5.1k citations · 9.6k across the 102 of their papers we have counts for
12 papers · 1 filter
Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Sik Kim +3
When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a fa…
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
Renbo Tu, Nicholas Roberts, Mikhail Khodak +3
Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet. This makes the performance o…
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Lucio M. Dery, Paul Michel, Ameet Talwalkar +1
In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks. However, widely used methods for levera…
Learning-to-learn non-convex piecewise-Lipschitz functions
Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma +1
We analyze the meta-learning of the initialization and step-size of learning algorithms for piecewise-Lipschitz functions, a non-convex setting with applications to both machine le…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li +4
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are…
Finding and Fixing Spurious Patterns with Explanations
Gregory Plumb, Marco Tulio Ribeiro, Ameet Talwalkar
Image classifiers often use spurious patterns, such as "relying on the presence of a person to detect a tennis racket, which do not generalize. In this work, we present an end-to-e…
Sanity Simulations for Saliency Methods
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Saliency methods are a popular class of feature attribution explanation methods that aim to capture a model's predictive reasoning by identifying "important" pixels in an input ima…
Rethinking Neural Operations for Diverse Tasks
Nicholas Roberts, Mikhail Khodak, Tri Dao +3
An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users…
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Valerie Chen, Jeffrey Li, Joon Sik Kim +2
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and t…
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
Jeremy M. Cohen, Simran Kaur, Yuanzhi Li +2
We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the…
On Data Efficiency of Meta-learning
Maruan Al-Shedivat, Liam Li, Eric Xing +1
Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks. Motivated by use-cases in personalized federated learning, we study the o…