Charles X. Ling

ICLR Conference 2025 Conference Paper

Event-Driven Online Vertical Federated Learning

• Ganyu Wang
• Boyu Wang 0004
• Bin Gu 0001
• Charles X. Ling

Online learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents challenges due to the unique nature of VFL, where clients possess non-intersecting feature sets for the same sample. In real-world scenarios, the clients may not receive data streaming for the disjoint features for the same entity synchronously. Instead, the data are typically generated by an *event* relevant to only a subset of clients. We are the first to identify these challenges in online VFL, which have been overlooked by previous research. To address these challenges, we proposed an event-driven online VFL framework. In this framework, only a subset of clients were activated during each event, while the remaining clients passively collaborated in the learning process. Furthermore, we incorporated *dynamic local regret (DLR)* into VFL to address the challenges posed by online learning problems with non-convex models within a non-stationary environment. We conducted a comprehensive regret analysis of our proposed framework, specifically examining the DLR under non-convex conditions with event-driven online VFL. Extensive experiments demonstrated that our proposed framework was more stable than the existing online VFL framework under non-stationary data conditions while also significantly reducing communication and computation costs.

ICML Conference 2025 Conference Paper

FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning

• Ganyu Wang
• Jinjie Fang
• Maxwell J. Yin
• Bin Gu 0001
• Xi Chen 0009
• Boyu Wang 0004
• Yi Chang 0001
• Charles X. Ling

Black-Box Discrete Prompt Learning (BDPL) is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting Federated Learning (FL) to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called FedOne, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results.

ICML Conference 2025 Conference Paper

Homophily Enhanced Graph Domain Adaptation

• Ruiyi Fang
• Bingheng Li
• Jingyu Zhao
• Ruizhi Pu
• Qiuhao Zeng
• Gezheng Xu
• Charles X. Ling
• Boyu Wang 0004

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the significance of graph homophily, a pivotal factor for graph domain alignment, which, however, has long been overlooked in existing approaches. Specifically, our analysis first reveals that homophily discrepancies exist in benchmarks. Moreover, we also show that homophily discrepancies degrade GDA performance from both empirical and theoretical aspects, which further underscores the importance of homophily alignment in GDA. Inspired by this finding, we propose a novel homophily alignment algorithm that employs mixed filters to smooth graph signals, thereby effectively capturing and mitigating homophily discrepancies between graphs. Experimental results on a variety of benchmarks verify the effectiveness of our method.

ICLR Conference 2025 Conference Paper

On the Benefits of Attribute-Driven Graph Domain Adaptation

• Ruiyi Fang
• Bingheng Li
• Zhao Kang 0001
• Qiuhao Zeng
• Nima Hosseini Dashtbayaz
• Ruizhi Pu
• Charles X. Ling
• Boyu Wang 0004

Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscore the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmark verify the effectiveness of our method.

ICLR Conference 2025 Conference Paper

Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control

• Gezheng Xu
• Hui Guo
• Li Yi
• Charles X. Ling
• Boyu Wang
• Grace Yi

Source-Free Domain Adaptation (SFDA) seeks to adapt a pre-trained source model to the target domain using only unlabeled target data, without access to the original source data. While current state-of-the-art (SOTA) methods rely on leveraging weak supervision from the source model to extract reliable information for self-supervised adaptation, they often overlook the uncertainty that arises during the transfer process. In this paper, we conduct a systematic and theoretical analysis of the uncertainty inherent in existing SFDA methods and demonstrate its impact on transfer performance through the lens of Distributionally Robust Optimization (DRO). Building upon the theoretical results, we propose a novel instance-dependent uncertainty control algorithm for SFDA. Our method is designed to quantify and exploit the uncertainty during the adaptation process, significantly improving the model performance. Extensive experiments on benchmark datasets and empirical analyses confirm the validity of our theoretical findings and the effectiveness of the proposed method. This work offers new insights into understanding and advancing SFDA performance.

