1k citations · 2.6k across the 149 of their papers we have counts for
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Clustered Monotone Transforms for Rating Factorization
Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo +1
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogene…
Interpreting Black Box Predictions using Fisher Kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh +1
Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models. To this end, we take a novel look at black box interpr…
Joint Nonparametric Precision Matrix Estimation with Confounding
Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo
We consider the problem of precision matrix estimation where, due to extraneous confounding of the underlying precision matrix, the data are independent but not identically distrib…
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials
Yogatheesan Varatharajah, Brent Berry, Sanmi Koyejo +1
Clinical trials involving multiple treatments utilize randomization of the treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evalua…
xGEMs: Generating Examplars to Explain Black-Box Models
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim +1
This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data po…
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong +1
Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification. These complex performance measures are ty…
Performance Metric Elicitation from Pairwise Classifier Comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta +1
Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of m…
Phocas: dimensional Byzantine-resilient stochastic gradient descent
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily mani…
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generaliz…
Generalized Byzantine-tolerant SGD
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily m…
Learning the Base Distribution in Implicit Generative Models
Cem Subakan, Oluwasanmi Koyejo, Paris Smaragdis
Popular generative model learning methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple dist…