Search Results for author: Sanmi Koyejo

Found 33 papers, 9 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

FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation

no code implementations20 Jul 2023 Dhruv Pai, Andres Carranza, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo

We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks.

Anomaly Detection

Deceptive Alignment Monitoring

no code implementations20 Jul 2023 Andres Carranza, Dhruv Pai, Rylan Schaeffer, Arnuv Tandon, Sanmi Koyejo

As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves.

Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting

no code implementations20 Jul 2023 Rylan Schaeffer, Kateryna Pistunova, Samar Khanna, Sarthak Consul, Sanmi Koyejo

We find that the logically \textit{invalid} reasoning prompts do indeed achieve similar performance gains on BBH tasks as logically valid reasoning prompts.

Language Modelling

Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data

no code implementations24 Jun 2023 Alycia Lee, Brando Miranda, Sudharsan Sundar, Sanmi Koyejo

Current trends to pre-train capable Large Language Models (LLMs) mostly focus on scaling of model and dataset size.

Is Pre-training Truly Better Than Meta-Learning?

no code implementations24 Jun 2023 Brando Miranda, Patrick Yu, Saumya Goyal, Yu-Xiong Wang, Sanmi Koyejo

Using this analysis, we demonstrate the following: 1. when the formal diversity of a data set is low, PT beats MAML on average and 2. when the formal diversity is high, MAML beats PT on average.

Few-Shot Learning

Communication-Efficient Federated Learning through Importance Sampling

no code implementations22 Jun 2023 Berivan Isik, Francesco Pase, Deniz Gunduz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi

The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL).

Federated Learning

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions

no code implementations1 Jun 2023 Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo

We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions.


Are Emergent Abilities of Large Language Models a Mirage?

1 code implementation28 Apr 2023 Rylan Schaeffer, Brando Miranda, Sanmi Koyejo

Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models.

Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa

no code implementations5 Apr 2023 Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa, Sanmi Koyejo, Katherine Heller

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose.


Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation

no code implementations1 Mar 2023 Pedro Cisneros-Velarde, Sanmi Koyejo

Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium of an underlying general-sum Markov game.

Multi-agent Reinforcement Learning Q-Learning

Adapting to Latent Subgroup Shifts via Concepts and Proxies

no code implementations21 Dec 2022 Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai

We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.

Unsupervised Domain Adaptation

Latent Multimodal Functional Graphical Model Estimation

no code implementations31 Oct 2022 Katherine Tsai, Boxin Zhao, Sanmi Koyejo, Mladen Kolar

Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences.

Invariant Aggregator for Defending against Federated Backdoor Attacks

no code implementations4 Oct 2022 Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo, Shruti Tople

Empirical results on three datasets with different modalities and varying numbers of clients further demonstrate that our approach mitigates a broad class of backdoor attacks with a negligible cost on the model utility.

Federated Learning Model Optimization

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

no code implementations2 Aug 2022 Brando Miranda, Patrick Yu, Yu-Xiong Wang, Sanmi Koyejo

This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison.

Few-Shot Learning Transfer Learning

One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning

no code implementations31 May 2022 Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar

Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically.

Reinforcement Learning (RL)

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

1 code implementation NAACL 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

1 code implementation31 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.


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

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

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

1 code implementation 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.

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

3 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.

BIG-bench Machine Learning

Asynchronous Federated Optimization

1 code implementation10 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 +1

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