ICLR Conference 2025 Conference Paper

ZETA: Leveraging Z-order Curves for Efficient Top-k Attention

• Qiuhao Zeng
• Jerry Huang
• Peng Lu
• Gezheng Xu
• Boxing Chen
• Charles X. Ling
• Boyu Wang 0004

Over recent years, the Transformer has become a fundamental building block for sequence modeling architectures. Yet at its core is the use of self-attention, whose memory and computational cost grow quadratically with the sequence length $N$, rendering it prohibitively expensive for long sequences. A promising approach is top-$k$ attention, which selects only the $k$ most relevant tokens and achieves performance comparable to vanilla self-attention while significantly reducing space and computational demands. However, causal masks require the current query token to only attend to past tokens, preventing existing top-$k$ attention methods from efficiently searching for the most relevant tokens in parallel, thereby limiting training efficiency. In this work, we propose ZETA, leveraging Z-Order Curves for Efficient Top-k Attention, to enable parallel querying of past tokens for entire sequences. We first theoretically show that the choice of key and query dimensions involves a trade-off between the curse of dimensionality and the preservation of relative distances after projection. In light of this insight, we propose reducing the dimensionality of keys and queries in contrast to values and further leveraging Z-order curves to map low-dimensional keys and queries into one-dimensional space, which permits parallel sorting, thereby largely improving the efficiency for top-$k$ token selection. Experimental results demonstrate that ZETA~matches the performance of standard attention on synthetic tasks Associative Recall and outperforms attention and its variants on Long-Range Arena and WikiText-103 language modeling.

AAAI Conference 2024 Conference Paper

Generalizing across Temporal Domains with Koopman Operators

• Qiuhao Zeng
• Wei Wang
• Fan Zhou
• Gezheng Xu
• Ruizhi Pu
• Changjian Shui
• Christian Gagné
• Shichun Yang

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have been proposed to address this issue, a comprehensive understanding of the underlying generalization theory is still lacking. In this study, we contribute novel theoretic results that aligning conditional distribution leads to the reduction of generalization bounds. Our analysis serves as a key motivation for solving the Temporal Domain Generalization (TDG) problem through the application of Koopman Neural Operators, resulting in Temporal Koopman Networks (TKNets). By employing Koopman Neural Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains. Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach.

ICML Conference 2024 Conference Paper

Intersectional Unfairness Discovery

• Gezheng Xu
• Qi Chen 0015
• Charles X. Ling
• Boyu Wang 0004
• Changjian Shui

AI systems have been shown to produce unfair results for certain subgroups of population, highlighting the need to understand bias on certain sensitive attributes. Current research often falls short, primarily focusing on the subgroups characterized by a single sensitive attribute, while neglecting the nature of intersectional fairness of multiple sensitive attributes. This paper focuses on its one fundamental aspect by discovering diverse high-bias intersectional sensitive attributes. Specifically, we propose a Bias-Guided Generative Network (BGGN). By treating each bias value as a reward, BGGN efficiently generates high-bias intersectional sensitive attributes. Experiments on real-world text and image datasets demonstrate a diverse and efficient discovery of BGGN. To further evaluate the generated unseen but possible unfair intersectional sensitive attributes, we formulate them as prompts and use modern generative AI to produce new text and images. The results of frequently generating biased data provides new insights of discovering potential unfairness in popular modern generative AI systems. Warning: This paper contains examples that are offensive in nature.

ICLR Conference 2024 Conference Paper

Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time

• Qiuhao Zeng
• Changjian Shui
• Long-Kai Huang
• Peng Liu
• Xi Chen 0009
• Charles X. Ling
• Boyu Wang 0004

Distribution shifts over time are common in real-world machine-learning applications. This scenario is formulated as Evolving Domain Generalization (EDG), where models aim to generalize well to unseen target domains in a time-varying system by learning and leveraging the underlying evolving pattern of the distribution shifts across domains. However, existing methods encounter challenges due to the limited number of timestamps (every domain corresponds to a timestamp) in EDG datasets, leading to difficulties in capturing evolving dynamics and risking overfitting to the sparse timestamps, which hampers their generalization and adaptability to new tasks. To address this limitation, we propose a novel approach SDE-EDG that collects the Infinitely Fined-Grid Evolving Trajectory (IFGET) of the data distribution with continuous-interpolated samples to bridge temporal gaps (intervals between two successive timestamps). Furthermore, by leveraging the inherent capacity of Stochastic Differential Equations (SDEs) to capture continuous trajectories, we propose their use to align SDE-modeled trajectories with IFGET across domains, thus enabling the capture of evolving distribution trends. We evaluate our approach on several benchmark datasets and demonstrate that it can achieve superior performance compared to existing state-of-the-art methods.

