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01/28/2023
Leveraging Importance Weights in Subset Selection
We present a subset selection algorithm designed to work with arbitrary ...
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07/29/2021
Batch Active Learning at Scale
The ability to train complex and highly effective models often requires ...
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06/04/2021
Churn Reduction via Distillation
In real-world systems, models are frequently updated as more data become...
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06/04/2021
Active Covering
We analyze the problem of active covering, where the learner is given an...
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10/11/2020
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, w...
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06/25/2020
An Analysis of SVD for Deep Rotation Estimation
Symmetric orthogonalization via SVD, and closely related procedures, are...
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12/02/2019
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels
We propose using active learning based techniques to further improve the...
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06/28/2019
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation
We tested in a live setting the use of active learning for selecting tex...
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04/30/2019
Categorical Feature Compression via Submodular Optimization
In the era of big data, learning from categorical features with very lar...
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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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10/13/2018
Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces....
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06/26/2018
The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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03/21/2016
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Performance of machine learning algorithms depends critically on identif...
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09/29/2015
Foundations of Coupled Nonlinear Dimensionality Reduction
In this paper we introduce and analyze the learning scenario of coupled ...
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08/09/2014
Matrix Coherence and the Nystrom Method
The Nystrom method is an efficient technique used to speed up large-scal...
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05/09/2012
Multiple Source Adaptation and the Renyi Divergence
This paper presents a novel theoretical study of the general problem of ...
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05/09/2012
L2 Regularization for Learning Kernels
The choice of the kernel is critical to the success of many learning alg...
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03/02/2012
Algorithms for Learning Kernels Based on Centered Alignment
This paper presents new and effective algorithms for learning kernels. I...
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02/14/2012
Ensembles of Kernel Predictors
This paper examines the problem of learning with a finite and possibly l...
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04/05/2011
Online and Batch Learning Algorithms for Data with Missing Features
We introduce new online and batch algorithms that are robust to data wit...
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12/17/2009