Samuel Müller | DeepAI

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∙ 07/11/2023

CR-Lasso: Robust cellwise regularized sparse regression

Cellwise contamination remains a challenging problem for data scientists...

0 Peng Su, et al. ∙

∙ 05/27/2023

PFNs4BO: In-Context Learning for Bayesian Optimization

In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible su...

0 Samuel Müller, et al. ∙

∙ 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...

0 Noah Hollmann, et al. ∙

∙ 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...

0 Diane Wagner, et al. ∙

∙ 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...

0 Noah Hollmann, et al. ∙

∙ 05/05/2022

COMBSS: Best Subset Selection via Continuous Optimization

We consider the problem of best subset selection in linear regression, w...

0 Sarat Moka, et al. ∙

∙ 12/20/2021

Transformers Can Do Bayesian Inference

Currently, it is hard to reap the benefits of deep learning for Bayesian...

0 Samuel Müller, et al. ∙

∙ 10/24/2021

Robust Variable Selection under Cellwise Contamination

Cellwise outliers are widespread in data and traditional robust methods ...

0 Peng Su, et al. ∙

∙ 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...

0 Samuel Müller, et al. ∙

∙ 10/20/2019

Byte-Pair Encoding for Text-to-SQL Generation

Neural sequence-to-sequence models provide a competitive approach to the...

0 Samuel Müller, et al. ∙

∙ 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...

0 P. Y. O'Shaughnessy, et al. ∙

∙ 10/03/2018

Semiparametric Regression using Variational Approximations

Semiparametric regression offers a flexible framework for modeling non-l...

0 Francis K. C. Hui, et al. ∙

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