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Xiangyu Chang

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

Bayes-Optimal Fair Classification with Multiple Sensitive Features

Yi Yang
Yinghui Huang
Xiangyu Chang

Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive features. In this paper, we characterize the Bayes-optimal fair classifier for multiple sensitive features under general approximate fairness measures, including *mean difference* (MD) and *mean ratio* (MR). We show that these approximate measures for existing group fairness notions, including Demographic Parity, Equal Opportunity, Predictive Equality, and Accuracy Parity, are linear transformations of selection rates for specific groups defined by both labels and sensitive features. We then characterize that Bayes-optimal fair classifiers for multiple sensitive features under both MD and MR become instance-dependent thresholding rules that rely on a weighted sum of these group membership probabilities. Our framework applies to both attribute-aware and attribute-blind settings and can accommodate composite fairness notions like Equalized Odds. Building on this, we propose two practical algorithms for Bayes-optimal fair classification via in-processing and post-processing. We show empirically that our methods compare favorably to existing methods.

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AAAI Conference 2026 Conference Paper

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack

Jing Xue
Zhishen Sun
Haishan Ye
Luo Luo
Xiangyu Chang
Guang Dai

Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. This attack aims to determine whether a specific sample is part of the model's training set by analyzing the model's output. While traditional membership inference attacks focus on leveraging the model’s posterior output, such as confidence on the target sample, we propose IMIA, a novel attack strategy that utilizes the process of generating adversarial samples to infer membership. We propose to infer the member properties of the target sample using the number of iterations required to generate its adversarial sample. We conduct experiments across multiple models and datasets, and our results demonstrate that the number of iterations for generating an adversarial sample is a reliable feature for membership inference, achieving strong performance both in black-box and white-box attack scenarios. This work provides a new perspective for evaluating model privacy and highlights the potential of adversarial example-based features for privacy leakage assessment.

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JMLR Journal 2025 Journal Article

Optimal Decentralized Composite Optimization for Strongly Convex Functions

Haishan Ye
Xiangyu Chang

This paper concentrates on decentralized composite optimization for strongly convex functions. Specifically, we first study the case where each local objective function $f_i(x)$ held by agent $i$ is $L$-smooth and convex, while the regularization term $g(x)$ is $\mu$-strongly convex. For this problem class, we propose the first decentralized algorithm that simultaneously achieves the optimal computation and communication complexities. Furthermore, we extend our algorithm to two broader scenarios. In the first extension, when each $f_i(x)$ is $L$-smooth and $\mu$-strongly convex while $g(x)$ is merely convex, our algorithm continues to attain the optimal complexities. In the second extension, we show that under time-varying communication networks, our algorithm matches the lower bounds on decentralized optimization established in Kovalev et al. (2021). Finally, extensive experiments validate both the computational and communication efficiency of the proposed algorithms. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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ICLR Conference 2025 Conference Paper

ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs

Hao Di
Tong He
Haishan Ye
Yinghui Huang 0001
Xiangyu Chang
Guang Dai
Ivor W. Tsang

As large language models (LLMs) are increasingly being integrated into various real-world applications, the identification of their vulnerabilities to jailbreaking attacks becomes an essential component of ensuring the safety and reliability of LLMs. Previous studies have developed LLM assistants, known as the adversarial prompter, to automatically generate suffixes that manipulate target LLMs into generating harmful and undesirable outputs. However, these approaches often suffer from low performance or generate semantically meaningless prompts, which can be easily identified by perplexity-based defenses. In this paper, we introduce a novel two-stage method, $\texttt{ProAdvPrompter}$, that significantly improves the performance of adversarial prompters. In $\texttt{ProAdvPrompter}$, the first stage (Exploration) utilizes the loss information to guide the adversarial prompter in generating suffixes that are more likely to elicit harmful responses. Then the second stage (Exploitation) iteratively fine-tunes the prompter using high-quality generated adversarial suffixes to further boost performance. Additionally, we incorporate the prompt template to aid in the Exploration stage and propose a filtering mechanism to accelerate the training process in the Exploitation stage. We evaluate $\texttt{ProAdvPrompter}$ against the well-aligned LLMs (i.e., Llama2-Chat-7B and Llama3-chat-8B), achieving attack success rates of 99.68% and 97.12% respectively after 10 trials on the AdvBench dataset, thereby enhancing performance by $\sim 2$ times compared to previous works. Moreover, $\texttt{ProAdvPrompter}$ reduces training time by 20% on Llama3-Instruct-8B, generates more generalized adversarial suffixes, and demonstrates resilience against the perplexity defense. An ablation study further evaluates the effects of key components in $\texttt{ProAdvPrompter}$ (the prompt template and the filtering mechanism).

