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Anil Jain

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4 papers

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TIST Journal 2022 Journal Article

Trustworthy AI: A Computational Perspective

Haochen Liu
Yiqi Wang
Wenqi Fan
Xiaorui Liu
Yaxin Li
Shaili Jain
Yunhao Liu
Anil Jain

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone’s daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by reducing labor, increasing everyday conveniences, and promoting social good. However, recent research and AI applications indicate that AI can cause unintentional harm to humans by, for example, making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against a group or groups. Consequently, trustworthy AI has recently garnered increased attention regarding the need to avoid the adverse effects that AI could bring to people, so people can fully trust and live in harmony with AI technologies. A tremendous amount of research on trustworthy AI has been conducted and witnessed in recent years. In this survey, we present a comprehensive appraisal of trustworthy AI from a computational perspective to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex subject, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Nondiscrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

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NeurIPS Conference 2012 Conference Paper

Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning

Jinfeng Yi
Rong Jin
Shaili Jain
Tianbao Yang
Anil Jain

One of the main challenges in data clustering is to define an appropriate similarity measure between two objects. Crowdclustering addresses this challenge by defining the pairwise similarity based on the manual annotations obtained through crowdsourcing. Despite its encouraging results, a key limitation of crowdclustering is that it can only cluster objects when their manual annotations are available. To address this limitation, we propose a new approach for clustering, called \textit{semi-crowdsourced clustering} that effectively combines the low-level features of objects with the manual annotations of a subset of the objects obtained via crowdsourcing. The key idea is to learn an appropriate similarity measure, based on the low-level features of objects, from the manual annotations of only a small portion of the data to be clustered. One difficulty in learning the pairwise similarity measure is that there is a significant amount of noise and inter-worker variations in the manual annotations obtained via crowdsourcing. We address this difficulty by developing a metric learning algorithm based on the matrix completion method. Our empirical study with two real-world image data sets shows that the proposed algorithm outperforms state-of-the-art distance metric learning algorithms in both clustering accuracy and computational efficiency.

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NeurIPS Conference 2010 Conference Paper

Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition

Serhat Bucak
Rong Jin
Anil Jain

Recent studies have shown that multiple kernel learning is very effective for object recognition, leading to the popularity of kernel learning in computer vision problems. In this work, we develop an efficient algorithm for multi-label multiple kernel learning (ML-MKL). We assume that all the classes under consideration share the same combination of kernel functions, and the objective is to find the optimal kernel combination that benefits all the classes. Although several algorithms have been developed for ML-MKL, their computational cost is linear in the number of classes, making them unscalable when the number of classes is large, a challenge frequently encountered in visual object recognition. We address this computational challenge by developing a framework for ML-MKL that combines the worst-case analysis with stochastic approximation. Our analysis shows that the complexity of our algorithm is $O(m^{1/3}\sqrt{ln m})$, where $m$ is the number of classes. Empirical studies with object recognition show that while achieving similar classification accuracy, the proposed method is significantly more efficient than the state-of-the-art algorithms for ML-MKL.

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NeurIPS Conference 2002 Conference Paper

Feature Selection in Mixture-Based Clustering

Martin Law
Anil Jain
Mário Figueiredo

There exist many approaches to clustering, but the important issue of feature selection, i. e. , selecting the data attributes that are relevant for clustering, is rarely addressed. Feature selection for clustering is difﬁcult due to the absence of class labels. We propose two approaches to feature selection in the context of Gaussian mixture-based clustering. In the ﬁrst one, instead of making hard selections, we estimate feature saliencies. An expectation-maximization (EM) algorithm is derived for this task. The second approach extends Koller and Sahami’s mutual-information- based feature relevance criterion to the unsupervised case. Feature selec- tion is then carried out by a backward search scheme. This scheme can be classiﬁed as a “wrapper”, since it wraps mixture estimation in an outer layer that performs feature selection. Experimental results on synthetic and real data show that both methods have promising performance.

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