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20122026
most citedAre Emergent Abilities of Large Language Models a Mirage?

133 citations · 498 across the 111 of their papers we have counts for

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Showing 2019Show all

14 papers · 1 filter

cs.LG2019★ 2 cited

Learning Controllable Disentangled Representations with Decorrelation Regularization

Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang

A crucial problem in learning disentangled image representations is controlling the degree of disentanglement during image editing, while preserving the identity of objects. In thi…

cs.LG2019

Advances and Open Problems in Federated Learning

Peter Kairouz, H. Brendan McMahan, Brendan Avent +56

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…

cs.LG2019

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

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta +1

When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learnin…

stat.ML2019★ 3 cited

Learning Sparse Distributions using Iterative Hard Thresholding

Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1

Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…

math.ST2019

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Sen Na, Mladen Kolar, Oluwasanmi Koyejo

Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific i…

stat.ML2019

Consistent Classification with Generalized Metrics

Xiaoyan Wang, Ran Li, Bowei Yan +1

We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i.e., involving multiple, possibly correlated multiclass labels. Our analys…