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NeurIPS Conference 2025 Conference Paper

When and How Unlabeled Data Provably Improve In-Context Learning

Yingcong Li
Xiangyu Chang
Muti Kara
Xiaofeng Liu
Amit Roy-Chowdhury
Samet Oymak

Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a canonical setting where the demonstrations are drawn according to a binary Gaussian mixture model (GMM) and a certain fraction of the demonstrations have missing labels. We provide a comprehensive theoretical study to show that: (1) The loss landscape of one-layer linear attention models recover the optimal fully-supervised estimator but completely fail to exploit unlabeled data; (2) In contrast, multilayer or looped transformers can effectively leverage unlabeled data by implicitly constructing estimators of the form $\sum_{i\ge 0} a_i (X^\top X)^iX^\top y$ with $X$ and $y$ denoting features and partially-observed labels (with missing entries set to zero). We characterize the class of polynomials that can be expressed as a function of depth and draw connections to Expectation Maximization, an iterative pseudo-labeling algorithm commonly used in semi-supervised learning. Importantly, the leading polynomial power is exponential in depth, so mild amount of depth/looping suffices. As an application of theory, we propose looping off-the-shelf tabular foundation models to enhance their semi-supervision capabilities. Extensive evaluations on real-world datasets show that our method significantly improves the semisupervised tabular learning performance over the standard single pass inference.

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NeurIPS Conference 2024 Conference Paper

CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions

Sk M. Ahmed
Fahim F. Niloy
Xiangyu Chang
Dripta S. Raychaudhuri
Samet Oymak
Amit K. Roy-Chowdhury

Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different models to transfer knowledge to evolving data distributions. However, this approach faces difficulties when the dynamic test distribution is available only in small batches and without access to the original source data. To address the challenge of adapting to dynamic distributions in such practical settings, we propose continual multi-source adaptation to dynamic distributions (CONTRAST), a novel method that optimally combines multiple source models to adapt to the dynamic test data. CONTRAST has two distinguishing features. First, it efficiently computes the optimal combination weights to combine the source models to adapt to the test data distribution continuously as a function of time. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of ``forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Through theoretical analysis we show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting. Experimental analysis on diverse datasets demonstrates that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting).

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ICML Conference 2024 Conference Paper

Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods

Hao Di
Haishan Ye
Xiangyu Chang
Guang Dai
Ivor W. Tsang

In decentralized optimization, $m$ agents form a network and only communicate with their neighbors, which gives advantages in data ownership, privacy, and scalability. At the same time, decentralized stochastic gradient descent ($\texttt{SGD}$) methods, as popular decentralized algorithms for training large-scale machine learning models, have shown their superiority over centralized counterparts. Distributed stochastic gradient tracking $\texttt{DSGT}$ has been recognized as the popular and state-of-the-art decentralized $\texttt{SGD}$ method due to its proper theoretical guarantees. However, the theoretical analysis of $\texttt{DSGT}$ shows that its iteration complexity is $\tilde{\mathcal{O}} \left(\frac{\bar{\sigma}^2}{m\mu \varepsilon} + \frac{\sqrt{L}\bar{\sigma}}{\mu(1 - \lambda_2(W))^{1/2} C_W \sqrt{\varepsilon} }\right)$, where the doubly stochastic matrix $W$ represents the network topology and $ C_W $ is a parameter that depends on $W$. Thus, it indicates that the convergence property of $\texttt{DSGT}$ is heavily affected by the topology of the communication network. To overcome the weakness of $\texttt{DSGT}$, we resort to the snap-shot gradient tracking skill and propose two novel algorithms, snap-shot $\texttt{DSGT}$ ($\texttt{SS-DSGT}$) and accelerated snap-shot $\texttt{DSGT}$ ($\texttt{ASS-DSGT}$). We further justify that $\texttt{SS-DSGT}$ exhibits a lower iteration complexity compared to $\texttt{DSGT}$ in the general communication network topology. Additionally, $\texttt{ASS-DSGT}$ matches $\texttt{DSGT}$’s iteration complexity $\mathcal{O}\left( \frac{\bar{\sigma}^2}{m\mu \varepsilon} + \frac{\sqrt{L}\bar{\sigma}}{\mu (1 - \lambda_2(W))^{1/2}\sqrt{\varepsilon}} \right)$ under the same conditions as $\texttt{DSGT}$. Numerical experiments validate $\texttt{SS-DSGT}$’s superior performance performance in the general communication network topology and exhibit better practical performance of $\texttt{ASS-DSGT}$ on the specified $W$ compared to $\texttt{DSGT}$.

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ICML Conference 2024 Conference Paper

Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient

Hao Di
Haishan Ye
Yueling Zhang
Xiangyu Chang
Guang Dai
Ivor W. Tsang

Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods. However, in composite optimization problems, ZO methods encounter an additional variance called the coordinate-wise variance, which stems from the random gradient estimation. To reduce this variance, prior works require estimating all partial derivatives, essentially approximating FO information. This approach demands $\mathcal{O}(d)$ function evaluations ($d$ is the dimension size), which incurs substantial computational costs and is prohibitive in high-dimensional scenarios. This paper proposes the Zeroth-order Proximal Double Variance Reduction ($\texttt{ZPDVR}$) method, which utilizes the averaging trick to reduce both sampling and coordinate-wise variances. Compared to prior methods, $\texttt{ZPDVR}$ relies solely on random gradient estimates, calls the stochastic zeroth-order oracle (SZO) in expectation $\mathcal{O}(1)$ times per iteration, and achieves the optimal $\mathcal{O}(d(n + \kappa)\log (\frac{1}{\epsilon}))$ SZO query complexity in the strongly convex and smooth setting, where $\kappa$ represents the condition number and $\epsilon$ is the desired accuracy. Empirical results validate $\texttt{ZPDVR}$’s linear convergence and demonstrate its superior performance over other related methods.

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NeurIPS Conference 2024 Conference Paper

Selective Attention: Enhancing Transformer through Principled Context Control

Xuechen Zhang
Xiangyu Chang
Mingchen Li
Amit Roy-Chowdhury
Jiasi Chen
Samet Oymak

The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed major success, it notably treats all queries $q$ in the same way by applying the mapping $V^\top\text{softmax}(Kq)$, where $V, K$ are the value and key embeddings respectively. In this work, we argue that this uniform treatment hinders the ability to control contextual sparsity and relevance. As a solution, we introduce the Selective Self-Attention (SSA) layer that augments the softmax nonlinearity with a principled temperature scaling strategy. By controlling temperature, SSA adapts the contextual sparsity of the attention map to the query embedding and its position in the context window. Through theory and experiments, we demonstrate that this alleviates attention dilution, aids the optimization process, and enhances the model's ability to control softmax spikiness of individual queries. We also incorporate temperature scaling for value embeddings and show that it boosts the model's ability to suppress irrelevant/noisy tokens. Notably, SSA is a lightweight method which introduces less than 0. 5\% new parameters through a weight-sharing strategy and can be fine-tuned on existing LLMs. Extensive empirical evaluations demonstrate that SSA-equipped models achieve a noticeable and consistent accuracy improvement on language modeling benchmarks.

