Computer-Aided Document Indexing System. - Mladen Kolar; I. Vukmirovic; Bojana Dalbelo Basic; Jan Snajder (J. Comput. Inf. Technol., 2005)

Comparison of Collocation Extraction Measures for Document Indexing. - Sasa Petrovic; Jan Snajder; Bojana Dalbelo Basic; Mladen Kolar (J. Comput. Inf. Technol., 2006)

CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing. - Pradipta Ray; Suyash Shringarpure; Mladen Kolar; Eric P. Xing (PLoS Comput. Biol., 2008)

Sparsistent Learning of Varying-coefficient Models with Structural Changes. - Mladen Kolar; Le Song; Eric P. Xing (NIPS, 2009)

Time-Varying Dynamic Bayesian Networks. - Le Song; Mladen Kolar; Eric P. Xing (NIPS, 2009)

KELLER: estimating time-varying interactions between genes. - Le Song; Mladen Kolar; Eric P. Xing (Bioinform., 2009)

On Sparse Nonparametric Conditional Covariance Selection. - Mladen Kolar; Ankur P. Parikh; Eric P. Xing (ICML, 2010)

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach. - Mladen Kolar; Eric P. Xing (AISTATS, 2010)

Minimax Localization of Structural Information in Large Noisy Matrices. - Mladen Kolar; Sivaraman Balakrishnan; Alessandro Rinaldo; Aarti Singh (NIPS, 2011)

Union Support Recovery in Multi-task Learning. - Mladen Kolar; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2011)

On Time Varying Undirected Graphs. - Mladen Kolar; Eric P. Xing (AISTATS, 2011)

Variance Function Estimation in High-dimensions. - Mladen Kolar; James Sharpnack (ICML, 2012)

Consistent Covariance Selection From Data With Missing Values. - Mladen Kolar; Eric P. Xing (ICML, 2012)

Marginal Regression For Multitask Learning. - Mladen Kolar; Han Liu 001 (AISTATS, 2012)

Feature Selection in High-Dimensional Classification. - Mladen Kolar; Han Liu 001 (ICML (1), 2013)

Markov Network Estimation From Multi-attribute Data. - Mladen Kolar; Han Liu 001; Eric P. Xing (ICML (3), 2013)

Estimating Undirected Graphs Under Weak Assumptions. - Larry A. Wasserman; Mladen Kolar; Alessandro Rinaldo (CoRR, 2013)

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems. - Mladen Kolar (2013)

Graph estimation from multi-attribute data. - Mladen Kolar; Han Liu 001; Eric P. Xing (J. Mach. Learn. Res., 2014)

Learning structured densities via infinite dimensional exponential families. - Siqi Sun; Mladen Kolar; Jinbo Xu (NIPS, 2015)

ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models. - Rina Foygel Barber; Mladen Kolar (CoRR, 2015)

Distributed Multitask Learning. - Jialei Wang; Mladen Kolar; Nathan Srebro (CoRR, 2015)

Optimal Feature Selection in High-Dimensional Discriminant Analysis. - Mladen Kolar; Han Liu 001 (IEEE Trans. Inf. Theory, 2015)

Inference for High-dimensional Exponential Family Graphical Models. - Jialei Wang; Mladen Kolar (AISTATS, 2016)

Distributed Multi-Task Learning. - Jialei Wang; Mladen Kolar; Nathan Srebro (AISTATS, 2016)

Statistical Inference for Pairwise Graphical Models Using Score Matching. - Ming Yu 005; Mladen Kolar; Varun Gupta (NIPS, 2016)

Distributed Multi-Task Learning with Shared Representation. - Jialei Wang; Mladen Kolar; Nathan Srebro (CoRR, 2016)

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. - Jialei Wang; Jason D. Lee; Mehrdad Mahdavi; Mladen Kolar; Nati Srebro (AISTATS, 2017)

An Influence-Receptivity Model for Topic Based Information Cascades. - Ming Yu 005; Varun Gupta; Mladen Kolar (ICDM, 2017)

Efficient Distributed Learning with Sparsity. - Jialei Wang; Mladen Kolar; Nathan Srebro; Tong Zhang 001 (ICML, 2017)

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. - Arun Sai Suggala; Mladen Kolar; Pradeep Ravikumar (NIPS, 2017)

Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models. - Junwei Lu; Mladen Kolar; Han Liu 001 (J. Mach. Learn. Res., 2017)

Provable Gaussian Embedding with One Observation. - Ming Yu 005; Zhuoran Yang; Tuo Zhao; Mladen Kolar; Zhaoran Wang 001 (NeurIPS, 2018)

Distributed Stochastic Multi-Task Learning with Graph Regularization. - Weiran Wang; Jialei Wang; Mladen Kolar; Nathan Srebro (CoRR, 2018)

High-dimensional Index Volatility Models via Stein's Identity. - Sen Na; Mladen Kolar (CoRR, 2018)

Learning Influence-Receptivity Network Structure with Guarantee. - Ming Yu 005; Varun Gupta; Mladen Kolar (AISTATS, 2019)

Partially Linear Additive Gaussian Graphical Models. - Sinong Geng; Minhao Yan; Mladen Kolar; Sanmi Koyejo (ICML, 2019)

Convergent Policy Optimization for Safe Reinforcement Learning. - Ming Yu 005; Zhuoran Yang; Mladen Kolar; Zhaoran Wang 001 (NeurIPS, 2019)

