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Found 9 papers, 1 papers with code
Discrete Nonparametric Causal Discovery Under Latent Class Confounding
The task of learning these structures from data is known as ``causal discovery''.
Omitted Labels in Causality: A Study of Paradoxes
We explore what we call ``omitted label contexts,'' in which training data is limited to a subset of the possible labels.
Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity
We consider the problem of identifying, from statistics, a distribution of discrete random variables $X_1,\ldots, X_n$ that is a mixture of $k$ product distributions.
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases.
Causal Inference Despite Limited Global Confounding via Mixture Models
A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the random variables that is Markovian on the graph.
Expert Graphs: Synthesizing New Expertise via Collaboration
Consider multiple experts with overlapping expertise working on a classification problem under uncertain input.
Source Identification for Mixtures of Product Distributions
We give an algorithm for source identification of a mixture of $k$ product distributions on $n$ bits.
Robust Correction of Sampling Bias Using Cumulative Distribution Functions
Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift.
The Sparse Hausdorff Moment Problem, with Application to Topic Models
We give an algorithm for identifying a $k$-mixture using samples of $m=2k$ iid binary random variables using a sample of size $\left(1/w_{\min}\right)^2 \cdot\left(1/\zeta\right)^{O(k)}$ and post-sampling runtime of only $O(k^{2+o(1)})$ arithmetic operations.
