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06/05/2023
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales ...
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03/01/2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
ML models are ubiquitous in real world applications and are a constant f...
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02/28/2023
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
The sheer size of modern neural networks makes model serving a serious c...
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01/31/2023
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets
Adversarial nets have proved to be powerful in various domains including...
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01/30/2023
Benchmarking Robustness to Adversarial Image Obfuscations
Automated content filtering and moderation is an important tool that all...
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05/19/2022
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
Decision tree ensembles are widely used and competitive learning models....
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02/10/2022
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
We present L0Learn: an open-source package for sparse linear regression ...
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04/14/2021
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
We present a new algorithmic framework for grouped variable selection th...
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04/17/2020
Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
We consider the least squares regression problem, penalized with a combi...
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02/18/2020
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Neural networks and tree ensembles are state-of-the-art learners, each w...
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01/17/2020
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We consider a discrete optimization based approach for learning sparse c...
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02/05/2019
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
In many learning settings, it is beneficial to augment the main features...
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03/05/2018
