Oluwasanmi Koyejo

Affiliations:
  • University of Illinois at Urbana-Champaign, Department of Computer Science
  • Stanford University, Poldrack Lab


According to our database1, Oluwasanmi Koyejo authored at least 107 papers between 2009 and 2022.

Collaborative distances:

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Bibliography

2022
Toward a Controllable Disentanglement Network.
IEEE Trans. Cybern., 2022

One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning.
CoRR, 2022

A Reduction to Binary Approach for Debiasing Multiclass Datasets.
CoRR, 2022

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction.
CoRR, 2022

Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization.
CoRR, 2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?
CoRR, 2022

Fair Wrapping for Black-box Predictions.
CoRR, 2022

2021
Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

Does MAML Only Work via Feature Re-use? A Data Centric Perspective.
CoRR, 2021

The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence.
CoRR, 2021

Joint Gaussian Graphical Model Estimation: A Survey.
CoRR, 2021

Secure Byzantine-Robust Distributed Learning via Clustering.
CoRR, 2021

A Field Guide to Federated Optimization.
CoRR, 2021

Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task.
Brain Informatics, 2021

Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Learning To Recover Sharp Detail From Simulated Low-Dose Ct Studies.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability.
Proceedings of the 38th International Conference on Machine Learning, 2021

Optimizing Black-box Metrics with Iterative Example Weighting.
Proceedings of the 38th International Conference on Machine Learning, 2021

Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation.
Proceedings of the 9th International Conference on Learning Representations, 2021

Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
A Nonconvex Framework for Structured Dynamic Covariance Recovery.
CoRR, 2020

Quadratic Metric Elicitation with Application to Fairness.
CoRR, 2020

EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision.
CoRR, 2020

Bayesian Coresets: An Optimization Perspective.
CoRR, 2020

Does Adversarial Transferability Indicate Knowledge Transferability?
CoRR, 2020

Fairness with Overlapping Groups.
CoRR, 2020

Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information.
CoRR, 2020

Towards A Controllable Disentanglement Network.
CoRR, 2020

Fairness with Overlapping Groups; a Probabilistic Perspective.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

CSER: Communication-efficient SGD with Error Reset.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fair Performance Metric Elicitation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

On the consistency of top-k surrogate losses.
Proceedings of the 37th International Conference on Machine Learning, 2020

Zeno++: Robust Fully Asynchronous SGD.
Proceedings of the 37th International Conference on Machine Learning, 2020

Optimization and Analysis of the pAp@k Metric for Recommender Systems.
Proceedings of the 37th International Conference on Machine Learning, 2020

Towards a Deep Network Architecture for Structured Smoothness.
Proceedings of the 8th International Conference on Learning Representations, 2020

Some New Tricks for Deep Glioma Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019
Dependent relevance determination for smooth and structured sparse regression.
J. Mach. Learn. Res., 2019

Learning Controllable Disentangled Representations with Decorrelation Regularization.
CoRR, 2019

Advances and Open Problems in Federated Learning.
CoRR, 2019

Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates.
CoRR, 2019

Consistent Classification with Generalized Metrics.
CoRR, 2019

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.
CoRR, 2019

Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates.
CoRR, 2019

Asynchronous Federated Optimization.
CoRR, 2019

Clustered Monotone Transforms for Rating Factorization.
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Joint Nonparametric Precision Matrix Estimation with Confounding.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

SLSGD: Secure and Efficient Distributed On-device Machine Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Learning Sparse Distributions using Iterative Hard Thresholding.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multiclass Performance Metric Elicitation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

FMRI Data Augmentation Via Synthesis.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance.
Proceedings of the 36th International Conference on Machine Learning, 2019

Partially Linear Additive Gaussian Graphical Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Max-Sliced Wasserstein Distance and Its Use for GANs.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Interpreting Black Box Predictions using Fisher Kernels.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Performance Metric Elicitation from Pairwise Classifier Comparisons.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials.
CoRR, 2018

xGEMs: Generating Examplars to Explain Black-Box Models.
CoRR, 2018

Eliciting Binary Performance Metrics.
CoRR, 2018

Zeno: Byzantine-suspicious stochastic gradient descent.
CoRR, 2018

Phocas: dimensional Byzantine-resilient stochastic gradient descent.
CoRR, 2018

Learning the Base Distribution in Implicit Generative Models.
CoRR, 2018

Generalized Byzantine-tolerant SGD.
CoRR, 2018

Clustered Fused Graphical Lasso.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics.
Proceedings of the 35th International Conference on Machine Learning, 2018

Bayesian Structure Learning for Dynamic Brain Connectivity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.
PLoS Comput. Biol., 2017

What's in a pattern? Examining the type of signal multivariate analysis uncovers at the group level.
NeuroImage, 2017

A Deflation Method for Structured Probabilistic PCA.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Consistency Analysis for Binary Classification Revisited.
Proceedings of the 34th International Conference on Machine Learning, 2017

Information Projection and Approximate Inference for Structured Sparse Variables.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Frequency Domain Predictive Modelling with Aggregated Data.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Rényi divergence minimization based co-regularized multiview clustering.
Mach. Learn., 2016

Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Examples are not enough, learn to criticize! Criticism for Interpretability.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Preference Completion from Partial Rankings.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Optimal Classification with Multivariate Losses.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Sparse Parameter Recovery from Aggregated Data.
Proceedings of the 33nd International Conference on Machine Learning, 2016

A Simple and Provable Algorithm for Sparse Diagonal CCA.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives.
NeuroImage, 2015

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics.
CoRR, 2015

Consistent Multilabel Classification.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Simultaneous Prognosis and Exploratory Analysis of Multiple Chronic Conditions Using Clinical Notes.
Proceedings of the 2015 International Conference on Healthcare Informatics, 2015

Simultaneous Prognosis of Multiple Chronic Conditions from Heterogeneous EHR Data.
Proceedings of the 2015 International Conference on Healthcare Informatics, 2015

Sparse Submodular Probabilistic PCA.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Generalized Linear Models for Aggregated Data.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
A constrained matrix-variate Gaussian process for transposable data.
Mach. Learn., 2014

Sparse Bayesian structure learning with dependent relevance determination priors.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Consistent Binary Classification with Generalized Performance Metrics.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On Prior Distributions and Approximate Inference for Structured Variables.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Toward open sharing of task-based fMRI data: the OpenfMRI project.
Frontiers Neuroinformatics, 2013

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
CoRR, 2013

Constrained Bayesian Inference for Low Rank Multitask Learning.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Retargeted matrix factorization for collaborative filtering.
Proceedings of the Seventh ACM Conference on Recommender Systems, 2013

Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Constrained Gaussian Process Regression for Gene-Disease Association.
Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013

Identifying candidate disease genes using a trace norm constrained bipartite raking model.
Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

Bayesian Structure Learning for Functional Neuroimaging.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Learning to Rank With Bregman Divergences and Monotone Retargeting.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011
Reports of the AAAI 2010 Fall Symposia.
AI Mag., 2011

A kernel-based approach to exploiting interaction-networks in heterogeneous information sources for improved recommender systems.
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, 2011

2010
Reports of the AAAI 2009 Fall Symposia.
AI Mag., 2010

Preface: Manifold Learning and Its Applications.
Proceedings of the Manifold Learning and Its Applications, 2010

Organizing Committee.
Proceedings of the Manifold Learning and Its Applications, 2010

2009
MiPPS: A Generative Model for Multi-Manifold Clustering.
Proceedings of the Manifold Learning and Its Applications, 2009


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