204 citations · 476 across the 32 of their papers we have counts for
8 papers · 1 filter
On Noisy Evaluation in Federated Hyperparameter Tuning
Kevin Kuo, Pratiksha Thaker, Mikhail Khodak +4
Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks. Is…
Learning-Augmented Private Algorithms for Multiple Quantile Release
Mikhail Khodak, Kareem Amin, Travis Dick +1
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors. We pro…
Meta-Learning in Games
Keegan Harris, Ioannis Anagnostides, Gabriele Farina +3
In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from r…
Provably tuning the ElasticNet across instances
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma +1
An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarante…
Meta-Learning Adversarial Bandits
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak +1
We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-…
AANG: Automating Auxiliary Learning
Lucio M. Dery, Paul Michel, Mikhail Khodak +2
Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whil…
Efficient Architecture Search for Diverse Tasks
Junhong Shen, Mikhail Khodak, Ameet Talwalkar
While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-exp…
Learning Predictions for Algorithms with Predictions
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar +1
A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms can take advantage of a possibly-imperfect prediction of some aspect of t…