ICLR Conference 2024 Conference Paper

Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s)

• Diyang Li
• Charles X. Ling
• Zhiqiang Xu
• Huan Xiong
• Bin Gu 0001

Generalized Linear Models (GLMs) encompass a wide array of regression and classification models, where prediction is a function of a linear combination of the input variables. Often in real-world scenarios, a number of observations would be added into or removed from the existing training dataset, necessitating the development of learning systems that can efficiently train optimal models with varying observations in an online (sequential) manner instead of retraining from scratch. Despite the significance of data-varying scenarios, most existing approaches to sparse GLMs concentrate on offline batch updates, leaving online solutions largely underexplored. In this work, we present the first algorithm without compromising accuracy for GLMs regularized by sparsity-enforcing penalties trained on varying observations. Our methodology is capable of handling the addition and deletion of observations simultaneously, while adaptively updating data-dependent regularization parameters to ensure the best statistical performance. Specifically, we recast sparse GLMs as a bilevel optimization objective upon varying observations and characterize it as an explicit gradient flow in the underlying space for the inner and outer subproblems we are optimizing over, respectively. We further derive a set of rules to ensure a proper transition at regions of non-smoothness, and establish the guarantees of theoretical consistency and finite convergence. Encouraging results are exhibited on real-world benchmarks.

IJCAI Conference 2024 Conference Paper

Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations

• Nima Hosseini Dashtbayaz
• Ghazal Farhani
• Boyu Wang
• Charles X. Ling

The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying on the existing theory leads to unjustified design choices and suboptimal performance. In this work, we analyze the residual loss by studying its characteristics at critical points to find the conditions that result in effective training of PINNs. Specifically, we first show that under certain conditions, the residual loss of PINNs can be globally minimized by a wide neural network. Furthermore, our analysis also reveals that an activation function with well-behaved high-order derivatives plays a crucial role in minimizing the residual loss. In particular, to solve a k-th order PDE, the k-th derivative of the activation function should be bijective. The established theory paves the way for designing and choosing effective activation functions for PINNs and explains why periodic activations have shown promising performance in certain cases. Finally, we verify our findings by conducting a set of experiments on several PDEs. Our code is publicly available at https: //github. com/nimahsn/pinns_tf2.

NeurIPS Conference 2023 Conference Paper

A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning

• Ganyu Wang
• Bin Gu
• Qingsong Zhang
• Xiang Li
• Boyu Wang
• Charles X. Ling

Vertical Federated Learning (VFL) is a collaborative machine learning paradigm that enables multiple participants to jointly train a model on their private data without sharing it. To make VFL practical, privacy security and communication efficiency should both be satisfied. Recent research has shown that Zero-Order Optimization (ZOO) in VFL can effectively conceal the internal information of the model without adding costly privacy protective add-ons, making it a promising approach for privacy and efficiency. However, there are still two key problems that have yet to be resolved. First, the convergence rate of ZOO-based VFL is significantly slower compared to gradient-based VFL, resulting in low efficiency in model training and more communication round, which hinders its application on large neural networks. Second, although ZOO-based VFL has demonstrated resistance to state-of-the-art (SOTA) attacks, its privacy guarantee lacks a theoretical explanation. To address these challenges, we propose a novel cascaded hybrid optimization approach that employs a zeroth-order (ZO) gradient on the most critical output layer of the clients, with other parts utilizing the first-order (FO) gradient. This approach preserves the privacy protection of ZOO while significantly enhancing convergence. Moreover, we theoretically prove that applying ZOO to the VFL is equivalent to adding Gaussian Mechanism to the gradient information, which offers an implicit differential privacy guarantee. Experimental results demonstrate that our proposed framework achieves similar utility as the Gaussian mechanism under the same privacy budget, while also having significantly lower communication costs compared with SOTA communication-efficient VFL frameworks.

