified Framework for Heterogeneous Semi-supervised Learning
  Marzi Heidari, Abdullah Alchihabi, Hao Yan, Yuhong Guo · Submitted to ICLR 2024 · Sep 22, 2023

  In this work, we introduce a novel problem setup termed as Heterogeneous Semi- Supervised Learning (HSSL), which presents unique challenges by bridging the semi-supervised learning (SSL) task and the unsupervised domain adaptation (UDA) tas…

• Self-Supervision is Not All You Need: In Defense of Semi-Supervised Learning
  Rohit Gupta, Mamshad Nayeem Rizve, Swetha Sirnam, Navid Kardan et al. · Submitted to ICLR 2024 · Sep 22, 2023

  Self-supervised (Self-SL) and Semi-supervised learning (Semi-SL) are two dominant approaches in limited label representation learning. Recent advances in Self-SL demonstrate its importance as a pretraining step to initialize the model with …

• Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations
  Huiqiao Fu, Kaiqiang Tang, Yuanyang Lu, Yiming Qi et al. · NeurIPS 2023 poster · Sep 21, 2023

  Imitation learning aims to reproduce expert behaviors without relying on an explicit reward signal. However, real-world demonstrations often present challenges, such as multi-modal, data imbalance, and expensive labeling processes. In this …

• Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
  Jiachen Liang, RuiBing Hou, Hong Chang, Bingpeng Ma et al. · NeurIPS 2023 poster · Sep 21, 2023

  Traditional semi-supervised learning (SSL) assumes that the feature distributions of labeled and unlabeled data are consistent which rarely holds in realistic scenarios. In this paper, we propose a novel SSL setting, where unlabeled sample…

• OTMatch: Improving Semi-Supervised Learning with Optimal Transport
  Zhiquan Tan, Kaipeng Zheng, Weiran Huang · Submitted to ICLR 2024 · Sep 18, 2023

  Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning i…

• Learning Label Refinement and Thresholds for Imbalanced Semi-Supervised Learning
  Zeju Li, Ying-Qiu Akina Zheng, Saad Jbabdi · Submitted to ICLR 2024 · Sep 18, 2023

  Semi-supervised learning (SSL) has proven to be effective in enhancing generalization when working with limited labeled training data. Existing SSL algorithms based on pseudo-labels rely on heuristic strategies or uncalibrated model confide…

• RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
  Yifan Zhang, Jingqin Yang, Zhiquan Tan, Yang Yuan · Submitted to ICLR 2024 · Sep 16, 2023

  Semi-supervised learning has gained prominence for its ability to utilize limited labeled data alongside abundant unlabeled data. However, prevailing algorithms often neglect the relationships among data points within a batch, focusing inst…

• Semi-Supervised Learning Using Semi-Definite Programming
  Tijl De Bie, Nello Cristianini · Semi-Supervised Learning 2006 · Jan 1, 2006