FODS 2020: ACM - IMS FOUNDATIONS OF DATA SCIENCE

This page contains an index consisting of author-provided keywords.

A
Adaptive Method	Toward Communication Efficient Adaptive Gradient Method
algorithmic accountability	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
algorithmic fairness	Incentives Needed for Low-Cost Fair Data Reuse
approximation	Large very dense subgraphs in a stream of edges
B
bagging	Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting
Bandit	Towards Practical Lipschitz Bandits
Bayesian data analysis	Transforming Probabilistic Programs for Model Checking
binary matrix	Non-Uniform Sampling of Fixed Margin Binary Matrices
black box	Interpreting Black Box Models via Hypothesis Testing
C
CAPTCHA	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
checkerboard swap	Non-Uniform Sampling of Fixed Margin Binary Matrices
Classification	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable Classification Acceleration via Merging Decision Trees
clustering	Large very dense subgraphs in a stream of edges
conditioning	Congenial Differential Privacy under Mandated Disclosure
confidence intervals	Statistical significance in high-dimensional linear mixed models
controlled feature selection	ADAGES: adaptive aggregation with stability for distributed feature selection
Convergence Analysis	Toward Communication Efficient Adaptive Gradient Method
cost of demographic secrecy	Incentives Needed for Low-Cost Fair Data Reuse
cost of fairness	Incentives Needed for Low-Cost Fair Data Reuse
curveball	Non-Uniform Sampling of Fixed Margin Binary Matrices
D
data-science pipelines	Incentives Needed for Low-Cost Fair Data Reuse
decision tree optimization	Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting
deep convolutional network	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
Dense subgraphs	Large very dense subgraphs in a stream of edges
Dimensionality Reduction	Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
distributed learning	ADAGES: adaptive aggregation with stability for distributed feature selection
E
ecology	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
ensemble learning	Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting
ethics	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
F
face recognition	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
fair-machine learning	Incentives Needed for Low-Cost Fair Data Reuse
false discovery rate	ADAGES: adaptive aggregation with stability for distributed feature selection
FDR control	Interpreting Black Box Models via Hypothesis Testing
Federated Learning	Toward Communication Efficient Adaptive Gradient Method
forecasts	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
G
Gaussian Process Latent Variable Model	Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
Gaussian Processes	Towards Practical Lipschitz Bandits
H
healthcare	AutoML and interpretability: powering the machine learning revolution in healthcare
high-dimensional statistics	Statistical significance in high-dimensional linear mixed models
Hyperparameter Tuning	Towards Practical Lipschitz Bandits
hypothesis testing	Interpreting Black Box Models via Hypothesis Testing
I
incentives fair representations	Incentives Needed for Low-Cost Fair Data Reuse
interpretability	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable Interpreting Black Box Models via Hypothesis Testing AutoML and interpretability: powering the machine learning revolution in healthcare
invariants	Congenial Differential Privacy under Mandated Disclosure
K
key1	Semantic Scholar, NLP, and the Fight Against COVID-19 Causal Reasoning Tutorial Fairness, Privacy, and Ethics in Data Science Tutorial
key2	Semantic Scholar, NLP, and the Fight Against COVID-19 Causal Reasoning Tutorial Fairness, Privacy, and Ethics in Data Science Tutorial
key3	Semantic Scholar, NLP, and the Fight Against COVID-19 Causal Reasoning Tutorial Fairness, Privacy, and Ethics in Data Science Tutorial
L
linear mixed effect model	Statistical significance in high-dimensional linear mixed models
Longitudinal Data	Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
M
machine learning	AutoML and interpretability: powering the machine learning revolution in healthcare
Markov Games	On Reinforcement Learning for Turn-based Zero-sum Markov Games
MCMC sampling	Non-Uniform Sampling of Fixed Margin Binary Matrices
Merging Decision Trees	Classification Acceleration via Merging Decision Trees
model checking	Transforming Probabilistic Programs for Model Checking
Monte Carlo	Congenial Differential Privacy under Mandated Disclosure
N
neural style transfer	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
O
Optimization	Toward Communication Efficient Adaptive Gradient Method
P
post-processing	Congenial Differential Privacy under Mandated Disclosure
probabilistic analysis	Large very dense subgraphs in a stream of edges
probabilistic programming	Transforming Probabilistic Programs for Model Checking
prototypes	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
R
random forests	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable Classification Acceleration via Merging Decision Trees Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting
random graphs	Large very dense subgraphs in a stream of edges
Reinforcement Learning	On Reinforcement Learning for Turn-based Zero-sum Markov Games
S
Sample Complexity	On Reinforcement Learning for Turn-based Zero-sum Markov Games
stability	ADAGES: adaptive aggregation with stability for distributed feature selection
static analysis	Transforming Probabilistic Programs for Model Checking
streaming	Large very dense subgraphs in a stream of edges
T
transparency	Interpreting Black Box Models via Hypothesis Testing
tree ensembles	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
V
variance component estimation	Statistical significance in high-dimensional linear mixed models

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