ICLR Conference 2023 Conference Paper

When Source-Free Domain Adaptation Meets Learning with Noisy Labels

• Li Yi
• Gezheng Xu
• Pengcheng Xu 0008
• Jiaqi Li 0005
• Ruizhi Pu
• Charles X. Ling
• A. Ian McLeod
• Boyu Wang 0004

Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the pseudo-labels generated by source models that can be noisy due to domain shift. In this paper, we study SFDA from the perspective of learning with label noise (LLN). Unlike the label noise in the conventional LLN scenario, we prove that the label noise in SFDA follows a different distribution assumption. We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA. Empirical evidence suggests that only marginal improvements are achieved when applying the existing LLN methods to solve the SFDA problem. On the other hand, although there exists a fundamental difference between the label noise in the two scenarios, we demonstrate theoretically that the early-time training phenomenon (ETP), which has been previously observed in conventional label noise settings, can also be observed in the SFDA problem. Extensive experiments demonstrate significant improvements to existing SFDA algorithms by leveraging ETP to address the label noise in SFDA.

NeurIPS Conference 2022 Conference Paper

On Learning Fairness and Accuracy on Multiple Subgroups

• Changjian Shui
• Gezheng Xu
• Qi Chen
• Jiaqi Li
• Charles X. Ling
• Tal Arbel
• Boyu Wang
• Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

ICML Conference 2021 Conference Paper

Aggregating From Multiple Target-Shifted Sources

• Changjian Shui
• Zijian Li 0001
• Jiaqi Li 0005
• Christian Gagné 0001
• Charles X. Ling
• Boyu Wang 0004

Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different sources based on their relations. In this paper, we analyzed the problem for aggregating source domains with different label distributions, where most recent source selection approaches fail. Our proposed algorithm differs from previous approaches in two key ways: the model aggregates multiple sources mainly through the similarity of semantic conditional distribution rather than marginal distribution; the model proposes a unified framework to select relevant sources for three popular scenarios, i. e. , domain adaptation with limited label on target domain, unsupervised domain adaptation and label partial unsupervised domain adaption. We evaluate the proposed method through extensive experiments. The empirical results significantly outperform the baselines.

AAAI Conference 2020 Conference Paper

Region-Based Global Reasoning Networks

• Chuanming Wang
• Huiyuan Fu
• Charles X. Ling
• Peilun Du
• Huadong Ma

Global reasoning plays a significant role in many computer vision tasks which need to capture long-distance relationships. However, most current studies on global reasoning focus on exploring the relationship between pixels and ignore the critical role of the regions. In this paper, we propose an novel approach that explores the relationship between regions which have richer semantics than pixels. Specifically, we design a region aggregation method that can gather regional features automatically into a uniform shape, and adjust theirs positions adaptively for better alignment. To achieve the best performance of global reasoning, we propose various relationship exploration methods and apply them on the regional features. Our region-based global reasoning module, named ReGr, is end-to-end and can be inserted into existing visual understanding models without extra supervision. To evaluate our approach, we apply ReGr to fine-grained classification and action recognition benchmark tasks, and the experimental results demonstrate the effectiveness of our approach.

UAI Conference 2017 Conference Paper

Triply Stochastic Gradients on Multiple Kernel Learning

• Xiang Li 0012
• Bin Gu 0001
• Shuang Ao
• Huaimin Wang 0001
• Charles X. Ling

Multiple Kernel Learning (MKL) is highly useful for learning complex data with multiple cues or representations. However, MKL is known to have poor scalability because of the expensive kernel computation. Dai et al (2014) proposed to use a doubly Stochastic Gradient Descent algorithm (doubly SGD) to greatly improve the scalability of kernel methods. However, the algorithm is not suitable for MKL because it cannot learn the kernel weights. In this paper, we provide a novel extension to doubly SGD for MKL so that both the decision functions and the kernel weights can be learned simultaneously. To achieve this, we develop the triply Stochastic Gradient Descent (triply SGD) algorithm which involves three sources of randomness – the data points, the random features, and the kernels, which was not considered in previous work. We prove that our algorithm enjoys similar convergence rate as that of doubly SGD. Comparing to several traditional MKL solutions, we show that our method has faster convergence speed and achieved better accuracy. Most importantly, our method makes it possible to learn MKL problems with millions of data points on a normal desktop PC.

