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Xiaowen Chu

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12 papers

1 author row

AAAI Conference 2026 Conference Paper

SALR: Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models

Longteng Zhang
Sen Wu
Shuai Hou
Zhengyu Qing
Zhuo Zheng
Danning Ke
Qihong Lin
Qiang Wang

Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments. Low-rank Adaptation (LoRA) reduces trainable parameters by factorizing weight updates, yet the underlying dense weights still impose high storage and computation costs. Magnitude-based pruning can yield sparse models but typically degrades LoRA’s performance when applied naively. In this paper, we introduce SALR (Sparsity-Aware Low-Rank Representation), a novel fine-tuning paradigm that unifies low-rank adaptation with sparse pruning under a rigorous mean-squared-error framework. We prove that statically pruning only the frozen base weights minimizes the pruning error bound, and we recover the discarded residual information via a truncated-SVD low-rank adapter, which provably reduces per-entry MSE by a factor of (1 - r/min(d, k)). To maximize hardware efficiency, we fuse multiple low-rank adapters into a single concatenated GEMM, and we adopt a bitmap-based encoding with a two-stage pipelined decoding + GEMM design to achieve true model compression and speedup. Empirically, SALR attains 50% sparsity on various LLMs while matching the performance of LoRA on GSM8K and MMLU, reduces model size by 2x, and delivers up to a 1.7x inference speedup.

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NeurIPS Conference 2025 Conference Paper

ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference

Xiang Liu
Zhenheng Tang
Peijie Dong
Zeyu Li
Bo Li
Xuming Hu
Xiaowen Chu

Large Language Models (LLMs) require significant GPU memory when processing long texts, with the key value (KV) cache consuming up to 70\% of total memory during inference. Although existing compression methods reduce memory by evaluating the importance of individual tokens, they overlook critical semantic relationships between tokens, resulting in fragmented context and degraded performance. We introduce \method{}, which fundamentally reimagines KV cache compression by treating semantic chunks - rather than isolated tokens - as basic compression units. This approach preserves complete linguistic structures and contextual integrity, ensuring that essential meaning is retained even under aggressive compression. Our innovation includes a novel layer-wise index reuse technique that exploits the higher cross-layer similarity of preserved indices in \method{}, reducing computational overhead and improving throughput by 26. 5\%. Comprehensive evaluations on challenging benchmarks: LongBench, Needle-In-A-HayStack, GSM8K, and JailbreakV demonstrate that \method{} outperforms state-of-the-art methods by up to 8. 7\% in precision while maintaining the same compression ratio. These results confirm that semantic-aware compression significantly enhances both efficiency and performance for long-context LLM inference, providing a simple yet effective solution to the memory bottleneck problem. \emph{The code is available at \href{https: //github. com/NVIDIA/kvpress}{link}. }

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AAAI Conference 2025 Conference Paper

ParZC: Parametric Zero-Cost Proxies for Efficient NAS

Peijie Dong
Lujun Li
Zhenheng Tang
Xiang Liu
Zimian Wei
Qiang Wang
Xiaowen Chu

Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the ability of zero-cost proxies in identifying superior architecture. However, we identify a critical issue with current zero-cost proxies: they aggregate node-wise zero-cost statistics without considering that not all nodes in a neural network equally impact performance estimation. Our observations reveal that node-wise zero-cost statistics significantly vary in their contributions to performance, with each node exhibiting a degree of uncertainty. Based on this insight, we introduce a novel method called Parametric Zero-Cost Proxies (ParZC) framework to enhance the adaptability of zero-cost proxies through parameterization. To address the node indiscrimination, we propose a Mixer Architecture with Bayesian Network (MABN) to explore the node-wise zero-cost statistics and estimate node-specific uncertainty. Moreover, we propose DiffKendall as a loss function to improve ranking consistency. Comprehensive experiments on NAS-Bench-101, 201, and NDS demonstrate the superiority of our proposed ParZC compared to existing zero-shot NAS methods. Additionally, we demonstrate the versatility and adaptability of ParZC on Vision Transformer search space.

