Publications

ML=Machine Learning/Statistics/Artificial Intelligence, BI=Biological Imaging, NS=Neuroscience
* Indicates equal contribution.

Selected Preprints and Abstracts

[ML] Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates
Cong Xie, Sanmi Koyejo, Indranil Gupta, and Haibin Lin
Preprint, 2019
[arXiv]

[ML, NS] Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity
Sen Na, Mladen Kolar, and Oluwasanmi Koyejo.
Preprint, 2019
[arXiv]

[ML] Consistent Classification with Generalized Metrics
Xiaoyan Wang, Ran Li, Bowei Yan, and Oluwasanmi Koyejo.
Preprint, 2019
[arXiv]

[ML] Zeno: Robust Asynchronous SGD with an Arbitrary Number of Byzantine Workers
Cong Xie, Sanmi Koyejo, and Indranil Gupta
Preprint, 2019
[arXiv]

[ML] Asynchronous Federated Optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta
Preprint, 2019
[arXiv]

[ML] On the Consistency of Top-k Surrogate Losses
Forest Yang and Sanmi Koyejo
Preprint, 2019
[arXiv]

[NS, BI] Informing Bayesian Functional Connectivity Modeling with Structural Connectivity Priors
Sameer Manchanda, Carolyn Murray, Sanmi Koyejo
Organization for Human Brain Mapping Annual Meeting (OHBM), 2019
[url]

[BI] Generative Neural Networks: Synthesizing a Complete Tomographic Study from a Single Frontal Radiograph
P. Cole, A. Chen, S. Sundararaman, A. Kadimisetty, N. A. Siddiqui, S. Koyejo, et. al.
Radiological Society of North America Annual Meeting (RSNA), 2019

[BI] Generative adversarial neural networks in the creation of synthetic chest radiographs: Can we fool the experts?
Ishan Deshpande, Alexander G. Schwing, Sanmi Koyejo, Nasir A. Siddiqui, Ayis T. Pyrros, and David A. Forsyth
Radiological Society of North America Annual Meeting (RSNA), 2018

Technical & Scientific Publications

[ML, BI, NS] Towards a Deep Network Architecture for Structured Smoothness
Haroun Habeeb and Oluwasanmi Koyejo
International Conference on Learning Representations (ICLR), 2020
[url]

[ML] Multiclass Performance Metric Elicitation
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, and Oluwasanmi Koyejo
Neural Information Processing Systems (NeurIPS), 2019
[url]

[ML] Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo
Neural Information Processing Systems (NeurIPS), 2019
[arXiv]

[ML, BI, NS] Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging
Peiye Zhuang, Bliss Chapman, Ran Li, and Sanmi Koyejo
Asilomar Conference on Signals, Systems, and Computers (Asilomar), 2019

[ML] Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance
Cong Xie, Sanmi Koyejo, and Indranil Gupta
International Conference on Machine Learning (ICML), pages 6893-6901, 2019
[url]

[ML, NS] Partially linear additive Gaussian graphical models
Sinong Geng, Minhao Yan, Mladen Kolar, and Sanmi Koyejo.
International Conference on Machine Learning (ICML) pages 2180-2190, 2019
[url]

[ML] Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation
Cong Xie, Sanmi Koyejo, and Indranil Gupta
Conference on Uncertainty in Artificial Intelligence (UAI), 2019
[arXiv]

[ML, NS] Joint nonparametric precision matrix estimation with confounding
Sinong Geng, Mladen Kolar, and Sanmi Koyejo.
Uncertainty in Artificial Intelligence (UAI), 2019
[arXiv]

[ML] Practical distributed learning: Secure machine learning with communication-efficient local updates
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2019
[arXiv]

[ML, BI, NS] FMRI data augmentation via synthesis
Peiye Zhuang Alexander Schwing and Oluwasanmi Koyejo
International Symposium on Biomedical Imaging (ISBI), 2019
[arXiv]

[ML, BI] Max-sliced wasserstein distance and its use for GANs
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander Schwing
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
[PDF]

[ML] Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim and Joydeep Ghosh
International Conference on Learning Representations; Workshop on Safe ML, 2019
[url]

