Schedule for WHOA-PSI-2019

$wustl.jpg$ Contact: Todd Kuffner, email: kuffner@wustl.edu

Schedule

Saturday, August 17th

Breakfast and Registration	6:30 - 8:15
Introductions	8:20	John Kolassa/Todd Kuffner
Session 1	Chair: John Kolassa Rutgers
	8:30	Miles Lopes UC Davis	Bootstrap Methods in High Dimensions: Spectral Statistics and Max Statistics
	9:00	Kristin Linn University of Pennsylvania	Interactive Q-learning
	9:30	Peter Song University of Michigan	Method Of Contraction-Expansion (MOCE) For Simultaneous Inference in Linear Models
Coffee Break	10:00 - 10:30
Session 2	Chair: John Kolassa Rutgers University
	10:30	Pallavi Basu Indian School of Business	Model selection principles for treatment effect estimation
	11:00	Mladen Kolar University of Chicago	High-dimensional inference with constraints
	11:30	Robert Tibshirani Stanford University	Prediction and outlier detection: a distribution-free prediction set with a balanced objective
Lunch	12:00 - 1:30
Session 3	Chair: Heather Battey Imperial College London
	1:30	Ioannis Kosmidis University of Warwick	Improved estimation of partially specified models
	2:00	Daniela De Angelis University of Cambridge	Value of Information for evidence synthesis
	2:30	Alastair Young Imperial College London	Challenges for (Bayesian) selective inference
Coffee Break	3:00 - 3:30
Session 4	Chair: Annie Qu UIUC
	3:30	Ryan Tibshirani Carnegie Mellon University	What deep learning taught me about linear models
	4:00	Daniel Yekutieli Tel Aviv University	Hierarchical Bayes modeling for large-scale inference
Coffee Break	4:30 - 5:00
Session 5	Chair: Xiao-Li Meng Harvard University
	5:00	Xihong Lin Harvard University	Hypothesis testing for a large number of composite nulls in genome-wide causal mediation analysis
	5:30	Iain Johnstone Stanford University	HOA-PSI for top eigenvalues in spiked PCA models
Poster Session and Banquet	6:10
	Poster Presenters	Stephen Bates Stanford University
		Zhiqi Bu University of Pennsylvania	SLOPE is better than LASSO: estimation and inference of SLOPE via approximate message passing
		Hongyuan Cao Florida State University
		Paromita Dubey UC Davis	Frechet analysis of variance and change point detection for random objects
		Yinqiu He University of Michigan	Likelihood ratio test in multivariate regression: from low to high dimension
		David Hong University of Pennsylvania	Asymptotic eigenstructure of weighted sample covariance matrices for large dimensional low-rank models with heteroscedastic noise

Sunday, August 18th

Breakfast and Registration	6:30 - 7:55
Session 6	Chair: Lucas Janson Harvard University
	8:00	Irina Gaynanova Texas A&M University	Direct inference for sparse differential network analysis
	8:30	Richard Samworth University of Cambridge	High-dimensional principal component analysis with heterogeneous missingness
	9:00	Vladimir Koltchinskii Georgia Tech	Bias reduction and efficiency in estimation of smooth functionals of high-dimensional parameters
Coffee Break	9:30 - 10:00
Session 7	Chair: Kristin Linn University of Pennsylvania
	10:00	Stephen M.S. Lee University of Hong Kong	High-dimensional Local Polynomial Regression with Variable Selection and Dimension Reduction
	10:30	Florentina Bunea Cornell University	Essential regression
	11:00	Jonathan Taylor Stanford University	Inference after selection through a black box
Lunch and Poster Session	11:30 - 1:00
	Poster Presenters	Byol Kim University of Chicago
		Lihua Lei UC Berkeley	The Bag-of-Null-Statistics procedure: an adaptive framework for selecting better test statistics
		Cong Ma Princeton University	Inference and uncertainty quantification for noisy matrix completion
		Matteo Sesia Stanford University	Multi-resolution localization of causal variants across the genome
		Nicholas Syring WUSTL
		Armeen Taeb Caltech
		Hua Wang University of Pennsylvania	The simultaneous inference trade-off analysis on Lasso path
		Yuling Yan Princeton University	Noisy matrix completion: understanding statistical guarantees for convex relaxation via nonconvex optimization
		Yubai Yuan UIUC	High-order embedding for hyperlink network prediction
		Xiaorui Zhu University of Cincinnati	Simultaneous confidence intervals using entire solution paths
Session 8	Chair: Hongyuan Cao Florida State University
	1:00	Yuval Benjamini Hebrew University of Jerusalem	Extrapolating the accuracy of multi-class classification
	1:30	Aaditya Ramdas Carnegie Mellon University	Online control of the false coverage rate and false sign rate
	2:00	Snigdha Panigrahi Stanford University	Post-selective estimation of linear mediation effects
Coffee Break	2:30 - 3:00
Session 9	Chair: Rina Foygel Barber University of Chicago
	3:00	Veronika Rockova University of Chicago	Multiscale analysis of BART priors
	3:30	Ed George University of Pennsylvania	Multidimensional monotonicity discovery with MBART
Coffee Break	4:00 - 4:30
Session 10	Chair: Xiao-Li Meng Harvard University
	4:30	Ulrike Schneider TU Wien	Uniformly valid confidence sets based on the Lasso in low dimensions
	5:00	Art Owen Stanford University	Six percent power and barely selective inference

Monday, August 19th

Breakfast	6:30 - 7:55
Session 11	Chair: Aaditya Ramdas Carnegie Mellon University
	8:00	Weijie Su University of Pennsylvania	Gaussian differential privacy
	8:30	Julia Fukuyama Indiana University	Phylogenetically-informed distance methods: their uses, properties, and potential
	9:00	Jingshen Wang UC Berkeley	Inference on treatment effects after model selection
Coffee Break	9:30 - 10:00
Session 12	Chair: Ed George University of Pennsylvania
	10:00	Rina Foygel Barber University of Chicago	Predictive inference with the jackknife+
	10:30	Annie Qu UIUC	Community detection with dependent connectivity
	11:00	Xiao-Li Meng Harvard University	The conditionality principle is (still) safe and sound, but our large-p-small-n models are ill (defined)
Lunch	11:30 - 1:00
Session 13	Chair: Yuval Benjamini Hebrew University of Jerusalem
	1:00	Brian Caffo Johns Hopkins University	Statistical properties of measurement in resting state functional magnetic resonance imaging
	1:30	Cynthia Rush Columbia University	Algorithmic Analysis of SLOPE via Approximate Message Passing
	2:00	Emmanuel Candes Stanford University
Coffee Break	2:30 - 3:00
Session 14	Chair: R.A. Fisher Rothamsted / Adelaide
	3:00	Jeff Cai / Linda Zhao University of Pennsylvania	Nonparametric empirical Bayes methods for sparse, noisy signals
	3:30	Arun Kumar Kuchibhotla University of Pennsylvania	Post-selection Inference for all