Lilian Kabeche, 10/22-10/24 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)

Morgan DeSantis, 11/5-11/6 (Monday seminar, Tuesday chalk talk) Seychelle Vos, 11/7-11/8 (Wednesday seminar, Thursday chalk talk) Anders Hansen, 11/26-11/28 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)

Franziska Bleichert, 12/3-12/4 (Monday seminar, Tuesday chalk talk)

# Tag Archives: seminars

# Biostatistics Seminar: 10/23/18 – 12:00 Noon – 47 College St, 106B

Please join the Biostatistics Seminar scheduled for Tuesday, October 23, 2018 at 12:00 Noon at 47 College St., Room 106B.

Yale calendar Link: “YSPH Biostatistics Seminar: “Testing for Balance in Social Networks”

Thank you,

Speaker: Derek Feng, PhD

Institution: Yale University

Time & Place: 12:00 Noon in Room 106B, 47 College St.

11:45 AM Lunch outside Rm. 106B

Title: “Testing for Balance in Social Networks”

Abstract:

Friendship and antipathy exist in concert with one another in real social networks. Despite the role they play in social interactions, antagonistic ties are poorly understood and infrequently measured. One important theory of negative ties that has received relatively little empirical evaluation is balance theory, the codification of the adage `the enemy of my enemy is my friend’ and similar sayings. Unbalanced triangles are those with an odd number of negative ties, and the theory posits that such triangles are rare. To test for balance, previous works have utilized a permutation test on the edge signs. The flaw in this method, however, is that it assumes that negative and positive edges are interchangeable. In reality, they could not be more different. Here, we propose a novel test of balance that accounts for this discrepancy and show that our test is more accurate at detecting balance. Along the way, we prove asymptotic normality of the test statistic under our null model, which is of independent interest. Our case study is a novel dataset of signed networks we collected from 32 isolated, rural villages in Honduras. Contrary to previous results, we find that there is only marginal evidence for balance in social tie formation in this setting.

BIS Seminar Notice Oct 23_2018.pdf

# ST Retreat – “Cancer In Translation” October 16, 2018

Dear YCC Members and Community,

Attached are the flyer and the agenda for the “Cancer In Translation” A Signal Transduction Retreat scheduled for Tuesday, October 16, 2018 in the Conference Center at West Campus. The keynote speaker will be David Solit, MD from the Memorial Sloan Kettering Cancer Center.

Please register by end of day Friday, October 12, 2018. For those who have not yet registered, please click the link below:

REGISTRATION

Feel free to post in your area and forward to anyone who may be interested. Please contact Susan Silva (susan.silva@yale.edu) with any questions.

ST Retreat Agenda – Final.pdf

Cancer In Translation E-Flyer FINAL.pdf

# Biostatistics Seminar: Transcription factor target identiﬁcation via knock-down experiments

Please join the Biostatistics Seminar October 22, 2018 at 12:00 Noon at 60 College Street, Rm. 216. Please also refer to the flyer attached.

Thank you.

Speaker: Leying Guan

Institution: Stanford University

Time & Place: 12:00 Noon in Room 216, 60 College Street

Title: “Transcription factor target identiﬁcation via knock-down experiments”

Abstract:

The perturbation of a transcription factor should aﬀect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose two new methods. The ﬁrst one is a modiﬁed two-group model using data from a single knock-down experiment. The null group corresponds to genes that are not direct targets, but can have small non-zero eﬀects. The second method constructs a depth constrained network using data from multiple knock-down experiments. I will give details of both methods and present some simulated as well as real data examples.

