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  • Publications
    • AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
    • Confidence Sets for Causal Orderings
    • Fast and Consistent Structure Learning in Graphical Models via Approximate Cross-Validation
    • Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters
    • Joint-Sparse Transfer Learning for High-Dimensional Multi-Output Regression
    • Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
    • Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference
    • Provable Accelerated Bayesian Optimization with Knowledge Transfer
    • Simultaneous Inference for Covariance and Precision Matrices of Long-Range Dependent Time Series
    • SMART: A Spectral Transfer Approach to Multi-Task Learning
    • Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation
    • Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
    • Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
    • High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
    • Statistical Inference for Networks of High-Dimensional Point Processes
    • A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
    • Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
    • Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
    • Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
    • High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
    • High-Dimensional Markov-switching Ordinary Differential Processes
    • Inconsistency of cross-validation for structure learning in Gaussian graphical models
    • Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
    • Latent Multimodal Functional Graphical Model Estimation
    • On the Lasso for Graphical Continuous Lyapunov Models
    • Personalized Binomial DAGs Learning with Network Structured Covariates
    • Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
    • Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
    • One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning
    • Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
    • Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
    • Differentially Private Matrix Completion through Low-rank Matrix Factorization
    • Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
    • L-SVRG and L-Katyusha with Adaptive Sampling
    • Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
    • Provably training overparameterized neural network classifiers with non-convex constraints
    • Gradient-Variation Bound for Online Convex Optimization with Constraints
    • Local AdaGrad-type algorithm for stochastic convex-concave optimization
    • Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
    • An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians
    • Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems
    • Personalized Federated Learning with Multiple Known Clusters
    • A Nonconvex Framework for Structured Dynamic Covariance Recovery
    • FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
    • Joint Gaussian Graphical Model Estimation: A Survey
    • Inference for high-dimensional varying-coefficient quantile regression
    • Two-sample inference for high-dimensional Markov networks
    • Robust Inference for High-Dimensional Linear Models via Residual Randomization
    • Estimating differential latent variable graphical models with applications to brain connectivity
    • High-dimensional Index Volatility Models via Stein's Identity
    • Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees
    • Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
    • Estimation of a Low-rank Topic-Based Model for Information Cascades
    • Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
    • Posterior Ratio Estimation for Latent Variables
    • Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
    • Kernel meets sieve: post-regularization confidence bands for sparse additive model
    • Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
    • Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator
    • Constrained High Dimensional Statistical Inference
    • Partially Linear Additive Gaussian Graphical Models
    • Learning Influence-Receptivity Network Structure with Guarantee
    • Convergent Policy Optimization for Safe Reinforcement Learning
    • Direct Estimation of Differential Functional Graphical Models
    • High-dimensional Varying Index Coefficient Models via Stein's Identity
    • Distributed Stochastic Multi-Task Learning with Graph Regularization
    • Joint Nonparametric Precision Matrix Estimation with Confounding
    • Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
    • Provable Gaussian Embedding with One Observation
    • ROCKET: Robust confidence intervals via Kendall's tau for transelliptical graphical models
    • Scalable Peaceman-Rachford Splitting Method with Proximal Terms
    • Efficient Distributed Learning with Sparsity
    • Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
    • An Influence-Receptivity Model for Topic based Information Cascades
    • Recovering block-structured activations using compressive measurements
    • Sketching meets random projection in the dual: a provable recovery algorithm for big and high-dimensional data
    • Sketching meets random projection in the dual: a provable recovery algorithm for big and high-dimensional data
    • The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
    • Uniform inference for high-dimensional quantile regression: linear functionals and regression rank scores
    • Distributed Multi-Task Learning
    • Distributed Multi-Task Learning with Shared Representation
    • Discussion of ``Coauthorship and citation networks for statisticians''
    • Inference for High-dimensional Exponential Family Graphical Models
    • Statistical Inference for Pairwise Graphical Models Using Score Matching
    • Learning structured densities via infinite dimensional exponential families
    • Optimal variable selection in multi-group sparse discriminant analysis
    • A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression
    • Inference for Sparse Conditional Precision Matrices
    • Mean and variance estimation in high-dimensional heteroscedastic models with non-convex penalties
    • Optimal Feature Selection in High-Dimensional Discriminant Analysis
    • Berry-Esseen bounds for estimating undirected graphs
    • Graph Estimation From Multi-attribute Data
    • Feature Selection in High-Dimensional Classification
    • Markov Network Estimation From Multi-attribute Data
    • Consistent Covariance Selection From Data With Missing Values
    • Variance Function Estimation in High-dimensions
    • Estimating Networks With Jumps
    • Marginal Regression For Multitask Learning
    • Minimax Localization of Structural Information in Large Noisy Matrices
    • On Time Varying Undirected Graphs
    • Statistical and computational tradeoffs in biclustering
    • Union Support Recovery In Multi-task Learning
    • Estimating Time-varying Networks
    • On Sparse Nonparametric Conditional Covariance Selection
    • Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
    • Sparsistent Estimation Of Time-varying Discrete Markov Random Fields
    • KELLER: estimating time-varying interactions between genes
    • Sparsistent Learning Of Varying-coefficient Models With Structural Changes
    • Time-varying Dynamic Bayesian Networks
    • CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing
    • Time Varying Ising Models
    • Comparison of collocation extraction measures for document indexing
    • Computer-Aided document Indexing Systems
  • Team

KELLER: estimating time-varying interactions between genes

May 1, 2009·
L. Song
,
M. Kolar
,
E. P. Xing
· 0 min read
DOI
Type
Journal article
Publication
Bioinformatics
publication
Last updated on Oct 3, 2026
← Sparsistent Estimation Of Time-varying Discrete Markov Random Fields Jul 1, 2009
Sparsistent Learning Of Varying-coefficient Models With Structural Changes Jan 1, 2009 →