Mladen Kolar

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 Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning JMLR 2024 On the Lasso for Graphical Continuous Lyapunov Models CLeaR 2024 Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning ICML 2024 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 Differentially Private Matrix Completion through Low-rank Matrix Factorization AISTATS 2023 Gradient-Variation Bound for Online Convex Optimization with Constraints AAAI 2023 One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning AISTATS 2023 A Nonconvex Framework for Structured Dynamic Covariance Recovery JMLR 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting JMLR 2022 Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning ICML 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 Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach NeurIPS 2020 Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees ICML 2020 Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching JMLR 2020 Convergent Policy Optimization for Safe Reinforcement Learning NeurIPS 2019 Direct Estimation of Differential Functional Graphical Models NeurIPS 2019 High-dimensional Varying Index Coefficient Models via Stein's Identity JMLR 2019 Joint Nonparametric Precision Matrix Estimation with Confounding UAI 2019 Learning Influence-Receptivity Network Structure with Guarantee AISTATS 2019 Partially Linear Additive Gaussian Graphical Models ICML 2019 Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models JMLR 2018 Provable Gaussian Embedding with One Observation NeurIPS 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 NeurIPS 2017 Distributed Multi-Task Learning AISTATS 2016 Inference for High-dimensional Exponential Family Graphical Models AISTATS 2016 Statistical Inference for Pairwise Graphical Models Using Score Matching NeurIPS 2016 Learning structured densities via infinite dimensional exponential families NeurIPS 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 Minimax Localization of Structural Information in Large Noisy Matrices NeurIPS 2011 On Time Varying Undirected Graphs AISTATS 2011 Union Support Recovery in Multi-task Learning JMLR 2011 Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach AISTATS 2010 Sparsistent Learning of Varying-coefficient Models with Structural Changes NeurIPS 2009 Time-Varying Dynamic Bayesian Networks NeurIPS 2009