Oluwasanmi Koyejo

research

∙ 06/02/2023

Layer-Wise Feedback Alignment is Conserved in Deep Neural Networks

In the quest to enhance the efficiency and bio-plausibility of training ...

0 Zachary Robertson, et al. ∙

research

∙ 06/02/2023

No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions

The growing demand for data and AI-generated digital goods, such as pers...

0 Zachary Robertson, et al. ∙

research

∙ 03/24/2023

Double Descent Demystified: Identifying, Interpreting Ablating the Sources of a Deep Learning Puzzle

Double descent is a surprising phenomenon in machine learning, in which ...

0 Rylan Schaeffer, et al. ∙

research

∙ 02/10/2023

Federated Domain Adaptation via Gradient Projection

Federated Domain Adaptation (FDA) describes the federated learning setti...

0 Enyi Jiang, et al. ∙

research

∙ 12/21/2022

Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations

We propose a Target Conditioned Representation Independence (TCRI) objec...

0 Olawale Salaudeen, et al. ∙

research

∙ 12/07/2022

Metric Elicitation; Moving from Theory to Practice

Metric Elicitation (ME) is a framework for eliciting classification metr...

0 Safinah Ali, et al. ∙

research

∙ 11/01/2022

Batch Active Learning from the Perspective of Sparse Approximation

Active learning enables efficient model training by leveraging interacti...

0 Maohao Shen, et al. ∙

research

∙ 10/31/2022

Latent Multimodal Functional Graphical Model Estimation

Joint multimodal functional data acquisition, where functional data from...

0 Katherine Tsai, et al. ∙

research

∙ 10/11/2022

Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging

In the experimental sciences, statistical power analyses are often used ...

0 Peiye Zhuang, et al. ∙

research

∙ 10/11/2022

Controllable Radiance Fields for Dynamic Face Synthesis

Recent work on 3D-aware image synthesis has achieved compelling results ...

0 Peiye Zhuang, et al. ∙

research

∙ 05/31/2022

A Reduction to Binary Approach for Debiasing Multiclass Datasets

We propose a novel reduction-to-binary (R2B) approach that enforces demo...

14 Ibrahim Alabdulmohsin, et al. ∙

research

∙ 02/03/2022

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

Machine learning (ML) robustness and domain generalization are fundament...

0 Xiaojun Xu, et al. ∙

research

∙ 02/02/2022

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

Fairness and robustness are often considered as orthogonal dimensions wh...

31 Jessica Schrouff, et al. ∙

research

∙ 10/19/2021

Joint Gaussian Graphical Model Estimation: A Survey

Graphs from complex systems often share a partial underlying structure a...

0 Katherine Tsai, et al. ∙

research

∙ 10/06/2021

Secure Byzantine-Robust Distributed Learning via Clustering

Federated learning systems that jointly preserve Byzantine robustness an...

0 Raj Kiriti Velicheti, et al. ∙

research

∙ 02/18/2021

Optimizing Black-box Metrics with Iterative Example Weighting

We consider learning to optimize a classification metric defined by a bl...

0 Gaurush Hiranandani, et al. ∙

research

∙ 02/01/2021

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

Controllable semantic image editing enables a user to change entire imag...

10 Peiye Zhuang, et al. ∙

research

∙ 11/11/2020

A Nonconvex Framework for Structured Dynamic Covariance Recovery

We propose a flexible yet interpretable model for high-dimensional data ...

0 Katherine Tsai, et al. ∙

research

∙ 11/03/2020

Quadratic Metric Elicitation with Application to Fairness

Metric elicitation is a recent framework for eliciting performance metri...

0 Gaurush Hiranandani, et al. ∙

research

∙ 07/26/2020

CSER: Communication-efficient SGD with Error Reset

The scalability of Distributed Stochastic Gradient Descent (SGD) is toda...

2 Cong Xie, et al. ∙

research

∙ 07/01/2020

Bayesian Coresets: An Optimization Perspective

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

0 Jacky Y. Zhang, et al. ∙

research

∙ 06/25/2020

Does Adversarial Transferability Indicate Knowledge Transferability?

Despite the immense success that deep neural networks (DNNs) have achiev...

0 Kaizhao Liang, et al. ∙

research

∙ 06/23/2020

Fair Performance Metric Elicitation

What is a fair performance metric? We consider the choice of fairness me...

0 Gaurush Hiranandani, et al. ∙

research

∙ 01/28/2020

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

We study the standard problem of recommending relevant items to users; a...

0 Amar Budhiraja, et al. ∙

research

∙ 01/22/2020

Towards A Controllable Disentanglement Network

This paper addresses two crucial problems of learning disentangled image...