[ML] Aggregation for Sensitive Data
Avradeep Bhowmik, Joydeep Ghosh, and Oluwasanmi Koyejo
13th International conference on Sampling Theory and Applications (SampTA), 2019
[url]

[ML, NS] Dependent relevance determination for smooth and structured sparse regression
Anqi Wu, Oluwasanmi Koyejo, and Jonathan Pillow
Journal of Machine Learning Research, 20:89:1-89:43, 2019
[url]

[ML] Performance metric elicitation from pairwise classifier comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, and Oluwasanmi Koyejo
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[arXiv]

[ML] Interpreting black box predictions using Fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Oluwasanmi Koyejo
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[arXiv]

[NS] Human cognition involves the dynamic integration of neural activity and neuromodulatory systems
James M Shine, Michael Breakspear, Peter T Bell, Kayla Ehgoetz Martens, Richard Shine, Oluwasanmi Koyejo, Olaf Sporns, and Russell A Poldrack
Nature Neuroscience, 2019
[url]

[ML] Clustered monotone transforms for rating factorization
Raghav Somani, Gaurush Hiranandani, Oluwasanmi Koyejo, and Sreangsu Acharyya
ACM International Conference on Web Search and Data Mining (WSDM), 2019
[arXiv] [code]

[ML] Clustered Fused Graphical Lasso
Yizhi Zhu and Oluwasanmi Koyejo
Conference on Uncertainty in Artificial Intelligence (UAI), 2018
[pdf]

[ML] Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong and Pradeep Ravikumar
International Conference on Machine Learning (ICML), 2018
[url]

[NS, ML] Bayesian structure learning for dynamic brain connectivity
Michael Riis Andersen, Lars Kai Hansen, Ole Winther, Russell A. Poldrack, and Oluwasanmi Koyejo
International conference on Artificial Intelligence and Statistics (AISTATS), 2018
[url]

[NS, BI] MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Oscar Esteban, Daniel Birman, Marie Schaer, Oluwasanmi O Koyejo, Russell A Poldrack, and Krzysztof J Gorgolewski
PLoS One, 12(9):e0184661, 2017
[url] [arXiv] [code]

[NS] Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition
Timothy N Rubin, Oluwasanmi Koyejo, Krzysztof J Gorgolewski, Michael N Jones, Russell A Poldrack, and Tal Yarkoni
PLOS Computational Biology, 2017
[arXiv]

[ML] Consistency Analysis for Binary Classification Revisited
Krzysztof Dembczynski, Wojciech Kotlowski, Oluwasanmi Koyejo, and Nagarajan Natarajan
International Conference on Machine Learning (ICML), 2017
[url]

[NS, ML] False Discovery Rate Smoothing
Wesley Tansey, Oluwasanmi Koyejo, Russell A. Poldrack and James G. Scott
Journal of the American Statistical Association (JASA): Theory and Methods, 2017
[arXiv] [code]

[ML] Frequency Domain Predictive Modeling with Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
Proceedings of the 20th International conference on Artificial Intelligence and Statistics (AISTATS), 2017
[url]

[ML, NS] Information Projection and Approximate Inference for Structured Sparse Variables
Rajiv Khanna, Joydeep Ghosh, Rusell Poldrack, Oluwasanmi Koyejo
Proceedings of the 20th International conference on Artificial Intelligence and Statistics (AISTATS), 2017
[url] [prelim. arXiv]

[ML, NS] A Deflation Method for Structured Probabilistic PCA
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, and Oluwasanmi Koyejo
Proceedings of the SIAM International Conference on Data Mining (SDM), 2017
[pdf]

[NS, ML] What's in a pattern? Examining the Type of Signal Multivariate Analysis Uncovers At the Group Level
Roee Gilron, Jonathan Rosenblatt, Oluwasanmi Koyejo, Russell A. Poldrack, and Roy Mukamel
NeuroImage (2017)
[url]

[NS] The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance
J.M. Shine, P.G. Bissett, P.T. Bell, O. Koyejo, J.H. Balsters, K.J. Gorgolewski, C.A. Moodie and R. A. Poldrack
Neuron (2016)
[url] [arXiv] [code]

