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07/11/2023
CR-Lasso: Robust cellwise regularized sparse regression
Cellwise contamination remains a challenging problem for data scientists...
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05/27/2023
PFNs4BO: In-Context Learning for Bayesian Optimization
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible su...
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05/05/2023
GPT for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
As the field of automated machine learning (AutoML) advances, it becomes...
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07/16/2022
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Self-Supervised Learning (SSL) has become a very active area of Deep Lea...
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07/05/2022
Meta-Learning a Real-Time Tabular AutoML Method For Small Data
We present TabPFN, an AutoML method that is competitive with the state o...
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05/05/2022
COMBSS: Best Subset Selection via Continuous Optimization
We consider the problem of best subset selection in linear regression, w...
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12/20/2021
Transformers Can Do Bayesian Inference
Currently, it is hard to reap the benefits of deep learning for Bayesian...
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10/24/2021
Robust Variable Selection under Cellwise Contamination
Cellwise outliers are widespread in data and traditional robust methods ...
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02/05/2021
In-Loop Meta-Learning with Gradient-Alignment Reward
At the heart of the standard deep learning training loop is a greedy gra...
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10/20/2019
Byte-Pair Encoding for Text-to-SQL Generation
Neural sequence-to-sequence models provide a competitive approach to the...
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12/09/2018
Bootstrapping F test for testing Random Effects in Linear Mixed Models
Recently Hui et al. (2018) use F tests for testing a subset of random ef...
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10/03/2018
