More Like this

1. Contrastive Unlearning: A Contrastive Approach to Machine Unlearning

   https://doi.org/10.24963/ijcai.2025/830  

   Lee, Hong kyu; Zhang, Qiuchen; Yang, Carl; Lou, Jian; Xiong, Li (September 2025, International Joint Conferences on Artificial Intelligence Organization)

   Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance is challenging. Existing works mainly exploit input and output space and classification loss, which can result in ineffective unlearning or performance loss. In addition, they utilize unlearning or remaining samples ineffectively, sacrificing either unlearning efficacy or efficiency. Our main insight is that the direct optimization on the representation space utilizing both unlearning and remaining samples can effectively remove influence of unlearning samples while maintaining representations learned from remaining samples. We propose a contrastive unlearning framework, leveraging the concept of representation learning for more effective unlearning. It removes the influence of unlearning samples by contrasting their embeddings against the remaining samples' embeddings so that their embeddings are closer to the embeddings of unseen samples. Experiments on a variety of datasets and models on both class unlearning and sample unlearning showed that contrastive unlearning achieves the best unlearning effects and efficiency with the lowest performance loss compared with the state-of-the-art algorithms. In addition, it is generalizable to different contrastive frameworks and other models such as vision-language models. Our main code is available on github.com/Emory-AIMS/Contrastive-Unlearning 

   more » « less
2. Enhancing Contrastive Representation Learning through Data

   Zhang, Honggen (May 2025, ProQuest Dissertation)

   Contrastive learning learns input representation by pushing similar data together and pulling dissimilar data away, along with data augmentation and pretext task construction. It enhances the large model learning due to its ability to use a large amount of unlabeled data. It has been suc- cessfully applied to large language models, pre-trained image models, and multimodal models. In addition, contrastive learning learns a representation from modeling the explainable structure of the latent space, which has a broad application in scientific discovery and interpretable Artificial Intelligence (AI). The primary focus of this thesis is to explore contrastive learning from a data construction perspective in real-world problems to fill the gap between the principle of contrastive learning and its application. The challenges, such as sampling bias and data quality, will largely affect the representations learned by contrastive learning. This thesis analyzes the data construction chanlledges and limitations in 1) the negative sampling of knowledge graph embedding (KGE), 2) high-quliaty preference data labeling of Large Language Models (LLMs) alignment, 3) data augmentation in Non-linear dynamic system modeling, and 4) data properties in functions of mesange RNA (mRNA) sequence. To solve the challenges 1), a hardness and structure-based objective function was proposed by considering sampling bias in hard negative sampling. For challenge 2), the similarity of response embedding is used to evaluate the quality of preference pairs to mitigate the labeling error of humans when they face an ambiguous response pair. Chal- lenge 3) is solved by systematically considering the physical system and contrastive learning. A data augmentation strategy by partitioning the full sequence is used for learning the transition matrix in the latent linear space. Challenge 4) is common to see in the biological domain due to the high cost of lab experiments. Pre-trained model will advantage the limited dataset su- pervised learning by learning general features from domain knowledge. A contrastive learning based teacher-student framework is proposed for mRNA sequence learning by contrasting the unmasked sequence and the hard-masked sequence. By providing careful data construction or data sampling, contrastive learning will be boosted to solve tasks in reality. For the KGE, the novel contrastive loss function learns the boundary between negative samples and positive samples to improve the link prediction task in the knowl- edge graph; For the LLM alignment, in the same labeling cost, the selected dissimilar responses will improve the vanilla direct preference optimization (DPO) alignment; The data augmentation with contrastive loss play crucial role to learn more accuracy dynamic system, which explained by the learned the continiues eigenfunction; By considering the tearch-student framework with hard-masked strategy, the pre-trained model achieve the state-of-the-art result by fine-tuning on limited downstrame task data. Overall, this thesis provides a broad data-driven contrastive learning methodology to enhance representation learning in different domains. The methodology consists of a imprived objective function in the face of data bias, a better data selection reducing labeling error, and proper data augmentation for a particular application domain. This methodology improve the learning result compare to traditional method. 

   more » « less
3. Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training

   Meng, Jian; Yang, Li; Lee, Kyungmin; Shin, Jinwoo; Fan, Deliang; Seo, Jae-sun (December 2023, Thirty-seventh Conference on Neural Information Processing Systems)

   Contrastive learning (CL) has been widely investigated with various learning mech- anisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing large-sized encoders to achieve comparable performance as the supervised learning counterpart. Despite the success of the labelless training, current contrastive learning algorithms failed to achieve good performance with lightweight (compact) models, e.g., MobileNet, while the re- quirements of the heavy encoders impede the energy-efficient computation, espe- cially for resource-constrained AI applications. Motivated by this, we propose a new self-supervised CL scheme, named SACL-XD, consisting of two technical components, Slimmed Asymmetrical Contrastive Learning (SACL) and Cross- Distillation (XD), which collectively enable efficient CL with compact models. While relevant prior works employed a strong pre-trained model as the teacher of unsupervised knowledge distillation to a lightweight encoder, our proposed method trains CL models from scratch and outperforms them even without such an expensive requirement. Compared to the SoTA lightweight CL training (dis- tillation) algorithms, SACL-XD achieves 1.79% ImageNet-1K accuracy improve- ment on MobileNet-V3 with 64⇥ training FLOPs reduction. Code is available at https://github.com/mengjian0502/SACL-XD. 

   more » « less
4. Understanding Dynamics of Nonlinear Representation Learning and Its Application

   https://doi.org/10.1162/neco_a_01483  

   Kawaguchi, Kenji; Zhang, Linjun; Deng, Zhun (March 2022, Neural Computation)

   Abstract Representations of the world environment play a crucial role in artificial intelligence. It is often inefficient to conduct reasoning and inference directly in the space of raw sensory representations, such as pixel values of images. Representation learning allows us to automatically discover suitable representations from raw sensory data. For example, given raw sensory data, a deep neural network learns nonlinear representations at its hidden layers, which are subsequently used for classification (or regression) at its output layer. This happens implicitly during training through minimizing a supervised or unsupervised loss. In this letter, we study the dynamics of such implicit nonlinear representation learning. We identify a pair of a new assumption and a novel condition, called the on-model structure assumption and the data architecture alignment condition. Under the on-model structure assumption, the data architecture alignment condition is shown to be sufficient for the global convergence and necessary for global optimality. Moreover, our theory explains how and when increasing network size does and does not improve the training behaviors in the practical regime. Our results provide practical guidance for designing a model structure; for example, the on-model structure assumption can be used as a justification for using a particular model structure instead of others. As an application, we then derive a new training framework, which satisfies the data architecture alignment condition without assuming it by automatically modifying any given training algorithm dependent on data and architecture. Given a standard training algorithm, the framework running its modified version is empirically shown to maintain competitive (practical) test performances while providing global convergence guarantees for deep residual neural networks with convolutions, skip connections, and batch normalization with standard benchmark data sets, including MNIST, CIFAR-10, CIFAR-100, Semeion, KMNIST, and SVHN. 

   more » « less
5. Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

   Babakniya, Sara; Fabian, Zalan; He, Chaoyang; Soltanolkotabi, Mahdi; Avestimehr, Salman (December 2023, Proceedings of Neural Information Processing Systems)

   Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines. 

   more » « less