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portfolio
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publications
Meta Continual Learning on Graphs with Experience Replay
Published in Transactions on Machine Learning Research, 2023
Continual learning is a machine learning approach where the challenge is that a constructed learning model executes incoming tasks while maintaining its performance over the earlier tasks. In order to address this issue, we devise a technique that combines two uniquely important concepts in machine learning, namely “replay buffer” and “meta learning”, aiming to exploit the best of two worlds. In this method, the model weights are initially computed by using the current task dataset. Next, the dataset of the current task is merged with the stored samples from the earlier tasks and the model weights are updated using the combined dataset. This aids in preventing the model weights converging to the optimal parameters of the current task and enables the preservation of information from earlier tasks. We choose to adapt our technique to graph data structure and the task of node classification on graphs. We introduce MetaCLGraph, which outperforms the baseline methods over various graph datasets including Citeseer, Corafull, Arxiv, and Reddit. This method illustrates the potential of combining replay buffer and meta learning in the field of continual learning on graphs.
Recommended citation: Unal, A., Akgül, A., Kandemir, M., & Unal, G. Meta Continual Learning on Graphs with Experience Replay. Transactions on Machine Learning Research.
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A Study Regarding Machine Unlearning on Facial Attribute Data
Published in 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), 2024
Machine learning (ML) models require large amounts of data and many of the stored data is used to train ML models. However, the ML models learn insights about the data during their training and this raises privacy concerns of the individuals regarding personal data. These concerns led to the introduction of legislation focusing on the “right to be forgotten” and machine unlearning has emerged to address these concerns. Although machine unlearning studies focus on data privacy issues generally, machine unlearning is also used to fix the mistrained machine learning models as well. Mistraining may occur due to problems in the data such as mislabeling. Machine unlearning can solve this problem by discarding the information regarding the problematic data. In this study, the effects of machine unlearning on facial attribute classification are discovered. Experimental results on CelebA dataset show the effectiveness of machine unlearning methods. The code repository can be accessed at https://github.com/ituvisionlab/face-attribute-unlearning.
Recommended citation: Gündoğdu, E., Unal, A., & Unal, G. (2024, May). A Study Regarding Machine Unlearning on Facial Attribute Data. In 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) (pp. 1-5). IEEE.
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Deep Neural Repression Collapse
Published in Third Conference on Parsimony and Learning (CPAL 2026), 2026
Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We show that in the collapsed layers of neural regression models, features lie in a subspace that corresponds to the target dimension, the feature covariance aligns with the target covariance, the input subspace of the layer weights aligns with the feature subspace, and the linear prediction error of the features is close to the overall prediction error of the model. In addition to establishing Deep NRC, we also show that models that exhibit Deep NRC learn the intrinsic dimension of low rank targets and explore the necessity of weight decay in inducing Deep NRC. This paper provides a more complete picture of the simple structure learned by deep networks in the context of regression.
Recommended citation: Rangamani, A., Unal, A. (2026, March). Deep Neural Regression Collapse. In Third Conference on Parsimony and Learning (CPAL 2026).
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An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
Published in 29th International Conference on Artificial Intelligence and Statistics (AISTATS) 2026, 2026
While numerous machine unlearning (MU) methods have recently been developed with promising results in erasing the influence of forgotten data, classes, or concepts, they are also highly vulnerable-for example, simple fine-tuning can inadvertently reintroduce erased concepts. In this paper, we address this contradiction by examining the internal representations of unlearned models, in contrast to prior work that focuses primarily on output-level behavior. Our analysis shows that many state-of-the-art MU methods appear successful mainly due to a misalignment between last-layer features and the classifier, a phenomenon we call feature-classifier misalignment. In fact, hidden features remain highly discriminative, and simple linear probing can recover near-original accuracy. Assuming neural collapse in the original model, we further demonstrate that adjusting only the classifier can achieve negligible forget accuracy while preserving retain accuracy, and we corroborate this with experiments using classifier-only fine-tuning. Motivated by these findings, we propose MU methods based on a class-mean features (CMF) classifier, which explicitly enforces alignment between features and classifiers. Experiments on standard benchmarks show that CMF-based unlearning reduces forgotten information in representations while maintaining high retain accuracy, highlighting the need for faithful representation-level evaluation of MU.
Recommended citation: Gao, Y., Unal, A., Rangamani, A., & Zhu, Z. (2026). An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations. arXiv preprint arXiv:2604.08271.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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