47 citations · 57 across the 9 of their papers we have counts for
11 papers
GUIDE: Guided Initialization and Distillation of Embeddings
Khoa Trinh, Gaurav Menghani, Erik Vee
Algorithmic efficiency techniques such as distillation (\cite{hinton2015distillation}) are useful in improving model quality without increasing serving costs, provided a larger tea…
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh +3
Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled dat…
Improved Bi-point Rounding Algorithms and a Golden Barrier for -Median
Kishen N. Gowda, Thomas Pensyl, Aravind Srinivasan +1
The current best approximation algorithms for -median rely on first obtaining a structured fractional solution known as a bi-point solution, and then rounding it to an integer s…
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal +3
Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher''…
Robust Active Distillation
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos +2
Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting…
Approximation algorithms for stochastic clustering
David G. Harris, Shi Li, Thomas Pensyl +2
We consider stochastic settings for clustering, and develop provably-good approximation algorithms for a number of these notions. These algorithms yield better approximation ratios…
A Lottery Model for Center-type Problems With Outliers
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
In this paper, we give tight approximation algorithms for the -center and matroid center problems with outliers. Unfairness arises naturally in this setting: certain clients cou…
Dependent randomized rounding for clustering and partition systems with knapsack constraints
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that n…
Fairness in Resource Allocation and Slowed-down Dependent Rounding
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
We consider an issue of much current concern: could fairness, an issue that is already difficult to guarantee, worsen when algorithms run much of our lives? We consider this in the…
An Improved Approximation for -median, and Positive Correlation in Budgeted Optimization
Jarosław Byrka, Thomas Pensyl, Bartosz Rybicki +2
Dependent rounding is a useful technique for optimization problems with hard budget constraints. This framework naturally leads to \emph{negative correlation} properties. However,…
'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators
Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah +27
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open…