# Math-applied Next week: Applied Math Program: Seminar & Refreshments Wednesday, Oct 10, 2018

Note UNUSUAL DAY and LOCATION

APPLIED MATH SEMINAR

Speaker: David van Dijk, Yale

Date: Wednesday, October 10, 2018

Time: 3:45p.m. Refreshments (AKW, 1st Floor Break Area)

4:00p.m. Seminar (LOM 214)

Title: Understanding neural networks inside and out: designing constraints to enhance interpretability

Deep neural networks can learn meaningful representations of data. However, these representations are hard to interpret. In this talk I will present three ongoing projects in which I use specially designed constraints on latent representations of neural nets in order to make them more interpretable. First, I will present SAUCIE (Sparse Autoencoder for Clustering Imputation and Embedding) which is a framework for performing several data analysis tasks on a unified data representation. In SAUCIE we constrain the latent dimensions to be amenable to clustering, batch correction, imputation, and

visualization. Next, I will present a novel class of regularizations termed Graph Spectral Regularizations that impose graph structure on the otherwise unstructured activations of latent layers. By

considering the activations as signals on this graph we can use graph signal processing, and specifically graph filtering, to constrain the activations. I will show that, among other uses, this allows us to extract topological structure, such as clusters and progressions from data. Further, I will show that when the imposed graph is a 2D grid, with a smoothing penalty, the latent encodings become image-like. Such imposed grid structure also allows for addition of convolutional layers, even when the input data is naturally unstructured. Finally, in the third project, I propose a neural network framework, termed DyMoN (Dynamics Modeling network), that is capable of learning any stochastic dynamic process. I show that a DyMoN can learn harmonic and chaotic behavior, of single and double pendula respectively, and can give insight into the dynamics of biological systems.

# YINS 9/17 Applied DS Seminar, Andrew Barron: Deep Learning

“Accuracy of High-Dimensional Deep Learning Networks”

Speaker: Andrew Barron

Professor of Statistics and Data Science at Yale University

Monday, September 17, 4:15-5:30pm

Location: Yale Institute for Network Science, 17 Hillhouse Ave, Room 328

Talk summary: It has been experimentally observed in recent years that multi-layer artificial neural networks have a surprising ability to generalize, even when trained with far more parameters than observations. Is there a theoretical basis for this? The best available bounds on their metric entropy and associated complexity measures are essentially linear in the number of parameters, which is inadequate to explain this phenomenon. Here we examine the statistical risk (mean squared predictive error) of multi-layer networks with L1 controls on their parameters and with ramp activation functions (also called lower-rectified linear units). In this setting, the risk is shown to be upper-bounded by [(L^3 log d)/n]^{1/2}, where d is the input dimension to each layer, L is the number of layers, and n is the sample size. In this way, the input dimension can be much larger than the sample size and the estimator can still be accurate, provided the target function has such L1 controls and that the sample size is at least moderately large compared to L^3 log d. The heart of the analysis is the development of a sampling strategy that demonstrates the accuracy of a sparse covering of deep ramp networks. Lower bounds show that the identified risk is minimax optimal, this being so already in the subclass of functions with L = 2. This is joint work with Jason Klusowski.

# Statseminars Fwd: YINS 9/12 YINS Seminar, Constantinos Daskalakis: Adversarial Networks

“Improving Generative Adversarial Networks using Game Theory and Statistics”

Speaker: Constantinos Daskalakis

Professor of Computer Science and Electrical Engineering at MIT

Wednesday, September 12, 12:00-1:00pm

Location: Yale Institute for Network Science, 17 Hillhouse Ave, Room 328

Talk summary: Generative Adversarial Networks (aka GANs) are a recently proposed approach for learning samplers of high-dimensional distributions with intricate structure, such as distributions over natural images, given samples from these distributions. They are obtained by setting up a two-player zero-sum game between two neural networks, which learn statistics of a target distribution by adapting their strategies in the game using gradient descent. Despite their intriguing performance in practice, GANs pose great challenges to both optimization and statistics. Their training suffers from oscillations, and they are difficult to scale to high-dimensional settings. We study how game-theoretic and statistical techniques can be brought to bear on these important challenges. We use Game Theory towards improving GAN training, and Statistics towards scaling up the dimensionality of the generated distributions.