5.1k citations · 9.6k across the 102 of their papers we have counts for
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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…
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…
SONAR: Joint Architecture and System Optimization Search
Elias Jääsaari, Michelle Ma, Ameet Talwalkar +1
There is a growing need to deploy machine learning for different tasks on a wide array of new hardware platforms. Such deployment scenarios require tackling multiple challenges, in…
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…
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera +1
A growing body of work studies Blindspot Discovery Methods ("BDM"s): methods that use an image embedding to find semantically meaningful (i.e., united by a human-understandable con…
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus +6
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robu…
Use-Case-Grounded Simulations for Explanation Evaluation
Valerie Chen, Nari Johnson, Nicholay Topin +2
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use…
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…
Perspectives on Incorporating Expert Feedback into Model Updates
Valerie Chen, Umang Bhatt, Hoda Heidari +2
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficie…
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…