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AAAI Conference 2024 Conference Paper

CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning

Qingsong Yan
Qiang Wang
Kaiyong Zhao
Jie Chen
Bo Li
Xiaowen Chu
Fei Deng

Neural Radiance Fields have demonstrated impressive performance in novel view synthesis. However, NeRF and most of its variants still rely on traditional complex pipelines to provide extrinsic and intrinsic camera parameters, such as COLMAP. Recent works, like NeRFmm, BARF, and L2G-NeRF, directly treat camera parameters as learnable and estimate them through differential volume rendering. However, these methods work for forward-looking scenes with slight motions and fail to tackle the rotation scenario in practice. To overcome this limitation, we propose a novel camera parameter free neural radiance field (CF-NeRF), which incrementally reconstructs 3D representations and recovers the camera parameters inspired by incremental structure from motion. Given a sequence of images, CF-NeRF estimates camera parameters of images one by one and reconstructs the scene through initialization, implicit localization, and implicit optimization. To evaluate our method, we use a challenging real-world dataset, NeRFBuster, which provides 12 scenes under complex trajectories. Results demonstrate that CF-NeRF is robust to rotation and achieves state-of-the-art results without providing prior information and constraints.

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NeurIPS Conference 2024 Conference Paper

Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models

Lujun Li
Peijie Dong
Zhenheng Tang
Xiang Liu
Qiang Wang
Wenhan Luo
Wei Xue
Qifeng Liu

In this paper, we present DSA, the first automated framework for discovering sparsity allocation schemes for layer-wise pruning in Large Language Models (LLMs). LLMs have become increasingly powerful, but their large parameter counts make them computationally expensive. Existing pruning methods for compressing LLMs primarily focus on evaluating redundancies and removing element-wise weights. However, these methods fail to allocate adaptive layer-wise sparsities, leading to performance degradation in challenging tasks. We observe that per-layer importance statistics can serve as allocation indications, but their effectiveness depends on the allocation function between layers. To address this issue, we develop an expression discovery framework to explore potential allocation strategies. Our allocation functions involve two steps: reducing element-wise metrics to per-layer importance scores, and modelling layer importance to sparsity ratios. To search for the most effective allocation function, we construct a search space consisting of pre-process, reduction, transform, and post-process operations. We leverage an evolutionary algorithm to perform crossover and mutation on superior candidates within the population, guided by performance evaluation. Finally, we seamlessly integrate our discovered functions into various uniform methods, resulting in significant performance improvements. We conduct extensive experiments on multiple challenging tasks such as arithmetic, knowledge reasoning, and multimodal benchmarks spanning GSM8K, MMLU, SQA, and VQA, demonstrating that our DSA method achieves significant performance gains on the LLaMA-1|2|3, Mistral, and OPT models. Notably, the LLaMA-1|2|3 model pruned by our DSA reaches 4. 73\%|6. 18\%|10. 65\% gain over the state-of-the-art techniques (e. g. , Wanda and SparseGPT).

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NeurIPS Conference 2024 Conference Paper

FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion

Zhenheng Tang
Yonggang Zhang
Peijie Dong
Yiu-ming Cheung
Amelie C. Zhou
Bo Han
Xiaowen Chu

One-shot Federated Learning (OFL) significantly reduces communication costs in FL by aggregating trained models only once. However, the performance of advanced OFL methods is far behind the normal FL. In this work, we provide a causal view to find that this performance drop of OFL methods comes from the isolation problem, which means that local isolatedly trained models in OFL may easily fit to spurious correlations due to the data heterogeneity. From the causal perspective, we observe that the spurious fitting can be alleviated by augmenting intermediate features from other clients. Built upon our observation, we propose a novel learning approach to endow OFL with superb performance and low communication and storage costs, termed as FuseFL. Specifically, FuseFL decomposes neural networks into several blocks, and progressively trains and fuses each block following a bottom-up manner for feature augmentation, introducing no additional communication costs. Comprehensive experiments demonstrate that FuseFL outperforms existing OFL and ensemble FL by a significant margin. We conduct comprehensive experiments to show that FuseFL supports high scalability of clients, heterogeneous model training, and low memory costs. Our work is the first attempt using causality to analyze and alleviate data heterogeneity of OFL.

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NeurIPS Conference 2024 Conference Paper

Should We Really Edit Language Models? On the Evaluation of Edited Language Models

Qi Li
Xiang Liu
Zhenheng Tang
Peijie Dong
Zeyu Li
Xinglin Pan
Xiaowen Chu

Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization, and locality, with many methods excelling across these criteria. Some recent works disclose the pitfalls of these editing methods such as knowledge distortion or conflict. However, the general abilities of post-edited language models remain unexplored. In this paper, we perform a comprehensive evaluation on various editing methods and different language models, and have following findings. (1) Existing editing methods lead to inevitable performance deterioration on general benchmarks, indicating that existing editing methods maintain the general abilities of the model within only a few dozen edits. When the number of edits is slightly large, the intrinsic knowledge structure of the model is disrupted or even completely damaged. (2) Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing. (3) Language model with large scale is more resistant to editing compared to small model. (4) The safety of the edited model, is significantly weakened, even for those safety-aligned models. Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models, which motivates further research on more practical and reliable editing methods.

