Search Results for author: Krzysztof Chalupka

Found 8 papers, 2 papers with code

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes

1 code implementation8 Apr 2018 Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt

The test is based on the idea that when $P(X \mid Y, Z) = P(X \mid Y)$, $Z$ is not useful as a feature to predict $X$, as long as $Y$ is also a regressor.

Causal Regularization

no code implementations8 Feb 2017 Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, Jimeng Sun

In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.

Representation Learning

Estimating Causal Direction and Confounding of Two Discrete Variables

no code implementations4 Nov 2016 Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona

We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error.

Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data

no code implementations30 May 2016 Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, Frederick Eberhardt

We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific Ocean.

Multi-Level Cause-Effect Systems

no code implementations25 Dec 2015 Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt

We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis.

Generalized Regressive Motion: a Visual Cue to Collision

no code implementations26 Oct 2015 Krzysztof Chalupka, Michael Dickinson, Pietro Perona

Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles.

Visual Causal Feature Learning

no code implementations7 Dec 2014 Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems.

Active Learning

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