Huan-Hsin Tseng

VLDB 2026 Research / reviewers in the wild / expert

dblp:201/7281 · DBLP profile ↗

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12ranked-venue papers

2as first author

11since 2021 · last 2025

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 8 · 1 first-author · 7 since 2021Artificial intelligence and machine learning · 4 · 1 first-author · 4 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Year	Publication	Venue	Position
2025	Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators abstract Particle accelerators are composed of various components, and their properties are finely tuned to optimize certain particle beam qualities as they accelerate. In particular, particle colliders like the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) are interested in maximizing luminosity, a measure of the collision rate primarily determined by the beam intensity (number of particles) and its beam size. However, finding and maintaining optimum settings is a time-consuming expert operator activity. This work proposes the use of the Recurrent Proximal Policy Optimization (RPPO), a reinforcement learning algorithm, to find parameters of quadrupole magnet strengths optimizing the beam qualities in the Booster to AGS (Alternating Gradient Synchrotron) section of the RHIC complex. Thilina Balasooriya, Shinjae Yoo, Vincent Schoefer, Huan-Hsin Tseng, Yuan Gao 0035, Weijian Lin, Chanaka De Silva	ICASSP	4
2024	Federated Quantum Machine Learning with Differential Privacy abstract The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices. Rod Rofougaran, Shinjae Yoo, Huan-Hsin Tseng, Samuel Yen-Chi Chen	ICASSP	3
2024	Quantum Federated Learning with Quantum Networks abstract A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have been previously proposed, which would enable fast and completely secure online communications. Previous work has yielded a hybrid quantum-classical transfer learning scheme for classical data and communication with a hub-spoke topology. While quantum communication is secure from eavesdrop attacks and no measurements from quantum to classical translation, due to no cloning theorem, hub-spoke topology is not ideal for quantum communication without quantum memory. Here we seek to improve this model by implementing a decentralized ring topology for the federated learning scheme, where each client is given a portion of the entire dataset and only performs training on that set. We also demonstrate the first successful use of quantum weights for quantum federated learning, which allows us to perform our training entirely in quantum. Tyler Wang, Huan-Hsin Tseng, Shinjae Yoo	ICASSP	2
2024	Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy Preserving Quantum Machine Learning abstract The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple distributed teachers are trained on disjoint data sets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML). William M. Watkins, Heehwan Wang, Sangyoon Bae, Huan-Hsin Tseng, Jiook Cha, Samuel Yen-Chi Chen, Shinjae Yoo	ICASSP	4
2024	Utilizing a Hybrid Matrix Product State and Variational Quantum Circuit Architecture for the Detection of Kidney Diseases abstract Computed tomography (CT) scans are widely used for diagnosing and monitoring renal diseases but have a misdiagnosis rate of nearly 30%. Neural networks, particularly when integrated with quantum architectures, can significantly reduce this rate. This study introduces a Hybrid Matrix Product State (MPS) and Variational Quantum Circuit (VQC) architecture, where MPS is used for feature extraction and VQC for classification. The MPS-VQC model demonstrated enhanced performance, achieving 99.49% accuracy in distinguishing between kidney cysts, tumors, and healthy scans, compared to 52.92% using Principal Component Analysis (PCA)-VQC. As medical imaging increasingly adopts machine learning, quantum-inspired architectures like MPS-VQC offer a promising solution for improving diagnostic accuracy in renal disease detection and can be adapted for broader medical imaging tasks to enhance clinical outcomes and efficiency. Vidur Reddy Jannapureddy, Shinjae Yoo, Huan-Hsin Tseng	ICMLA	3
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	115
2024	INSURE: An Information Theory iNspired diSentanglement and pURification modEl for Domain Generalization abstract Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG methods have focused on extracting domain-invariant features, the domain-specific class-relevant features have attracted attention and been argued to benefit generalization to the unseen target domain. To take into account the class-relevant domain-specific information, in this paper we propose an Information theory iNspired diSentanglement and pURification modEl (INSURE) to explicitly disentangle the latent features to obtain sufficient and compact (necessary) class-relevant feature for generalization to the unseen domain. Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information. We further propose a paired purification loss function to let the auxiliary feature discard all the class-relevant information and thus the class-relevant feature will contain sufficient and compact (necessary) class-relevant information. Moreover, instead of using multiple encoders, we propose to use a learnable binary mask as our disentangler to make the disentanglement more efficient and make the disentangled features complementary to each other. We conduct extensive experiments on five widely used DG benchmark datasets including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE achieves state-of-the-art performance. We also empirically show that domain-specific class-relevant features are beneficial for domain generalization. The code is available at https://github.com/yuxi120407/INSURE. Huan-Hsin Tseng, Shinjae Yoo, Haibin Ling, Yuewei Lin	IEEE Trans. Image Process.	2
2023	On the Robustness of Non-Intrusive Speech Quality Model by Adversarial Examples abstract It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although network models have the potential to be a surrogate for complex human hearing perception, they may contain instabilities in predictions. This work shows that deep speech quality predictors can be vulnerable to adversarial perturbations, where the prediction can be changed drastically by unnoticeable perturbations as small as −30 dB compared with speech inputs. In addition to exposing the vulnerability of deep speech quality predictors, we further explore and confirm the viability of adversarial training for strengthening robustness of models. Hsin-Yi Lin, Huan-Hsin Tseng, Yu Tsao 0001	ICASSP	2
2023	Interpretations of Domain Adaptations via Layer Variational Analysis Huan-Hsin Tseng, Hsin-Yi Lin, Kuo-Hsuan Hung, Yu Tsao 0001	ICLR	1
2022	Boosting Self-Supervised Embeddings for Speech Enhancement abstract Self-supervised learning (SSL) representation for speech has achieved state-of-the-art (SOTA) performance on several downstream tasks.However, there remains room for improvement in speech enhancement (SE) tasks.In this study, we used a crossdomain feature to solve the problem that SSL embeddings may lack fine-grained information to regenerate speech signals.By integrating the SSL representation and spectrogram, the result can be significantly boosted.We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.Consequently, we found that SSL representations with lower noise robustness are more important.Furthermore, our experiments on the VCTK-DEMAND dataset demonstrated that fine-tuning an SSL representation with an SE model can outperform the SOTA SSL-based SE methods in PESQ, CSIG and COVL without invoking complicated network architectures.In later experiments, the CN distance in SSL embeddings was observed to increase after fine-tuning.These results verify our expectations and may help design SE-related SSL training in the future. Kuo-Hsuan Hung, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Tien Chiang, Yu Tsao 0001, Chii-Wann Lin	INTERSPEECH	3
2021	Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport abstract This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean references of noisy speech in a target domain, by exploiting the knowledge available from the source domain. The domain shift between training and testing data has been reported to be an obstacle to learning problems in diverse fields. Although rich literature exists on unsupervised domain adaptation for classification, the methods proposed, especially in regressions, remain scarce and often depend on additional information regarding the input data. The proposed DOTN approach tactically fuses the optimal transport (OT) theory from mathematical analysis with generative adversarial frameworks, to help evaluate continuous labels in the target domain. The experimental results on two SE tasks demonstrate that by extending the classical OT formulation, our proposed DOTN outperforms previous adversarial domain adaptation frameworks in a purely unsupervised manner. Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao 0001	NeurIPS	2
2017	Power-law stochastic neighbor embedding abstract Stochastic neighbor embedding (SNE) aims to transform the observations in high-dimensional space into a low-dimensional space which preserves neighbor identities by minimizing the Kullback-Leibler divergence of the pairwise distributions between two spaces where Gaussian distributions are assumed. Data visualization could be improved by adopting the t-SNE where Student t distribution is used in the low-dimensional space. However, data pairs in the latent space are forced to be squeezed due to the loss of dimensions. This study incorporates the power-law distribution into construction of the p-SNE. Such an unsupervised p-SNE increases the physical forces in neighbor embedding so that the neighbors in the low-dimensional space can be adjusted flexibly to reflect the neighboring in the high-dimensional space. The experiments on three learning tasks illustrate that the manifold or data structure using the proposed p-SNE is preserved in better shape than that using SNE and t-SNE. Huan-Hsin Tseng, Issam El-Naqa, Jen-Tzung Chien	ICASSP	1