Mladen Kolar
44 papers · 2009–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (26) π£ Hot Topic Early Bird
π
Interdisciplinary Bridge
π£
Hot Topic Early Bird
π§
Keyword Pioneer
π¬
Deep Specialist
(14)
π
Keyword Champion
ποΈ
Keyword Collector
(97)
π
Conference Pioneer
π
Trend Setter
β‘
Prolific Year
(6)
π
Century Club
(44)
π₯
Unstoppable
(17)
Conferences
AISTATS (11)
ICML (10)
JMLR (10)
NIPS (10)
AAAI (1)
CLEAR (1)
UAI (1)
Top co-authors
Research topics
Keywords
graphical model
(10)
exponential family
(5)
variable selection
(5)
hypothesis testing
(4)
graph estimation
(4)
probabilistic graphical model
(3)
statistical inference
(3)
feature selection
(3)
causal inference
(3)
confidence interval
(3)
score matching
(3)
distributed learning
(2)
sparse estimation
(2)
multi-task learning
(2)
brain connectivity
(2)
time series analysis
(2)
high-dimensional inference
(2)
constrained optimization
(2)
structure learning
(2)
nonconvex optimization
(2)
Papers
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
JMLR 2025
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
AISTATS 2025
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models
AISTATS 2024
On the Lasso for Graphical Continuous Lyapunov Models
CLEAR 2024
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
ICML 2024
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
JMLR 2024
Differentially Private Matrix Completion through Low-rank Matrix Factorization
AISTATS 2023
Gradient-Variation Bound for Online Convex Optimization with Constraints
AAAI 2023
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
ICML 2023
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
ICML 2023
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
AISTATS 2023
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
ICML 2022
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
JMLR 2022
A Nonconvex Framework for Structured Dynamic Covariance Recovery
JMLR 2022
Robust Inference for High-Dimensional Linear Models via Residual Randomization
ICML 2021
Estimation of a Low-rank Topic-Based Model for Information Cascades
JMLR 2020
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
JMLR 2020
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
NIPS 2020
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
ICML 2020
Joint Nonparametric Precision Matrix Estimation with Confounding
UAI 2019
Direct Estimation of Differential Functional Graphical Models
NIPS 2019
Convergent Policy Optimization for Safe Reinforcement Learning
NIPS 2019
Learning Influence-Receptivity Network Structure with Guarantee
AISTATS 2019
Partially Linear Additive Gaussian Graphical Models
ICML 2019
High-dimensional Varying Index Coefficient Models via Stein's Identity
JMLR 2019
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
JMLR 2018
Provable Gaussian Embedding with One Observation
NIPS 2018
Efficient Distributed Learning with Sparsity
ICML 2017
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
AISTATS 2017
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
NIPS 2017
Statistical Inference for Pairwise Graphical Models Using Score Matching
NIPS 2016
Inference for High-dimensional Exponential Family Graphical Models
AISTATS 2016
Distributed Multi-Task Learning
AISTATS 2016
Learning structured densities via infinite dimensional exponential families
NIPS 2015
Graph Estimation From Multi-Attribute Data
JMLR 2014
Feature Selection in High-Dimensional Classification
ICML 2013
Markov Network Estimation From Multi-attribute Data
ICML 2013
Marginal Regression For Multitask Learning
AISTATS 2012
On Time Varying Undirected Graphs
AISTATS 2011
Union Support Recovery in Multi-task Learning
JMLR 2011
Minimax Localization of Structural Information in Large Noisy Matrices
NIPS 2011
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
AISTATS 2010
Time-Varying Dynamic Bayesian Networks
NIPS 2009
Sparsistent Learning of Varying-coefficient Models with Structural Changes
NIPS 2009