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Kaiser Asif

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4 papers

2 author rows

NeurIPS Conference 2016 Conference Paper

Adversarial Multiclass Classification: A Risk Minimization Perspective

Rizal Fathony
Anqi Liu
Kaiser Asif
Brian Ziebart

Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses. In contrast with empirical risk minimization (ERM) methods, which use convex surrogate losses to approximate the desired non-convex target loss function, adversarial methods minimize non-convex losses by treating the properties of the training data as being uncertain and worst case within a minimax game. Despite this difference in formulation, we recast adversarial classification under zero-one loss as an ERM method with a novel prescribed loss function. We demonstrate a number of theoretical and practical advantages over the very closely related hinge loss ERM methods. This establishes adversarial classification under the zero-one loss as a method that fills the long standing gap in multiclass hinge loss classification, simultaneously guaranteeing Fisher consistency and universal consistency, while also providing dual parameter sparsity and high accuracy predictions in practice.

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IJCAI Conference 2016 Conference Paper

Adversarial Sequence Tagging

Jia Li
Kaiser Asif
Hong Wang
Brian D. Ziebart
Tanya Berger-Wolf

Providing sequence tagging that minimize Hamming loss is a challenging, but important, task. Directly minimizing this loss over a training sample is generally an NP-hard problem. Instead, existing sequence tagging methods minimize a convex upper bound that upper bounds the Hamming loss. Unfortunately, this often either leads to inconsistent predictors (e. g. , max-margin methods) or predictions that are mismatched on the Hamming loss (e. g. , conditional random fields). We present adversarial sequence tagging, a consistent structured prediction framework for minimizing Hamming loss by pessimistically viewing uncertainty. Our approach pessimistically approximates the training data, yielding an adversarial game between the sequence tag predictor and the sequence labeler. We demonstrate the benefits of the approach on activity recognition and information extraction/segmentation tasks.

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UAI Conference 2015 Conference Paper

Adversarial Cost-Sensitive Classification

Kaiser Asif
Wei Xing
Sima Behpour
Brian D. Ziebart

In many classification settings, mistakes incur different application-dependent penalties based on the predicted and actual class labels. Costsensitive classifiers minimizing these penalties are needed. We propose a robust minimax approach for producing classifiers that directly minimize the cost of mistakes as a convex optimization problem. This is in contrast to previous methods that minimize the empirical risk using a convex surrogate for the cost of mistakes, since minimizing the empirical risk of the actual cost-sensitive loss is generally intractable. By treating properties of the training data as uncertain, our approach avoids these computational difficulties. We develop theory and algorithms for our approach and demonstrate its benefits on cost-sensitive classification tasks.

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NeurIPS Conference 2015 Conference Paper

Adversarial Prediction Games for Multivariate Losses

Hong Wang
Wei Xing
Kaiser Asif
Brian Ziebart

Multivariate loss functions are used to assess performance in many modern prediction tasks, including information retrieval and ranking applications. Convex approximations are typically optimized in their place to avoid NP-hard empirical risk minimization problems. We propose to approximate the training data instead of the loss function by posing multivariate prediction as an adversarial game between a loss-minimizing prediction player and a loss-maximizing evaluation player constrained to match specified properties of training data. This avoids the non-convexity of empirical risk minimization, but game sizes are exponential in the number of predicted variables. We overcome this intractability using the double oracle constraint generation method. We demonstrate the efficiency and predictive performance of our approach on tasks evaluated using the precision at k, the F-score and the discounted cumulative gain.

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