Dhruv Nair

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Predictive Early Stopping – A Meta Learning Approach

Dhruv Nair | April 20, 2020

Introduction Model training is arguably the most time consuming, and computationally demanding part of the Machine Learning pipeline. Depending on the complexity of your model, or search space, it ...

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Estimating Uncertainty in Machine Learning Models — Part 3

Dhruv Nair | April 20, 2020

Check out part 1 (here)and part 2 (here) of this series In the last part of our series on uncertainty estimation, we addressed the limitations of approaches like bootstrapp...

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Estimating Uncertainty in Machine Learning Models – Part 2

Dhruv Nair | April 20, 2020

You can check out part 1 of this series here In part 1 of this series, we discussed the sources of uncertainty in machine learning models, and techniques to quantify u...

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Estimating Uncertainty in Machine Learning Models — Part 1

Dhruv Nair | April 20, 2020

“We demand rigidly defined areas of doubt and uncertainty!” - Douglas Adams, The Hitchhiker’s Guide to the Galaxy Why is uncertainty important? Let’s imag...

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ICLR Reproducibility Interview #2: Arnout Devos, Sylvain Chatel, Matthias Grossglauser

Dhruv Nair | April 20, 2020

R2D2 Meta-Learning Method. Full paper here Interview by Cecelia Shao The ...

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Part II: Manual Feature Engineering techniques for the Kaggle Home Credit Default Competition

Dhruv Nair | April 20, 2020

Our second post in this series, where the Comet.ml team competes to win the Kaggle Home Credit Def...

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Part I: Conducting Exploratory Data Analysis (EDA) for the Kaggle Home Credit Default Competition

Dhruv Nair | April 20, 2020

Follow along as the Comet.ml team competes to win the Kaggle Home Credit Default Competition�...

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Using fastText and Comet.ml to classify relationships in Knowledge Graphs

Dhruv Nair | April 20, 2020

TLDR: In this post, we will examine how a simple model, fastText, learns to represent entities in a subset of the FB15K kn...

More articles for this author

Predictive Early Stopping – A Meta Learning Approach

Estimating Uncertainty in Machine Learning Models — Part 3

Estimating Uncertainty in Machine Learning Models – Part 2

Estimating Uncertainty in Machine Learning Models — Part 1

ICLR Reproducibility Interview #2: Arnout Devos, Sylvain Chatel, Matthias Grossglauser

Part II: Manual Feature Engineering techniques for the Kaggle Home Credit Default Competition

Part I: Conducting Exploratory Data Analysis (EDA) for the Kaggle Home Credit Default Competition

Using fastText and Comet.ml to classify relationships in Knowledge Graphs

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