Author name cluster

Xin Yin

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

SepPrune: Structured Pruning for Efficient Deep Speech Separation

Yuqi Li
Kai Li
Xin Yin
Zhifei Yang
Zeyu Dong
Zhengtao Yao
Haoyan Xu
Yingli Tian

Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low-latency speech processing in real-time applications. In this paper, we propose SepPrune, the first structured pruning framework specifically designed to compress deep speech separation models and reduce their computational cost. SepPrune begins by analyzing the computational structure of a given model to identify layers with the highest computational burden. It then introduces a differentiable masking strategy to enable gradient-driven channel selection. Based on the learned masks, SepPrune prunes redundant channels and fine-tunes the remaining parameters to recover performance. Extensive experiments demonstrate that this learnable pruning paradigm yields substantial advantages for channel pruning in speech separation models, outperforming existing methods. Notably, a model pruned with SepPrune can recover 85% of the performance of a pre-trained model (trained over hundreds of epochs) with only one epoch of fine-tuning, and achieves convergence 36x faster than training from scratch.

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IROS Conference 2025 Conference Paper

Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning

Yunchang Ma
Tenglong Liu
Yixing Lan
Xin Yin
Changxin Zhang
Xinglong Zhang
Xin Xu 0001

In offline reinforcement learning, value overestimation caused by out-of-distribution (OOD) actions significantly limits policy performance. Recently, diffusion models have been leveraged for their strong distribution-matching capabilities, enforcing conservatism through behavior policy constraints. However, existing methods often apply indiscriminate regularization to redundant actions in low-quality datasets, resulting in excessive conservatism and an imbalance between the expressiveness and efficiency of diffusion modeling. To address these issues, we propose DIffusion policies with Value-conditional Optimization (DIVO), a novel approach that leverages diffusion models to generate high-quality, broadly covered in-distribution state-action samples while facilitating efficient policy improvement. Specifically, DIVO introduces a binary-weighted mechanism that utilizes the advantage values of actions in the offline dataset to guide diffusion model training. This enables a more precise alignment with the dataset’s distribution while selectively expanding the boundaries of high-advantage actions. During policy improvement, DIVO dynamically filters high-return-potential actions from the diffusion model, effectively guiding the learned policy toward better performance. This approach achieves a critical balance between conservatism and explorability in offline RL. We evaluate DIVO on the D4RL benchmark and compare it against state-of-the-art baselines. Empirical results demonstrate that DIVO achieves superior performance, delivering significant improvements in average returns across locomotion tasks and outperforming existing methods in the challenging AntMaze domain, where sparse rewards pose a major difficulty.

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IROS Conference 2025 Conference Paper

Learning Predictive Control with Online Modeling for Agile Maneuvering of Autonomous Vehicles

Xin Yin
Zengyi Zhang
Haotian Cao
Tenglong Liu
Yixing Lan
Xin Xu 0001
Xinglong Zhang

The agile maneuvering control of autonomous vehicles (AVs) requires the tracking of reference trajectories characterized by high acceleration, sharp curvature, considerable disturbances, and significant time-varying, all while ensuring stability and accuracy. The inherent uncertainty and time-varying nature of both the vehicle model and its environment pose significant challenges to achieving high-performance tracking during agile maneuvers. Developing a control algorithm that enables solving the optimal policy for nonlinear systems with uncertainties is critical. In this paper, we propose a learning-based predictive control approach, namely, an adaptive model predictive control (AMPC) with Actor-Critic Learning (ACL) for generating closed-loop MPC policies for agile maneuvering of AVs. The proposed approach leverages neural networks to model the dynamics uncertainties online. The control policy and model are updated simultaneously to realize performance op-timization under time-varying uncertainties. Simulation results demonstrate that our proposed algorithm outperforms other leading ACL methods, as well as MPC and Linear Quadratic Regulator (LQR). Furthermore, field test experiment results validate its effectiveness on the HongQi-EHS3 electric vehicle, showing superior control performance compared to MPC both on paved roads and curved off-roads with excellent stability performance.

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TCS Journal 2024 Journal Article

A secure hierarchical deterministic wallet with stealth address from lattices

Xin Yin
Zhen Liu

The concept of Hierarchical Deterministic Wallet (HDW) was introduced by Wuille in Bitcoin Improvement Proposal 32 (BIP32). HDW enables an individual/organization to generate cryptographic keys and subsequently ease the key management problems (e. g. , backup and recovery). Since the first HDW algorithm in 2012, HDW has gradually shown its fit for many promising use cases, such as Bitcoin-like cryptocurrencies, global key revocation in FIDO2 standard. In order to achieve all the features (i. e. , deterministic derivation, master public key and hierarchy) and the security (i. e. , safety of cryptocurrencies and privacy protection of users) requirements for HDW, Yin et al. (ESORICS 2022) conceptualized Hierarchical Deterministic Wallet supporting Stealth Address (HDWSA), and gave a provably secure construction from the standard Computational Diffie-Hellman Assumption. Unfortunately, the construction is not quantum-resistant. In this work, we propose the first HDWSA construction from lattices to fill this gap, we provide the security proof for the construction in the random oracle model (ROM) based on hard problems over lattices. Compared with existing works, to the best of our knowledge, our construction not only captures all the HDW features and security properties, but also provides the potential quantum resistance.

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IJCAI Conference 2016 Conference Paper

Online Bayesian Max-Margin Subspace Multi-View Learning

Jia He
Changying Du
Fuzhen Zhuang
Xin Yin
Qing He
Guoping Long

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

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