Search Results for author: Oluwasanmi Koyejo

Found 43 papers, 13 papers with code

Joint Gaussian Graphical Model Estimation: A Survey

1 code implementation19 Oct 2021 Katherine Tsai, Oluwasanmi Koyejo, Mladen Kolar

Graphs from complex systems often share a partial underlying structure across domains while retaining individual features.

Secure Byzantine-Robust Distributed Learning via Clustering

no code implementations6 Oct 2021 Raj Kiriti Velicheti, Derek Xia, Oluwasanmi Koyejo

Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem.

Federated Learning

Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation

1 code implementation ICLR 2021 Peiye Zhuang, Oluwasanmi Koyejo, Alexander G. Schwing

Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e. g., gradually making a summer scene look like it was taken in winter.

Image Manipulation

A Nonconvex Framework for Structured Dynamic Covariance Recovery

no code implementations11 Nov 2020 Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo

We prove a linear convergence rate up to a nontrivial statistical error for the proposed descent scheme and establish sample complexity guarantees for the estimator.

Quadratic Metric Elicitation for Fairness and Beyond

no code implementations3 Nov 2020 Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi Koyejo

Metric elicitation is a recent framework for eliciting performance metrics that best reflect implicit user preferences based on the application and context.


CSER: Communication-efficient SGD with Error Reset

no code implementations NeurIPS 2020 Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin

The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks.

Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective

1 code implementation1 Jul 2020 Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo

Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference.

Bayesian Inference

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

2 code implementations25 Jun 2020 Kaizhao Liang, Jacky Y. Zhang, Boxin Wang, Zhuolin Yang, Oluwasanmi Koyejo, Bo Li

Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain.

Transfer Learning

Towards a Deep Network Architecture for Structured Smoothness

no code implementations ICLR 2020 Haroun Habeeb, Oluwasanmi Koyejo

We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate the inductive bias of structured smoothness into a deep learning model.

Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information

no code implementations28 Jan 2020 Amar Budhiraja, Gaurush Hiranandani, Darshak Chhatbar, Aditya Sinha, Navya Yarrabelly, Ayush Choure, Oluwasanmi Koyejo, Prateek Jain

In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sources of side-information in the form of graphs.


Toward a Controllable Disentanglement Network

1 code implementation22 Jan 2020 Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang

By exploring the real-valued space of the soft target representation, we are able to synthesize novel images with the designated properties.

Learning Controllable Disentangled Representations with Decorrelation Regularization

no code implementations25 Dec 2019 Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang

By exploiting the real-valued space of the soft target representations, we are able to synthesize novel images with the designated properties.

Learning Sparse Distributions using Iterative Hard Thresholding

no code implementations NeurIPS 2019 Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo

Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such as sparse inference.

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

1 code implementation12 Sep 2019 Sen Na, Mladen Kolar, Oluwasanmi Koyejo

Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation.

Consistent Classification with Generalized Metrics

no code implementations24 Aug 2019 Xiaoyan Wang, Ran Li, Bowei Yan, Oluwasanmi Koyejo

We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i. e., involving multiple, possibly correlated multiclass labels.

General Classification

Partially Linear Additive Gaussian Graphical Models

no code implementations8 Jun 2019 Sinong Geng, Minhao Yan, Mladen Kolar, Oluwasanmi Koyejo

We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders.

Clustered Monotone Transforms for Rating Factorization

no code implementations31 Oct 2018 Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo, Sreangsu Acharyya

This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations.

Recommendation Systems

Interpreting Black Box Predictions using Fisher Kernels

no code implementations23 Oct 2018 Rajiv Khanna, Been Kim, Joydeep Ghosh, Oluwasanmi Koyejo

Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models.

Data Summarization

Joint Nonparametric Precision Matrix Estimation with Confounding

no code implementations16 Oct 2018 Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo

Empirical results are presented using simulated and real brain imaging data, which suggest that our approach improves precision matrix estimation, as compared to baselines, when confounding is present.

xGEMs: Generating Examplars to Explain Black-Box Models

no code implementations22 Jun 2018 Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh

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 points cross decision boundaries.

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

no code implementations ICML 2018 Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar

Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification.

General Classification

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

1 code implementation25 May 2018 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.

Phocas: dimensional Byzantine-resilient stochastic gradient descent

no code implementations23 May 2018 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.

Learning the Base Distribution in Implicit Generative Models

no code implementations12 Mar 2018 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 distributions such as isotropic Gaussian.

Generalized Byzantine-tolerant SGD

no code implementations27 Feb 2018 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.

Hallucinating brains with artificial brains

no code implementations ICLR 2018 Peiye Zhuang, Alexander G. Schwing, Oluwasanmi Koyejo

Our classification results provide a quantitative evaluation of the quality of the generated images, and also serve as an additional contribution of this manuscript.

Dependent relevance determination for smooth and structured sparse regression

1 code implementation28 Nov 2017 Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow

Our approach represents a hierarchical extension of the relevance determination framework, where we add a transformed Gaussian process to model the dependencies between the prior variances of regression weights.

Consistency Analysis for Binary Classification Revisited

no code implementations ICML 2017 Krzysztof Dembczyński, Wojciech Kotłowski, Oluwasanmi Koyejo, Nagarajan Natarajan

Statistical learning theory is at an inflection point enabled by recent advances in understanding and optimizing a wide range of metrics.

General Classification Learning Theory

Preference Completion from Partial Rankings

no code implementations NeurIPS 2016 Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh

We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values.

Matrix Completion

Online Classification with Complex Metrics

no code implementations23 Oct 2016 Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar

The proposed framework is general, as it applies to both batch and online learning, and to both linear and non-linear models.

General Classification

Information Projection and Approximate Inference for Structured Sparse Variables

no code implementations12 Jul 2016 Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo

Approximate inference via information projection has been recently introduced as a general-purpose approach for efficient probabilistic inference given sparse variables.

A simple and provable algorithm for sparse diagonal CCA

no code implementations29 May 2016 Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated.

Generalized Linear Models for Aggregated Data

no code implementations14 May 2016 Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

We consider a limiting case of generalized linear modeling when the target variables are only known up to permutation, and explore how this relates to permutation testing; a standard technique for assessing statistical dependency.


The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Function

1 code implementation10 Nov 2015 James M. Shine, Patrick G. Bissett, Peter T. Bell, Oluwasanmi Koyejo, Joshua H. Balsters, Krzysztof J. Gorgolewski, Craig A. Moodie, Russell A. Poldrack

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions, however it is unclear how this mechanism manifests over time.

Neurons and Cognition

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics

no code implementations7 May 2015 Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit S. Dhillon

We provide a general theoretical analysis of expected out-of-sample utility, also referred to as decision-theoretic classification, for non-decomposable binary classification metrics such as F-measure and Jaccard coefficient.

General Classification

False discovery rate smoothing

1 code implementation22 Nov 2014 Wesley Tansey, Oluwasanmi Koyejo, Russell A. Poldrack, James G. Scott

We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods.

Methodology Applications Computation

A Constrained Matrix-Variate Gaussian Process for Transposable Data

no code implementations27 Apr 2014 Oluwasanmi Koyejo, Cheng Lee, Joydeep Ghosh

Transposable data represents interactions among two sets of entities, and are typically represented as a matrix containing the known interaction values.

Recommendation Systems

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