[NS] Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention
James M. Shine, Oluwasanmi Koyejo, and Russell A. Poldrack
Proceedings of the National Academy of Sciences (2016): 201604898
[url] [arXiv] [code]

[ML] Examples are not Enough, Learn to Criticize! Criticism for Interpretable Machine Learning
Been Kim*, Rajiv Khanna* and Oluwasanmi Koyejo*
Advances in Neural Information Processing Systems (NIPS) 29, 2016 (Oral)
[url] [code]

[ML] Preference Completion from Partial Rankings
Suriya Gunasekar, Oluwasanmi Koyejo, and Joydeep Ghosh
Advances in Neural Information Processing Systems (NIPS) 29, 2016
[url] [code]

[NS, ML] Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Timothy Rubin, Oluwasanmi Koyejo, Michael Jones, and Tal Yarkoni
Advances in Neural Information Processing Systems (NIPS) 29, 2016
[url] [code]

[ML] Sparse parameter recovery from aggregated data
Avradeep Bhowmik, Joydeep Ghosh, and Oluwasanmi Koyejo
International Conference on Machine Learning (ICML), 2016
[url]

[ML] Optimal classification with multivariate losses
Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep K Ravikumar, and Inderjit S Dhillon
International Conference on Machine Learning (ICML), 2016
[url] [prelim. arXiv]

[ML, NS] A simple and provable algorithm for sparse CCA
Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, and Russell A Poldrack
International Conference on Machine Learning (ICML), 2016
[url] [code]

[ML] Renyi divergence minimization based co-regularized multiview clustering
Shalmali Joshi, Joydeep Ghosh, Mark Reid, and Oluwasanmi Koyejo
Machine Learning, 2016
[url]

[NS] Long-term neural, behavioral, and physiological phenotyping of a single human: The myconnectome project
R. Poldrack, T. Laumann, O. Koyejo, B. Gregory, A. Hover, M.-Y. Chen, K. Gorgolewski, J. Luci, S.J. Joo, R. Boyd, S. Hunicke-Smith, Z. Simpson, T. Caven, V. Sochat, J. Shine, E. Gordon, A. Snyder, B. Adeyemo, S. Petersen, D. Glahn, D. McKay, J. Curran, H. Goring, M. Carless, J. Blangero, R. Dougherty, A. Leemans, D. Handwerker, L. Frick, E. Marcotte, J. Mumford
Nature Communications, 2015
[url] [web] [code]

[ML] Consistent multilabel classification
Oluwasanmi Koyejo*, Nagarajan Natarajan*, Pradeep Ravikumar, and Inderjit Dhillon
Advances in Neural Information Processing Systems (NIPS) 28, 2015
[url]

[NS] Effects of thresholding on correlation-based image similarity metrics
Vanessa V Sochat, Krzysztof J. Gorgolewski, Oluwasanmi Koyejo, Joke Durnez and Russell A Poldrack
Frontiers in Neuroscience, 9:418, 2015
[url] [code]

[ML] Simultaneous prognosis and exploratory analysis of multiple chronic conditions using clinical notes
Shalmali Joshi, Oluwasanmi Koyejo, Kristine Resurreccion, and Joydeep Ghosh
IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015), 2015
[url]

[NS, ML] Estimation of dynamic functional connectivity using multiplicative analytical coupling
James M Shine, Oluwasanmi Koyejo, Peter T Bell, Krzysztov J Gorgolewski, Moran Gilat, and Russell A Poldrack
NeuroImage, 122:399-407, 2015
[url] [code]

[ML] Generalized linear models for aggregated data
Avradeep Bhowmik, Joydeep Ghosh, and Oluwasanmi Koyejo
Proceedings of the 18th International conference on Artificial Intelligence and Statistics (AISTATS), 2015 (Oral)
[url]

[ML, NS] Sparse submodular probabilistic PCA
Rajiv Khanna, Joydeep Ghosh, Russell A. Poldrack, and Oluwasanmi Koyejo
Proceedings of the 18th International conference on Artificial Intelligence and Statistics (AISTATS), 2015 (Oral)
[url]

