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07/05/2023
Meta-Learning Adversarial Bandit Algorithms
We study online meta-learning with bandit feedback, with the goal of imp...
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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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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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10/20/2022
Private Algorithms with Private Predictions
When applying differential privacy to sensitive data, a common way of ge...
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09/28/2022
Meta-Learning in Games
In the literature on game-theoretic equilibrium finding, focus has mainl...
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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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05/27/2022
Meta-Learning Adversarial Bandits
We study online learning with bandit feedback across multiple tasks, wit...
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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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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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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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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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05/03/2021
Initialization and Regularization of Factorized Neural Layers
Factorized layers–operations parameterized by products of two or more ma...
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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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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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02/25/2020
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
One popular trend in meta-learning is to learn from many training tasks ...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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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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06/06/2019
Adaptive Gradient-Based Meta-Learning Methods
We build a theoretical framework for understanding practical meta-learni...
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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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02/25/2019
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Recent empirical works have successfully used unlabeled data to learn fe...
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05/14/2018
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Motivations like domain adaptation, transfer learning, and feature learn...
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04/29/2017
Extending and Improving Wordnet via Unsupervised Word Embeddings
This work presents an unsupervised approach for improving WordNet that b...
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04/19/2017