ICML Conference 2015 Conference Paper

A New Generalized Error Path Algorithm for Model Selection

• Bin Gu 0001
• Charles X. Ling

Model selection with cross validation (CV) is very popular in machine learning. However, CV with grid and other common search strategies cannot guarantee to find the model with minimum CV error, which is often the ultimate goal of model selection. Recently, various solution path algorithms have been proposed for several important learning algorithms including support vector classification, Lasso, and so on. However, they still do not guarantee to find the model with minimum CV error. In this paper, we first show that the solution paths produced by various algorithms have the property of piecewise linearity. Then, we prove that a large class of error (or loss) functions are piecewise constant, linear, or quadratic w. r. t. the regularization parameter, based on the solution path. Finally, we propose a new generalized error path algorithm (GEP), and prove that it will find the model with minimum CV error for the entire range of the regularization parameter. The experimental results on a variety of datasets not only confirm our theoretical findings, but also show that the best model with our GEP has better generalization error on the test data, compared to the grid search, manual search, and random search.

IJCAI Conference 2015 Conference Paper

Data Sparseness in Linear SVM

• Xiang Li
• Huaimin Wang
• Bin Gu
• Charles X. Ling

Large sparse datasets are common in many realworld applications. Linear SVM has been shown to be very efficient for classifying such datasets. However, it is still unknown how data sparseness would affect its convergence behavior. To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and the data sampling process in the PAC framework. We first study the convergence behavior of linear SVM experimentally, and make several observations, useful for real-world applications. We then offer theoretical proofs for our observations by studying the Bayes risk and PAC bound. Our experiment and theoretic results are valuable for learning large sparse datasets with linear SVM.

TIST Journal 2012 Journal Article

A Reliable People Counting System via Multiple Cameras

• Huadong Ma
• Chengbin Zeng
• Charles X. Ling

Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras. To improve the reliability of human detection from a single camera, we use a dimensionality reduction method on the multilevel edge and texture features to handle the large variations in human appearance and poses. To accelerate the detection speed, we propose a novel two-stage cascade-of-rejectors method. To handle the heavy occlusion in crowded scenes, we present a fusion method with error tolerance to combine human detection from multiple cameras. To improve the speed and accuracy of moving people counting, we combine our multiview fusion detection method with particle tracking to count the number of people moving in/out the camera view (“border control”). Extensive experiments and analyses show that our method outperforms state-of-the-art techniques in single- and multicamera datasets for both speed and reliability. We also design a deployed system for fast and reliable people (still or moving) counting by using multiple cameras.

IJCAI Conference 2011 Conference Paper

A New Search Engine Integrating Hierarchical Browsing and Keyword Search

• Da Kuang
• Xiao Li
• Charles X. Ling

The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i. e. , not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on Support Vector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1. 2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.

LORI Conference 2011 Conference Paper

Efficient Action Extraction with Many-to-Many Relationship between Actions and Features

• Jianfeng Du
• Yong Hu 0002
• Charles X. Ling
• Ming Fan
• Mei Liu

Real-world problems often call for efficient methods to discovery actionable knowledge on which business can directly act [3]. Some works for discovering actionable knowledge [3, 5] view actions as behaviors which render a state of an instance into a preferred state, where a state is represented by feature values of the instance and whether a state is preferred is determined by a classifier. Actions usually have many-to-many relations with features of an instance. That is, an action may affect multiple features of an instance, and vise versa, a feature may be influenced by multiple actions. This type of many-to-many relationships between actions and features is prevalent in real-world applications. However, most existing works [3, 5] only deal with one-to-one relationship and ignore manyto- many relationship between actions and features. In these works, an action is treated as a behavior with a fixed execution cost. Restricting to a one-to-one relationship between actions and features may not yield an action set (i. e. a set of actions) with the minimal total execution cost. Moreover, one-to-one relationship is simply a special case of many-to-many relationship, and hence the latter will be applicable to more real-world problems. Therefore we aim to extract action sets from a classifier for which the total execution cost is minimal based on many-to-many relationship between actions and features.

