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Haolin Yang

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning

Yichen Li
Hang Su
Huifa Li
Haolin Yang
Xinlin Zhuang
Haochen Xue
Haozhao Wang
Imran Razzak

Knowledge Distillation (KD) serves as an effective approach to addressing heterogeneity issues in Federated Learning (FL), leveraging additional datasets to align local and global models better. There are two primary distillation paradigms: feature-based distillation, which utilizes intermediate-layer features of the network, and logit-based distillation, which employs the final layer's logit outputs. However, existing studies often select distillation methods based on intuitive and empirical evidence when facing different heterogeneous settings, neglecting the intrinsic relationship between distillation paradigms and heterogeneity. This oversight may result in suboptimal federated knowledge distillation performance under heterogeneous conditions. In this paper, we propose the Consolidated Distillation for Heterogeneous Federated Learning - FedCD that balances knowledge representations from both feature-based and logit-based distillation to enhance performance. Specifically, to address the misalignment between knowledge conveyed by features and logits, we aggregate features from different layers via cross-layer attention to preserve semantic knowledge, followed by distribution modeling using Gaussian Mixture Models. This process strengthens knowledge distillation by constraining the transformation of different network layers' features under a consolidated distribution, thereby mitigating impacts from both data and model heterogeneity. Extensive experiments demonstrate that FedCD outperforms state-of-the-art methods by over 10.72% and validate the effectiveness of our approach.

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ICML Conference 2025 Conference Paper

Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport

Yuguang Yan
Zongyu Li
Haolin Yang
Zeqin Yang
Hao Zhou
Ruichu Cai
Zhifeng Hao

Causal inference seeks to estimate the effect given a treatment such as a medicine or the dosage of a medication. To reduce the confounding bias caused by the non-randomized treatment assignment, most existing methods reduce the shift between subpopulations receiving different treatments. However, these methods split limited training samples into smaller groups, which cuts down the number of samples in each group, while precise distribution estimation and alignment highly rely on a sufficient number of training samples. In this paper, we propose a distribution alignment paradigm without data splitting, which can be naturally applied in the settings of binary and continuous treatments. To this end, we characterize the confounding bias by considering different probability measures of the same set including all the training samples, and exploit the optimal transport theory to analyze the confounding bias and outcome estimation error. Based on this, we propose to learn balanced representations by reducing the bias between the marginal distribution and the conditional distribution of a treatment. As a result, data reduction caused by splitting is avoided, and the outcome prediction model trained on one treatment group can be generalized to the entire population. The experiments on both binary and continuous treatment settings demonstrate the effectiveness of our method.

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NeurIPS Conference 2025 Conference Paper

Unifying Attention Heads and Task Vectors via Hidden State Geometry in In-Context Learning

Haolin Yang
Hakaze Cho
Yiqiao Zhong
Naoya Inoue

The unusual properties of in-context learning (ICL) have prompted investigations into the internal mechanisms of large language models. Prior work typically focuses on either special attention heads or task vectors at specific layers, but lacks a unified framework linking these components to the evolution of hidden states across layers that ultimately produce the model’s output. In this paper, we propose such a framework for ICL in classification tasks by analyzing two geometric factors that govern performance: the separability and alignment of query hidden states. A fine-grained analysis of layer-wise dynamics reveals a striking two-stage mechanism—separability emerges in early layers, while alignment develops in later layers. Ablation studies further show that Previous Token Heads drive separability, while Induction Heads and task vectors enhance alignment. Our findings thus bridge the gap between attention heads and task vectors, offering a unified account of ICL’s underlying mechanisms.

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NeurIPS Conference 2025 Conference Paper

UniViT: Unifying Image and Video Understanding in One Vision Encoder

Feilong Tang
xiangan xiangan
Haolin Yang
Yin Xie
Kaicheng Yang
Ming Hu
Zheng Cheng
Xingyu Zhou

Despite the impressive progress of recent pretraining methods on multimodal tasks, existing methods are inherently biased towards either spatial modeling (e. g. , CLIP) or temporal modeling (e. g. , V-JEPA), limiting their joint capture of spatial details and temporal dynamics. To this end, we propose UniViT, a cluster-driven unified self-supervised learning framework that effectively captures the structured semantics of both image spatial content and video temporal dynamics through event-level and object-level clustering and discrimination. Specifically, we leverage offline clustering to generate semantic clusters across both modalities. For videos, multi-granularity event-level clustering progressively expands from single-event to structured multi-event segments, capturing coarse-to-fine temporal semantics; for images, object-level clustering captures fine-grained spatial semantics. However, while global clustering provides semantically consistent clusters, it lacks modeling of structured semantic relations (e. g. , temporal event structures). To address this, we introduce a contrastive objective that leverages these semantic clusters as pseudo-label supervision to explicitly enforce structural constraints, including temporal event relations and spatial object co-occurrences, capturing structured semantics beyond categories. Meanwhile, UniViT jointly embeds structured object-level and event-level semantics into a unified representation space. Furthermore, UniViT introduces two key components: (i) Unified Rotary Position Embedding integrates relative positional embedding with frequency-aware dimension allocation to support position-invariant semantic learning and enhance the stability of structured semantics in the discrimination stage; and (ii) Variable Spatiotemporal Streams adapt to inputs of varying frame lengths, addressing the rigidity of conventional fixed-input approaches. Extensive experiments across varying model scales demonstrate that UniViT achieves state-of-the-art performance on linear probing, attentive probing, question answering, and spatial understanding tasks.

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IJCAI Conference 2024 Conference Paper

Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation

Haolin Yang
Chaoqiang Zhao
Lu Sheng
Yang Tang

Nighttime self-supervised monocular depth estimation has received increasing attention in recent years. However, using night images for self-supervision is unreliable because the photometric consistency assumption is usually violated in the videos taken under complex lighting conditions. Even with domain adaptation or photometric loss repair, performance is still limited by the poor supervision of night images on trainable networks. In this paper, we propose a self-supervised nighttime monocular depth estimation method that does not use any night images during training. Our framework utilizes day images as a stable source for self-supervision and applies physical priors (e. g. , wave optics, reflection model and read-shot noise model) to compensate for some key day-night differences. With day-to-night data distribution compensation, our framework can be trained in an efficient one-stage self-supervised manner. Though no nighttime images are considered during training, qualitative and quantitative results demonstrate that our method achieves SoTA depth estimating results on the challenging nuScenes-Night and RobotCar-Night compared with existing methods.

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