[ML] Consistent binary classification with generalized performance metrics
Oluwasanmi Koyejo*, Nagarajan Natarajan*, Pradeep Ravikumar, and Inderjit Dhillon
Advances in Neural Information Processing Systems (NIPS) 27, 2014 (Spotlight)
[url]

[ML, NS] On prior distributions and approximate inference for structured variables
Oluwasanmi Koyejo, Rajiv Khanna, Joydeep Ghosh, and Russell A Poldrack
Advances in Neural Information Processing Systems (NIPS) 27, 2014
[url]

[ML, NS] Sparse dependent Bayesian structure learning
Anqi Wu, Mijung Park, Oluwasanmi Koyejo, and Jonathan Pillow
Advances in Neural Information Processing Systems (NIPS) 27, 2014
[url]

[ML] A constrained matrix-variate Gaussian process for transposable data
Oluwasanmi Koyejo, Cheng Lee, and Joydeep Ghosh
Machine Learning, 2014
[url] [arXiv]

[ML, NS] Constrained Bayesian inference for low rank multitask learning
Oluwasanmi Koyejo and Joydeep Ghosh
Proceedings of the 29th conference on Uncertainty in artificial intelligence (UAI), 2013
Awarded Amazon best student paper
[pdf] [arXiv]

[NS] Towards open sharing of task-based fMRI data: The OpenfMRI project
R. A. Poldrack, D. M. Barch, J. P. Mitchell, T. D. Wager, A. D. Wagner, J. T. Devlin, C. Cumba, O. Koyejo, and M. P. Milham
Frontiers in Neuroinformatics, 2013
[url] [web]

[ML, NS] Bayesian structure learning for functional neuroimaging
Mijung Park*, Oluwasanmi Koyejo*, Joydeep Ghosh, Russell A. Poldrack, and Jonathan W. Pillow
International Conference on Artificial Intelligence and Statistics (AISTATS), 2013
[url]

[ML] Identifying candidate disease genes using a trace norm constrained bipartite ranking model
Cheng Lee, Oluwasanmi Koyejo, and Joydeep Ghosh
Engineering in Medicine and Biology Society (EMBC), 2013
[pdf]

[ML, NS] Learning predictive cognitive structure from fMRI using supervised topic models
Oluwasanmi Koyejo, Priyank Patel, Joydeep Ghosh, and Russell A. Poldrack
International Workshop on Pattern Recognition in NeuroImaging (PRNI), 2013
[pdf]

[ML] Retargeted matrix factorization for collaborative filtering
Oluwasanmi Koyejo, Sreangsu Acharyya, and Joydeep Ghosh
Proceedings of the 7th ACM conference on Recommender systems (RecSys ’13). ACM, New York, NY, USA, 49-56
[url]

[ML] Constrained Gaussian Process Regression for Gene-Disease Association
Oluwasanmi Koyejo, Cheng Lee, and Joydeep Ghosh
ICDM Workshop on Biological Data Mining and its Applications in Healthcare, 2013
[url]

[ML, NS] Decoding cognitive processes from functional MRI
Oluwasanmi Koyejo and Russell A. Poldrack
NIPS Workshop on Machine Learning and Interpretation in Neuroimaging, 2013
[pdf]

[ML] Learning to rank with Bregman divergences and monotone retargeting
Sreangsu Acharyya*, Oluwasanmi Koyejo*, and Joydeep Ghosh
Proceedings of the 28th conference on Uncertainty in artificial intelligence (UAI), 2012
[pdf] [arXiv]

[ML] A kernel-based approach to exploiting interaction-networks in heterogeneous information sources for improved recommender systems
Oluwasanmi Koyejo, and Joydeep Ghosh
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HETREC). ACM, 2011
[pdf]

[ML] MiPPS; a generative model for multi-manifold clustering. In AAAI Fall Symposium on Manifold Learning and Its Applications
Oluwasanmi Koyejo and Joydeep Ghosh
AAAI Fall Symposium on Manifold Learning and Its Applications. AAAI Press, 2009
[pdf]

Thesis

Constrained relative entropy minimization with applications to multitask learning
Oluwasanmi Koyejo
University of Texas at Austin, 2013
[url]

See CV for other publications and materials.