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ICML Conference 2023 Conference Paper

2D-Shapley: A Framework for Fragmented Data Valuation

Zhihong Liu
Hoang Anh Just
Xiangyu Chang
Xi Chen
Ruoxi Jia 0001

Data valuation—quantifying the contribution of individual data sources to certain predictive behaviors of a model—is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.

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JMLR Journal 2023 Journal Article

Randomized Spectral Co-Clustering for Large-Scale Directed Networks

Xiao Guo
Yixuan Qiu
Hai Zhang
Xiangyu Chang

Directed networks are broadly used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. In particular, the well-known spectral clustering algorithm could be modified as the spectral co-clustering to co-cluster directed networks. However, large-scale networks pose great computational challenges to it. In this paper, we leverage sketching techniques and derive two randomized spectral co-clustering algorithms, one random-projection-based and the other random-sampling-based, to accelerate the co-clustering of large-scale directed networks. We theoretically analyze the resulting algorithms under two generative models – the stochastic co-block model and the degree-corrected stochastic co-block model, and establish their approximation error rates and misclustering error rates, indicating better bounds than the state-of-the-art results of co-clustering literature. Numerically, we design and conduct simulations to support our theoretical results and test the efficiency of the algorithms on real networks with up to millions of nodes. A publicly available R package RandClust is developed for better usability and reproducibility of the proposed methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2021 Conference Paper

Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks

Xiangyu Chang
Yingcong Li
Samet Oymak
Christos Thrampoulidis

Deep networks are typically trained with many more parameters than the size of the training dataset. Recent empirical evidence indicates that the practice of overparameterization not only benefits training large models, but also assists – perhaps counterintuitively – building lightweight models. Specifically, it suggests that overparameterization benefits model pruning / sparsification. This paper sheds light on these empirical findings by theoretically characterizing the highdimensional asymptotics of model pruning in the overparameterized regime. The theory presented addresses the following core question: “should one train a small model from the beginning, or first train a large model and then prune? ”. We analytically identify regimes in which, even if the location of the most informative features is known, we are better off fitting a large model and then pruning rather than simply training with the known informative features. This leads to a new double descent in the training of sparse models: growing the original model, while preserving the target sparsity, improves the test accuracy as one moves beyond the overparameterization threshold. Our analysis further reveals the benefit of retraining by relating it to feature correlations. We find that the above phenomena are already present in linear and randomfeatures models. Our technical approach advances the toolset of high-dimensional analysis and precisely characterizes the asymptotic distribution of over-parameterized least-squares. The intuition gained by analytically studying simpler models is numerically verified on neural networks.

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AAAI Conference 2018 Short Paper

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

Aven Samareh
Yan Jin
Zhangyang Wang
Xiangyu Chang
Shuai Huang

We present our preliminary work to determine if patient’s vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8). We proposed a multi-modal fusion model that combines three different modalities: audio, video, and text features. By training over the AVEC2017 dataset, our proposed model outperforms each single-modality prediction model, and surpasses the dataset baseline with a nice margin.

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JMLR Journal 2017 Journal Article

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang
Shao-Bo Lin
Ding-Xuan Zhou

This paper provides error analysis for distributed semi- supervised learning with kernel ridge regression (DSKRR) based on a divide-and-conquer strategy. DSKRR applies kernel ridge regression (KRR) to data subsets that are distributively stored on multiple servers to produce individual output functions, and then takes a weighted average of the individual output functions as a final estimator. Using a novel error decomposition which divides the generalization error of DSKRR into the approximation error, sample error and distributed error, we find that the sample error and distributed error reflect the power and limitation of DSKRR, compared with KRR processing the whole data. Thus a small distributed error provides a large range of the number of data subsets to guarantee a small generalization error. Our results show that unlabeled data play important roles in reducing the distributed error and enlarging the number of data subsets in DSKRR. Our analysis also applies to the case when the regression function is out of the reproducing kernel Hilbert space. Numerical experiments including toy simulations and a music-prediction task are employed to demonstrate our theoretical statements and show the power of unlabeled data in distributed learning. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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