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Found 24 papers, 3 papers with code
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model
We study the computational and statistical tradeoffs in inferring combinatorial structures of high dimensional simple zero-field ferromagnetic Ising model.
Inference of Dependency Knowledge Graph for Electronic Health Records
The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges.
Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recovery
Under this approach, we are able to locally approximate the action-value function and retrieve the nonlinear, independent contribution of select features as well as joint feature pairs.
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
To overcome these challenges, we propose to infer the conditional dependency structure among EHR features via a latent graphical block model (LGBM).
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data.
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Assortment optimization has received active explorations in the past few decades due to its practical importance.
Federated Offline Reinforcement Learning
Extensive simulations demonstrate the effectiveness of the proposed algorithm.
Lagrangian Inference for Ranking Problems
Our proposed method aims to infer general ranking properties of the BTL model.
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption.
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed.
Ranked #11 on
Image Classification
on WebVision-1000
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions.
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.
On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
We propose a generalization error bound for a general family of deep neural networks based on the depth and width of the networks, as well as the spectral norm of weight matrices.
Sketching Method for Large Scale Combinatorial Inference
In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks.
Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs
Our framework is based on the Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of the inter-subject precision matrix.
Adaptive Inferential Method for Monotone Graph Invariants
In this paper, we propose a new inferential framework for testing nested multiple hypotheses and constructing confidence intervals of the unknown graph invariants under undirected graphical models.
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization
We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e. g., low-rank matrix factorization, phase retrieval, and deep linear neural networks)}.
Combinatorial Inference for Graphical Models
We propose a new family of combinatorial inference problems for graphical models.
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
The testing procedures are based on a high dimensional, debiasing-free moment estimator, which uses a novel kernel smoothed Kendall's tau correlation matrix as an input statistic.
Graphical Fermat's Principle and Triangle-Free Graph Estimation
We consider the problem of estimating undirected triangle-free graphs of high dimensional distributions.
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
We develop a novel procedure for constructing confidence bands for components of a sparse additive model.
Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components.
