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07/28/2023
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Even though machine learning (ML) pipelines affect an increasing array o...
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06/13/2023
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Machine learning (ML) models that achieve high average accuracy can stil...
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04/13/2023
Learning Personalized Decision Support Policies
Individual human decision-makers may benefit from different forms of sup...
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02/16/2023
Assisting Human Decisions in Document Matching
Many practical applications, ranging from paper-reviewer assignment in p...
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02/11/2023
Cross-Modal Fine-Tuning: Align then Refine
Fine-tuning large-scale pretrained models has led to tremendous progress...
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02/09/2023
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
Machine learning models with high accuracy on test data can still produc...
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12/17/2022
On Noisy Evaluation in Federated Hyperparameter Tuning
Hyperparameter tuning is critical to the success of federated learning a...
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08/25/2022
SONAR: Joint Architecture and System Optimization Search
There is a growing need to deploy machine learning for different tasks o...
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07/20/2022
Provably tuning the ElasticNet across instances
An important unresolved challenge in the theory of regularization is to ...
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07/08/2022
Evaluating Systemic Error Detection Methods using Synthetic Images
We introduce SpotCheck, a framework for generating synthetic datasets to...
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06/24/2022
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Machine Learning (ML) models now inform a wide range of human decisions,...
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06/05/2022
Use-Case-Grounded Simulations for Explanation Evaluation
A growing body of research runs human subject evaluations to study wheth...
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05/27/2022
AANG: Automating Auxiliary Learning
When faced with data-starved or highly complex end-tasks, it is commonpl...
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05/13/2022
Perspectives on Incorporating Expert Feedback into Model Updates
Machine learning (ML) practitioners are increasingly tasked with develop...
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04/15/2022
Efficient Architecture Search for Diverse Tasks
While neural architecture search (NAS) has enabled automated machine lea...
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02/18/2022
Learning Predictions for Algorithms with Predictions
A burgeoning paradigm in algorithm design is the field of algorithms wit...
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12/12/2021
Bayesian Persuasion for Algorithmic Recourse
When subjected to automated decision-making, decision subjects may strat...
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10/12/2021
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search
Most existing neural architecture search (NAS) benchmarks and algorithms...
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09/15/2021
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Pre-training, where models are trained on an auxiliary objective with ab...
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08/19/2021
Learning-to-learn non-convex piecewise-Lipschitz functions
We analyze the meta-learning of the initialization and step-size of lear...
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06/08/2021
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Tuning hyperparameters is a crucial but arduous part of the machine lear...
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06/03/2021
Finding and Fixing Spurious Patterns with Explanations
Machine learning models often use spurious patterns such as "relying on ...
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05/13/2021
Sanity Simulations for Saliency Methods
Saliency methods are a popular class of feature attribution tools that a...
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03/29/2021
Rethinking Neural Operations for Diverse Tasks
An important goal of neural architecture search (NAS) is to automate-awa...
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03/10/2021
Towards Connecting Use Cases and Methods in Interpretable Machine Learning
Despite increasing interest in the field of Interpretable Machine Learni...
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02/26/2021
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
We empirically demonstrate that full-batch gradient descent on neural ne...
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01/30/2021
On Data Efficiency of Meta-learning
Meta-learning has enabled learning statistical models that can be quickl...
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11/02/2020
A Learning Theoretic Perspective on Local Explainability
In this paper, we explore connections between interpretable machine lear...
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04/16/2020
Geometry-Aware Gradient Algorithms for Neural Architecture Search
Many recent state-of-the-art methods for neural architecture search (NAS...
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04/07/2020
Model-Agnostic Characterization of Fairness Trade-offs
There exist several inherent trade-offs in designing a fair model, such ...
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03/03/2020
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional repr...
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01/07/2020
FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massive...
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09/12/2019
Differentially Private Meta-Learning
Parameter-transfer is a well-known and versatile approach for meta-learn...
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08/21/2019
Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devi...
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06/27/2019
Learning Fair Representations for Kernel Models
Fair representations are a powerful tool for establishing criteria like ...
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06/06/2019
Adaptive Gradient-Based Meta-Learning Methods
We build a theoretical framework for understanding practical meta-learni...
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05/31/2019
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on design...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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03/22/2019
On the support recovery of marginal regression
Leading methods for support recovery in high-dimensional regression, suc...
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03/12/2019
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning
Hyperparameter tuning of multi-stage pipelines introduces a significant ...
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02/27/2019
Provable Guarantees for Gradient-Based Meta-Learning
We study the problem of meta-learning through the lens of online convex ...
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02/20/2019
Random Search and Reproducibility for Neural Architecture Search
Neural architecture search (NAS) is a promising research direction that ...
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02/18/2019
Regularizing Black-box Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
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12/18/2018
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Communication on heterogeneous edge networks is a fundamental bottleneck...
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12/14/2018
On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
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12/03/2018
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, ...
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10/13/2018
Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces....
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07/09/2018
Supervised Local Modeling for Interpretability
Model interpretability is an increasingly important component of practic...
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07/03/2017
Parle: parallelizing stochastic gradient descent
We propose a new algorithm called Parle for parallel training of deep ne...
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05/30/2017