Caroline Uhler

Do Deeper Convolutional Networks Perform Better?

Over-parameterization is a recent topic of much interest in the machine ...
10/19/2020 ∙ by Eshaan Nichani, et al. ∙ 0 ∙ share

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Joint Inference of Multiple Graphs from Matrix Polynomials

Inferring graph structure from observations on the nodes is an important...
10/16/2020 ∙ by Madeline Navarro, et al. ∙ 0 ∙ share

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Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors

The following questions are fundamental to understanding the properties ...
09/18/2020 ∙ by Adityanarayanan Radhakrishnan, et al. ∙ 8 ∙ share

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Optimal Transport using GANs for Lineage Tracing

In this paper, we present Super-OT, a novel approach to computational li...
07/23/2020 ∙ by Neha Prasad, et al. ∙ 0 ∙ share

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Learning the Effective Dynamics of Complex Multiscale Systems

Simulations of complex multiscale systems are essential for science and ...
06/24/2020 ∙ by Pantelis R. Vlachas, et al. ∙ 0 ∙ share

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Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under differe...
06/15/2020 ∙ by Karren Yang, et al. ∙ 14 ∙ share

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Balancedness and Alignment are Unlikely in Linear Neural Networks

We study the invariance properties of alignment in linear neural network...
03/13/2020 ∙ by Adityanarayanan Radhakrishnan, et al. ∙ 0 ∙ share

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Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

We consider distributions arising from a mixture of causal models, where...
01/31/2020 ∙ by Basil Saeed, et al. ∙ 0 ∙ share

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Ordering-Based Causal Structure Learning in the Presence of Latent Variables

We consider the task of learning a causal graph in the presence of laten...
10/20/2019 ∙ by Daniel Irving Bernstein, et al. ∙ 0 ∙ share

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Permutation-Based Causal Structure Learning with Unknown Intervention Targets

We consider the problem of estimating causal DAG models from a mix of ob...
10/20/2019 ∙ by Chandler Squires, et al. ∙ 0 ∙ share

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Overparameterized Neural Networks Can Implement Associative Memory

Identifying computational mechanisms for memorization and retrieval is a...
09/26/2019 ∙ by Adityanarayanan Radhakrishnan, et al. ∙ 8 ∙ share

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Covariance Matrix Estimation under Total Positivity for Portfolio Selection

Selecting the optimal Markowitz porfolio depends on estimating the covar...
09/10/2019 ∙ by Raj Agrawal, et al. ∙ 0 ∙ share

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Algebraic Statistics in Practice: Applications to Networks

Algebraic statistics uses tools from algebra (especially from multilinea...
06/23/2019 ∙ by Marta Casanellas, et al. ∙ 0 ∙ share

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Learning High-Dimensional Gaussian Graphical Models under Total Positivity without Tuning Parameters

We consider the problem of estimating an undirected Gaussian graphical m...
06/12/2019 ∙ by Yuhao Wang, et al. ∙ 0 ∙ share

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Anchored Causal Inference in the Presence of Measurement Error

We consider the problem of learning a causal graph in the presence of me...
06/03/2019 ∙ by Basil Saeed, et al. ∙ 0 ∙ share

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Total positivity in structured binary distributions

We study binary distributions that are multivariate totally positive of ...
05/01/2019 ∙ by Steffen Lauritzen, et al. ∙ 0 ∙ share

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Size of Interventional Markov Equivalence Classes in Random DAG Models

Directed acyclic graph (DAG) models are popular for capturing causal rel...
03/05/2019 ∙ by Dmitriy Katz, et al. ∙ 0 ∙ share

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ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Determining the causal structure of a set of variables is critical for b...
02/27/2019 ∙ by Raj Agrawal, et al. ∙ 0 ∙ share

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Brownian motion tree models are toric

Felsenstein's classical model for Gaussian distributions on a phylogenet...
02/26/2019 ∙ by Bernd Sturmfels, et al. ∙ 0 ∙ share

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Multi-Domain Translation by Learning Uncoupled Autoencoders

Multi-domain translation seeks to learn a probabilistic coupling between...
02/09/2019 ∙ by Karren D. Yang, et al. ∙ 32 ∙ share

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Scalable Unbalanced Optimal Transport using Generative Adversarial Networks

Generative adversarial networks (GANs) are an expressive class of neural...
10/26/2018 ∙ by Karren D. Yang, et al. ∙ 0 ∙ share

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Downsampling leads to Image Memorization in Convolutional Autoencoders

Memorization of data in deep neural networks has become a subject of sig...
10/16/2018 ∙ by Adityanarayanan Radhakrishnan, et al. ∙ 0 ∙ share

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Maximum Likelihood Estimation for Totally Positive Log-Concave Densities

We study nonparametric density estimation for two classes of multivariat...
06/26/2018 ∙ by Elina Robeva, et al. ∙ 0 ∙ share

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High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models

We consider the problem of jointly estimating multiple related directed ...
04/03/2018 ∙ by Yuhao Wang, et al. ∙ 0 ∙ share

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Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models

Learning a Bayesian network (BN) from data can be useful for decision-ma...
03/15/2018 ∙ by Raj Agrawal, et al. ∙ 0 ∙ share

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Geometry of Discrete Copulas

Multivariate discrete distributions are fundamental to modeling. Discret...
02/20/2018 ∙ by Elisa Perrone, et al. ∙ 0 ∙ share

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Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

We consider the problem of learning causal DAGs in the setting where bot...
02/17/2018 ∙ by Karren D. Yang, et al. ∙ 0 ∙ share

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Direct Estimation of Differences in Causal Graphs

We consider the problem of estimating the differences between two causal...
02/15/2018 ∙ by Yuhao Wang, et al. ∙ 0 ∙ share

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Patchnet: Interpretable Neural Networks for Image Classification

The ability to visually understand and interpret learned features from c...
05/23/2017 ∙ by Adityanarayanan Radhakrishnan, et al. ∙ 0 ∙ share

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Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

Following the publication of an attack on genome-wide association studie...
07/30/2014 ∙ by Fei Yu, et al. ∙ 0 ∙ share

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Caroline Uhler

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