FODS 2020: ACM - IMS FOUNDATIONS OF DATA SCIENCE

B
Bernstein, Ryan	Transforming Probabilistic Programs for Model Checking
Blei, David	Causal Reasoning Tutorial
Burns, Collin	Interpreting Black Box Models via Hypothesis Testing
C
Carreira-Perpinan, Miguel	Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting
Chaintreau, Augustin	Incentives Needed for Low-Cost Fair Data Reuse
Chen, Haitian	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
Chen, Hao	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
Chen, Xiangyi	Toward Communication Efficient Adaptive Gradient Method
D
De Rougemont, Michel	Large very dense subgraphs in a stream of edges
Drton, Mathias	Statistical significance in high-dimensional linear mixed models
E
Etzioni, Oren	Semantic Scholar, NLP, and the Fight Against COVID-19
F
Fan, Chenglin	Classification Acceleration via Merging Decision Trees
Fosdick, Bailey	Non-Uniform Sampling of Fixed Margin Binary Matrices
Fout, Alex	Non-Uniform Sampling of Fixed Margin Binary Matrices
G
Geng, Dawei	Towards Practical Lipschitz Bandits
Gong, Ruobin	Congenial Differential Privacy under Mandated Disclosure
Grasso, Isabella	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
Gui, Yu	ADAGES: adaptive aggregation with stability for distributed feature selection
H
Hitt, Matthew	Non-Uniform Sampling of Fixed Margin Binary Matrices
Honavar, Vasant	Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
Hooker, Giles	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
J
Jiang, Bai	StyleCAPTCHA: CAPTCHA based on style-transferred images to defend against Deep Convolutional Networks
K
Kearns, Michael	Fairness, Privacy, and Ethics in Data Science Tutorial
L
Le, Thanh	Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data
Li, Ping	Classification Acceleration via Merging Decision Trees Toward Communication Efficient Adaptive Gradient Method
Li, Xiaoyun	Toward Communication Efficient Adaptive Gradient Method
Lin, Lina	Statistical significance in high-dimensional linear mixed models
M
Maio, Roland	Incentives Needed for Low-Cost Fair Data Reuse
Mathieu, Claire	Large very dense subgraphs in a stream of edges
Matthews, Abigail	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
Matthews, Jeanna	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
Meng, Xiao-Li	Congenial Differential Privacy under Mandated Disclosure
R
Record, Nick	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
Rudin, Cynthia	Towards Practical Lipschitz Bandits
Russell, David	Applying Algorithmic Accountability Frameworks withDomain-specific Codes of Ethics: A Case Study in EcosystemForecasting for Shellfish Toxicity in the Gulf of Maine
S
Shah, Devavrat	On Reinforcement Learning for Turn-based Zero-sum Markov Games
Shojaie, Ali	Statistical significance in high-dimensional linear mixed models
Soloviev, Matvey	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Somani, Varun	On Reinforcement Learning for Turn-based Zero-sum Markov Games
T
Tan, Sarah	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Tansey, Wesley	Interpreting Black Box Models via Hypothesis Testing
Thomason, Jesse	Interpreting Black Box Models via Hypothesis Testing
V
van der Schaar, Mihaela	AutoML and interpretability: powering the machine learning revolution in healthcare
Vákár, Matthijs	Transforming Probabilistic Programs for Model Checking
W
Wang, Tianyu	Towards Practical Lipschitz Bandits
Wells, Martin T.	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Wing, Jeannette	Transforming Probabilistic Programs for Model Checking
X
Xie, Qiaomin	On Reinforcement Learning for Turn-based Zero-sum Markov Games
Xu, Zhi	On Reinforcement Learning for Turn-based Zero-sum Markov Games
Y
Ye, Weicheng	Towards Practical Lipschitz Bandits
Z
Zharmagambetov, Arman	Ensembles of bagged TAO trees consistently improve over Random Forests, AdaBoost and Gradient Boosting

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