19 Zengjie Song, et al. ∙

research

∙ 12/25/2019

Learning Controllable Disentangled Representations with Decorrelation Regularization

A crucial problem in learning disentangled image representations is cont...

32 Zengjie Song, et al. ∙

research

∙ 11/20/2019

Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates

Recent years have witnessed the growth of large-scale distributed machin...

0 Cong Xie, et al. ∙

research

∙ 10/29/2019

Learning Sparse Distributions using Iterative Hard Thresholding

Iterative hard thresholding (IHT) is a projected gradient descent algori...

0 Jacky Y. Zhang, et al. ∙

research

∙ 09/12/2019

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Differential graphical models are designed to represent the difference b...

0 Sen Na, et al. ∙

research

∙ 08/24/2019

Consistent Classification with Generalized Metrics

We propose a framework for constructing and analyzing multiclass and mul...

0 Xiaoyan Wang, et al. ∙

research

∙ 07/22/2019

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Machine learning based decision making systems are increasingly affectin...

3 Shalmali Joshi, et al. ∙

research

∙ 06/08/2019

Partially Linear Additive Gaussian Graphical Models

We propose a partially linear additive Gaussian graphical model (PLA-GGM...

2 Sinong Geng, et al. ∙

research

∙ 10/31/2018

Clustered Monotone Transforms for Rating Factorization

Exploiting low-rank structure of the user-item rating matrix has been th...

0 Gaurush Hiranandani, et al. ∙

research

∙ 10/23/2018

Interpreting Black Box Predictions using Fisher Kernels

Research in both machine learning and psychology suggests that salient e...

5 Rajiv Khanna, et al. ∙

research

∙ 10/16/2018

Joint Nonparametric Precision Matrix Estimation with Confounding

We consider the problem of precision matrix estimation where, due to ext...

0 Sinong Geng, et al. ∙

research

∙ 06/22/2018

xGEMs: Generating Examplars to Explain Black-Box Models

This work proposes xGEMs or manifold guided exemplars, a framework to un...

0 Shalmali Joshi, et al. ∙

research

∙ 06/05/2018

Eliciting Binary Performance Metrics

Given a binary prediction problem, which performance metric should the c...

0 Gaurush Hiranandani, et al. ∙

research

∙ 06/02/2018

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Complex performance measures, beyond the popular measure of accuracy, ar...

0 Bowei Yan, et al. ∙

research

∙ 05/25/2018

Zeno: Byzantine-suspicious stochastic gradient descent

We propose Zeno, a new robust aggregation rule, for distributed synchron...

0 Cong Xie, et al. ∙

research

∙ 05/23/2018

Phocas: dimensional Byzantine-resilient stochastic gradient descent

We propose a novel robust aggregation rule for distributed synchronous S...

0 Cong Xie, et al. ∙

research

∙ 02/27/2018

Generalized Byzantine-tolerant SGD

We propose three new robust aggregation rules for distributed synchronou...

0 Cong Xie, et al. ∙

research

∙ 11/28/2017

Dependent relevance determination for smooth and structured sparse regression

In many problem settings, parameter vectors are not merely sparse, but d...

0 Anqi Wu, et al. ∙

research

∙ 11/14/2016

Preference Completion from Partial Rankings

We propose a novel and efficient algorithm for the collaborative prefere...

0 Suriya Gunasekar, et al. ∙

research

∙ 10/23/2016

Online Classification with Complex Metrics

We present a framework and analysis of consistent binary classification ...

0 Bowei Yan, et al. ∙

research

∙ 07/12/2016

Information Projection and Approximate Inference for Structured Sparse Variables

Approximate inference via information projection has been recently intro...

0 Rajiv Khanna, et al. ∙

research

∙ 05/29/2016

A simple and provable algorithm for sparse diagonal CCA

Given two sets of variables, derived from a common set of samples, spars...

0 Megasthenis Asteris, et al. ∙

research

∙ 05/14/2016

Generalized Linear Models for Aggregated Data

Databases in domains such as healthcare are routinely released to the pu...

0 Avradeep Bhowmik, et al. ∙

research

∙ 05/07/2015

Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics

We provide a general theoretical analysis of expected out-of-sample util...

0 Nagarajan Natarajan, et al. ∙

research

∙ 04/27/2014

A Constrained Matrix-Variate Gaussian Process for Transposable Data

Transposable data represents interactions among two sets of entities, an...

0 Oluwasanmi Koyejo, et al. ∙

research

∙ 09/26/2013

Constrained Bayesian Inference for Low Rank Multitask Learning

We present a novel approach for constrained Bayesian inference. Unlike c...

0 Oluwasanmi Koyejo, et al. ∙

Oluwasanmi Koyejo

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