TIST Journal 2011 Journal Article

Introduction to special issue on machine learning for business applications

• Charles X. Ling

ICML Conference 2008 Conference Paper

Discriminative parameter learning for Bayesian networks

• Jiang Su
• Harry Zhang
• Charles X. Ling
• Stan Matwin

IJCAI Conference 2007 Conference Paper

• Jin Huang
• Charles X. Ling

Evaluation measures play an important role in machine learning because they are used not only to compare different learning algorithms, but also often as goals to optimize in constructing learning models. Both formal and empirical work has been published in comparing evaluation measures. In this paper, we propose a general approach to construct new measures based on the existing ones, and we prove that the new measures are consistent with, and finer than, the existing ones. We also show that the new measure is more correlated to RMS (Root Mean Square error) with artificial datasets. Finally, we demonstrate experimentally that the greedy-search based algorithm (such as artificial neural networks) trained with the new and finer measure usually can achieve better prediction performance. This provides a general approach to improve the predictive performance of existing learning algorithms based on greedy search.

ICML Conference 2006 Conference Paper

Feature value acquisition in testing: a sequential batch test algorithm

• Victor S. Sheng
• Charles X. Ling

ICML Conference 2004 Conference Paper

Decision trees with minimal costs

• Charles X. Ling
• Qiang Yang 0001
• Jianning Wang
• Shichao Zhang 0001

IJCAI Conference 2003 Conference Paper

AUC: a Statistically Consistent and more Discriminating Measure than Accuracy

• Charles X. Ling
• Jin Huang
• Harry Zhang

Predictive accuracy has been used as the main and often only evaluation criterion for the predictive performance of classification learning algorithms. In recent years, the area under the ROC (Receiver Operating Characteristics) curve, or simply AUC, has been proposed as an alternative single-number measure for evaluating learning algorithms. In this paper, we prove that AUC is a better measure than accuracy. More specifically, we present rigourous definitions on consistency and discriminancy in comparing two evaluation measures for learning algorithms. We then present empirical evaluations and a formal proof to establish that AUC is indeed statistically consistent and more discriminating than accuracy. Our result is quite significant since we formally prove that, for the first time, AUC is a better measure than accuracy in the evaluation of learning algorithms.

ICML Conference 2003 Conference Paper

Decision Tree with Better Ranking

• Charles X. Ling
• Robert J. Yan

ICML Conference 2002 Conference Paper

Representational Upper Bounds of Bayesian Networks

• Huajie Zhang
• Charles X. Ling

JMLR Journal 2002 Journal Article

The Representational Power of Discrete Bayesian Networks

• Charles X. Ling
• Huajie Zhang

One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper, we establish an association between the structural complexity of Bayesian networks and their representational power. We use the maximum number of nodes' parents as the measure for the structural complexity of Bayesian networks, and the maximum XOR contained in a target function as the measure for the function complexity. A representational upper bound is established and proved. Roughly speaking, discrete Bayesian networks with each node having at most k parents cannot represent any function containing ( k +1)-XORs. Our theoretical results help us to gain a deeper understanding on the capacities and limitations of Bayesian networks.

ICML Conference 2001 Conference Paper

Learnability of Augmented Naive Bayes in Nonimal Domains

• Huajie Zhang
• Charles X. Ling

IJCAI Conference 1997 Conference Paper

Alignment Algorithms for Learning to Read Aloud

• Charles X. Ling
• Handong Wang

A complete system of learning spelling-tophoneme conversion of English words consists of three major processes: alignment, mapping learning, and grapheme generation. Such a system can be used to construct prototypes of reading machines for English or other lan­ guages quickly and automatically. This paper focusses on the alignment process, which is crit­ ical to mapping learning and grapheme genera­ tion. We present several novel alignment algo­ rithms which learn alignment without supervi­ sion. The basic alignment algorithm is a hillclimbing algorithm. Several improvements of it are studied and tested. In addition, a method that overcomes the pitfall in hill-climbing al­ gorithms is designed. Our best alignment al­ gorithm produces very impressive results: only 0. 5% of error rate.

ICML Conference 1994 Conference Paper

Learning Recursive Relations with Randomly Selected Small Training Sets

• David W. Aha
• Stephane Lapointe
• Charles X. Ling
• Stan Matwin