Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Erickson, Nick"'
Autor:
Salinas, David, Erickson, Nick
We introduce TabRepo, a new dataset of tabular model evaluations and predictions. TabRepo contains the predictions and metrics of 1310 models evaluated on 200 classification and regression datasets. We illustrate the benefit of our dataset in multipl
Externí odkaz:
http://arxiv.org/abs/2311.02971
Autor:
Shchur, Oleksandr, Turkmen, Caner, Erickson, Nick, Shen, Huibin, Shirkov, Alexander, Hu, Tony, Wang, Yuyang
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines
Externí odkaz:
http://arxiv.org/abs/2308.05566
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple
Externí odkaz:
http://arxiv.org/abs/2305.06090
Autor:
Garg, Saurabh, Erickson, Nick, Sharpnack, James, Smola, Alex, Balakrishnan, Sivaraman, Lipton, Zachary C.
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored. Meanwhile, popular deep domain adaptation heuristics tend to falter wh
Externí odkaz:
http://arxiv.org/abs/2302.03020
Publikováno v:
Quest (00336297); Jul-Sep2024, Vol. 76 Issue 3, p327-344, 18p
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well. Here we assemble 18 multimodal data tables that each contain some text fields and ste
Externí odkaz:
http://arxiv.org/abs/2111.02705
Publikováno v:
NeurIPS 2020
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators. While highly accurate, the resulting predictors are large, s
Externí odkaz:
http://arxiv.org/abs/2006.14284
Autor:
Erickson, Nick, Mueller, Jonas, Shirkov, Alexander, Zhang, Hang, Larroy, Pedro, Li, Mu, Smola, Alexander
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that p
Externí odkaz:
http://arxiv.org/abs/2003.06505
Autor:
Erickson, Nick, Zhao, Qi
This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of i
Externí odkaz:
http://arxiv.org/abs/1706.05749
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