Search Results for author: Sanmi Koyejo

Found 17 papers, 4 papers with code

Optimization and Analysis of the pAp@k Metric for Recommender Systems

no code implementations ICML 2020 Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain

Modern recommendation and notification systems must be robust to data imbalance, limitations on the number of recommendations/notifications, and heterogeneous engagement profiles across users.

Recommendation Systems

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

1 code implementation1 May 2022 Yong Xie, Dakuo Wang, Pin-Yu Chen, JinJun Xiong, Sijia Liu, Sanmi Koyejo

More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.

Adversarial Attack Combinatorial Optimization +1

Fair Wrapping for Black-box Predictions

no code implementations31 Jan 2022 Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie

We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.


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

no code implementations24 Dec 2021 Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo

We hypothesize that the diversity coefficient of the few-shot learning benchmark is predictive of whether meta-learning solutions will succeed or not.

Few-Shot Learning Transfer Learning

Does MAML Only Work via Feature Re-use? A Data Centric Perspective

1 code implementation24 Dec 2021 Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo

Recent work has suggested that a good embedding is all we need to solve many few-shot learning benchmarks.

Few-Shot Learning online learning

EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision

no code implementations10 Jul 2020 Siddharth Biswal, Peiye Zhuang, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Jimeng Sun

EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text.

Data Augmentation Image Classification

Fairness with Overlapping Groups

no code implementations24 Jun 2020 Forest Yang, Moustapha Cisse, Sanmi Koyejo

In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously.

Classification Fairness +1

FMRI data augmentation via synthesis

no code implementations13 Jul 2019 Peiye Zhuang, Alexander G. Schwing, Sanmi Koyejo

Thus, our results suggest that data augmentation via synthesis is a promising approach to address the limited availability of fMRI data, and to improve the quality of predictive fMRI models.

Data Augmentation

Max-Sliced Wasserstein Distance and its use for GANs

no code implementations CVPR 2019 Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, Alexander Schwing

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.

Image-to-Image Translation Translation

Zeno++: Robust Fully Asynchronous SGD

no code implementations ICML 2020 Cong Xie, Sanmi Koyejo, Indranil Gupta

We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers.

SLSGD: Secure and Efficient Distributed On-device Machine Learning

no code implementations16 Mar 2019 Cong Xie, Sanmi Koyejo, Indranil Gupta

We consider distributed on-device learning with limited communication and security requirements.

Data Poisoning Distributed Optimization

Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation

no code implementations10 Mar 2019 Cong Xie, Sanmi Koyejo, Indranil Gupta

Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning.

Asynchronous Federated Optimization

no code implementations10 Mar 2019 Cong Xie, Sanmi Koyejo, Indranil Gupta

Federated learning enables training on a massive number of edge devices.

Federated Learning

On the Consistency of Top-k Surrogate Losses

no code implementations ICML 2020 Forest Yang, Sanmi Koyejo

Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and suggests no convex hinge loss is top-$k$ calibrated.

General Classification

A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials

no code implementations1 Sep 2018 Yogatheesan Varatharajah, Brent Berry, Sanmi Koyejo, Ravishankar Iyer

However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies.

Decision Making reinforcement-learning

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