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Plamen P Angelov

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3 papers

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TMLR Journal 2026 Journal Article

Pull-to-Outlier & Contrastive Objective-level (POCO) Unlearning: A Framework for Sample and Objective Forgetting

Agil Aghasanli
Plamen P Angelov

Current Machine Unlearning (MU) methods require full retraining or extensive fine-tuning, lack formal removal criteria, and focus only on sample-level forgetting, limiting their practicality. We address these gaps with two lightweight, projection-only techniques operating above frozen feature extractors. Pull-to-Outlier Unlearning (POU) offers a transparent, unsupervised geometric removal method by displacing embeddings of unwanted samples or entire classes into synthetic outlier regions, while preserving downstream performance and distilling knowledge of the remaining data. To the best of our knowledge, Contrastive Objective-level Unlearning (COU) is the first method to remove learned objectives. It perturbs projection weights to eliminate a target task’s influence. Then it realigns the original data manifold, which can provide the possibility for managing agentic learning behaviors. We validate POU on CIFAR10, CIFAR100, and Caltech-256 with ResNet-based backbones, showing efficient instance and class forgetting with minimal impact on retained accuracy. COU is tested on DINO and CLIP feature representations, demonstrating effective objective-level erasure while preserving all non-target tasks.

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AAAI Conference 2025 Conference Paper

Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement

Yi Li
Yang Sun
Plamen P Angelov

In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phase in two diffusion networks. Throughout the diffusion process, noise clips from real-world noise interferences are added gradually to the clean speech spectra and a noise-aware reverse process is proposed to learn how to generate both clean speech spectra and noise spectra. Furthermore, to fully leverage the intrinsic relationship between magnitude and phase, we introduce a complex-cycle-consistent (CCC) mechanism that uses the estimated magnitude to map the phase, and vice versa. We implement this algorithm within a phase-aware speech enhancement diffusion model (SEDM). We conduct extensive experiments on public datasets to demonstrate the effectiveness of our method, highlighting the significant benefits of exploiting the intrinsic relationship between phase and magnitude information to enhance speech. The comparison to conventional diffusion models demonstrates the superiority of SEDM.

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TMLR Journal 2025 Journal Article

Recursive SNE: Fast Prototype-Based t-SNE for Large-Scale and Online Data

Agil Aghasanli
Plamen P Angelov

Dimensionality reduction techniques like t-SNE excel at visualizing structure in high-dimensional data but incur high computational costs that limit their use on large or streaming datasets. We introduce the Recursive SNE (RSNE) framework, which extends t-SNE with two complementary strategies: i-RSNE for real-time, point-wise updates and Bi-RSNE for efficient batch processing. Across diverse settings, including standard image benchmarks (CIFAR10/CIFAR100) with DINOv2 and CLIP features, domain-specific iROADS road scenes, neuroimaging data from the Haxby fMRI dataset, and long-term climate records, RSNE delivers substantial speedups over Barnes–Hut t-SNE while maintaining or even improving cluster separability. By combining a lightweight prototype-based initialization with localized KL-divergence refinements, RSNE offers a scalable and adaptable framework for both large-scale offline embedding and on-the-fly visualization of streaming data.

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