K
Krzysztof Chalupka
Researcher at California Institute of Technology
Publications - 18
Citations - 669
Krzysztof Chalupka is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Feature learning & Deep learning. The author has an hindex of 11, co-authored 18 publications receiving 478 citations. Previous affiliations of Krzysztof Chalupka include Amazon.com.
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A Framework for Evaluating Approximation Methods for Gaussian Process Regression
TL;DR: In this article, the relative merits of different Gaussian process regression approximations and in what situations they are most useful have been investigated, and the quality of the predictions obtained as a function of the compute time taken, and comparing to standard baselines.
Journal ArticleDOI
A framework for evaluating approximation methods for Gaussian process regression
TL;DR: Four different approximation algorithms are empirically investigated on four different prediction problems, and the quality of the predictions obtained as a function of the compute time taken are assessed.
Proceedings Article
Visual causal feature learning
TL;DR: In this paper, the Causal Coarsening Theorem is proposed to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior.
Proceedings ArticleDOI
Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications
TL;DR: This work proposes the first end-to-end algorithm for ZSL in video classification, which uses a trainable 3D CNN to learn the visual features and outperforms the state-of-the-art by a wide margin.
Journal ArticleDOI
Causal feature learning: an overview
TL;DR: A detailed introduction to the causal inference framework is presented, laying out the definitions and algorithmic steps, and a simple example illustrates the techniques involved in the learning steps and provides visual intuition.