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AAAI Conference 2023 Conference Paper

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

Xin He
Jiangchao Yao
Yuxin Wang
Zhenheng Tang
Ka Chun Cheung
Simon See
Bo Han
Xiaowen Chu

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subnets. Subsequent explorations decompose supernet weights via a particular criterion, e.g., gradient matching, to reduce the interference; yet they suffer from huge computational cost and low space separability. In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. NAS-LID evaluates the geometrical properties of architectures by calculating the low-cost LID features layer-by-layer, and the similarity characterized by LID enjoys better separability compared with gradients, which thus effectively reduces the interference among subnets. Extensive experiments on NASBench-201 indicate that NAS-LID achieves superior performance with better efficiency. Specifically, compared to the gradient-driven method, NAS-LID can save up to 86% of GPU memory overhead when searching on NASBench-201. We also demonstrate the effectiveness of NAS-LID on ProxylessNAS and OFA spaces. Source code:https://github.com/marsggbo/NAS-LID.

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AAAI Conference 2023 Conference Paper

Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity

Qingsong Yan
Qiang Wang
Kaiyong Zhao
Bo Li
Xiaowen Chu
Fei Deng

Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this problem, we propose a disparity-based MVS method based on the epipolar disparity flow (E-flow), called DispMVS, which infers the depth information from the pixel movement between two views. The core of DispMVS is to construct a 2D cost volume on the image plane along the epipolar line between each pair (between the reference image and several source images) for pixel matching and fuse uncountable depths triangulated from each pair by multi-view geometry to ensure multi-view consistency. To be robust, DispMVS starts from a randomly initialized depth map and iteratively refines the depth map with the help of the coarse-to-fine strategy. Experiments on DTUMVS and Tanks\&Temple datasets show that DispMVS is not sensitive to the depth range and achieves state-of-the-art results with lower GPU memory.

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AAAI Conference 2021 Conference Paper

Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans

Xin He
Shihao Wang
Xiaowen Chu
Shaohuai Shi
Jiangping Tang
Xin Liu
Chenggang Yan
Jiyong Zhang

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people’s lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3 18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID- 19 datasets to provide interpretability for medical diagnosis. Code: https: //github. com/HKBU-HPML/CovidNet3D.

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IJCAI Conference 2019 Conference Paper

A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification

Shaohuai Shi
Kaiyong Zhao
Qiang Wang
Zhenheng Tang
Xiaowen Chu

Gradient sparsification is a promising technique to significantly reduce the communication overhead in decentralized synchronous stochastic gradient descent (S-SGD) algorithms. Yet, many existing gradient sparsification schemes (e. g. , Top-k sparsification) have a communication complexity of O(kP), where k is the number of selected gradients by each worker and P is the number of workers. Recently, the gTop-k sparsification scheme has been proposed to reduce the communication complexity from O(kP) to O(k logP), which significantly boosts the system scalability. However, it remains unclear whether the gTop-k sparsification scheme can converge in theory. In this paper, we first provide theoretical proofs on the convergence of the gTop-k scheme for non-convex objective functions under certain analytic assumptions. We then derive the convergence rate of gTop-k S-SGD, which is at the same order as the vanilla mini-batch SGD. Finally, we conduct extensive experiments on different machine learning models and data sets to verify the soundness of the assumptions and theoretical results, and discuss the impact of the compression ratio on the convergence performance.

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TCS Journal 2010 Journal Article

Mechanism design for set cover games with selfish element agents

Xiang-Yang Li
Zheng Sun
Weizhao Wang
Xiaowen Chu
Shaojie Tang
Ping Xu

In this article, we study the set cover games when the elements are selfish agents, each of which has a privately known valuation of receiving the service from the sets, i. e. , being covered by some set. Each set is assumed to have a fixed cost. We develop several approximately efficient strategyproof mechanisms that decide, after soliciting the declared bids by all elements, which elements will be covered, which sets will provide the coverage to these selected elements, and how much each element will be charged. For single-cover set cover games, we present a mechanism that is at least 1 d max -efficient, i. e. , the total valuation of all selected elements is at least 1 d max fraction of the total valuation produced by any mechanism. Here, d max is the maximum size of the sets. For multi-cover set cover games, we present a budget-balanced strategyproof mechanism that is 1 d max H d max -efficient under reasonable assumptions. Here, H n is the harmonic function. For the set cover games when both sets and elements are selfish agents, we show that a cross-monotonic payment-sharing scheme does not necessarily induce a strategyproof mechanism.

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