322 citations · 1.5k across the 22 of their papers we have counts for
22 papers
Machine Teaching: A New Paradigm for Building Machine Learning Systems
Patrice Y. Simard, Saleema Amershi, David M. Chickering +8
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning…
Interactive Semantic Featuring for Text Classification
Camille Jandot, Patrice Simard, Max Chickering +2
In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. Thes…
Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
David Maxwell Chickering, Christopher Meek
We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of GES but, unlike GES, ha…
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Patrice Simard, David Chickering, Aparna Lakshmiratan +7
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine to…
A Transformational Characterization of Equivalent Bayesian Network Structures
David Maxwell Chickering
We present a simple characterization of equivalent Bayesian network structures based on local transformations. The significance of the characterization is twofold. First, we are ab…
Learning Equivalence Classes of Bayesian Networks Structures
David Maxwell Chickering
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring f…