Direct Estimation of Differential Functional Graphical Models. - Boxin Zhao; Y. Samuel Wang; Mladen Kolar (NeurIPS, 2019)

Joint Nonparametric Precision Matrix Estimation with Confounding. - Sinong Geng; Mladen Kolar; Oluwasanmi Koyejo (UAI, 2019)

Tensor Canonical Correlation Analysis. - You-Lin Chen; Mladen Kolar; Ruey S. Tsay (CoRR, 2019)

Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator. - Yuwei Luo; Zhuoran Yang; Zhaoran Wang 001; Mladen Kolar (CoRR, 2019)

High-dimensional Varying Index Coefficient Models via Stein's Identity. - Sen Na; Zhuoran Yang; Zhaoran Wang 001; Mladen Kolar (J. Mach. Learn. Res., 2019)

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. - Sen Na; Yuwei Luo; Zhuoran Yang; Zhaoran Wang 001; Mladen Kolar (ICML, 2020)

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach. - Luofeng Liao; You-Lin Chen; Zhuoran Yang; Bo Dai 001; Mladen Kolar; Zhaoran Wang 001 (NeurIPS, 2020)

Posterior Ratio Estimation for Latent Variables. - Yulong Zhang; Mingxuan Yi; Song Liu; Mladen Kolar (CoRR, 2020)

FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves. - Boxin Zhao; Y. Samuel Wang; Mladen Kolar (CoRR, 2020)

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach. - Luofeng Liao; You-Lin Chen; Zhuoran Yang; Bo Dai 001; Zhaoran Wang 001; Mladen Kolar (CoRR, 2020)

Statistical Inference for Networks of High-Dimensional Point Processes. - Xu Wang; Mladen Kolar; Ali Shojaie (CoRR, 2020)

Provably Training Neural Network Classifiers under Fairness Constraints. - You-Lin Chen; Zhaoran Wang 001; Mladen Kolar (CoRR, 2020)

Estimation of a Low-rank Topic-Based Model for Information Cascades. - Ming Yu 005; Varun Gupta; Mladen Kolar (J. Mach. Learn. Res., 2020)

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching. - Ming Yu 005; Varun Gupta; Mladen Kolar (J. Mach. Learn. Res., 2020)

Robust Inference for High-Dimensional Linear Models via Residual Randomization. - Y. Samuel Wang; Si Kai Lee; Panos Toulis; Mladen Kolar (ICML, 2021)

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning. - Luofeng Liao; Zuyue Fu; Zhuoran Yang; Mladen Kolar; Zhaoran Wang 001 (CoRR, 2021)

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach. - Boxin Zhao; Shengjun Zhai; Y. Samuel Wang; Mladen Kolar (CoRR, 2021)

Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems. - Luofeng Liao; Li Shen 008; Jia Duan; Mladen Kolar; Dacheng Tao (CoRR, 2021)

Joint Gaussian Graphical Model Estimation: A Survey. - Katherine Tsai; Oluwasanmi Koyejo; Mladen Kolar (CoRR, 2021)

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback. - Boxin Zhao; Ziqi Liu; Chaochao Chen 001; Mladen Kolar; Zhiqiang Zhang 012; Jun Zhou 011 (CoRR, 2021)

Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees. - You-Lin Chen; Mladen Kolar; Ruey S. Tsay (J. Comput. Graph. Stat., 2021)

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. - Boxiang Lyu; Zhaoran Wang 001; Mladen Kolar; Zhuoran Yang (ICML, 2022)

Personalized Federated Learning with Multiple Known Clusters. - Boxiang Lyu; Filip Hanzely; Mladen Kolar (CoRR, 2022)

One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning. - Pedro Cisneros-Velarde; Boxiang Lyu; Sanmi Koyejo; Mladen Kolar (CoRR, 2022)

Latent Multimodal Functional Graphical Model Estimation. - Katherine Tsai; Boxin Zhao; Oluwasanmi Koyejo; Mladen Kolar (CoRR, 2022)

A Nonconvex Framework for Structured Dynamic Covariance Recovery. - Katherine Tsai; Mladen Kolar; Oluwasanmi Koyejo (J. Mach. Learn. Res., 2022)

FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting. - Boxin Zhao; Y. Samuel Wang; Mladen Kolar (J. Mach. Learn. Res., 2022)

Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems. - Yuwei Luo; Varun Gupta; Mladen Kolar (Proc. ACM Meas. Anal. Comput. Syst., 2022)

Gradient-Variation Bound for Online Convex Optimization with Constraints. - Shuang Qiu; Xiaohan Wei; Mladen Kolar (AAAI, 2023)

One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. - Pedro Cisneros-Velarde; Boxiang Lyu; Sanmi Koyejo; Mladen Kolar (AISTATS, 2023)

Differentially Private Matrix Completion through Low-rank Matrix Factorization. - Lingxiao Wang; Boxin Zhao; Mladen Kolar (AISTATS, 2023)

Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. - Ilgee Hong; Sen Na; Michael W. Mahoney; Mladen Kolar (ICML, 2023)

Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. - Boxin Zhao; Boxiang Lyu; Raul Castro Fernandez; Mladen Kolar (ICML, 2023)

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. - Filip Hanzely; Boxin Zhao; Mladen Kolar (Trans. Mach. Learn. Res., 2023)

L-SVRG and L-Katyusha with Adaptive Sampling. - Boxin Zhao; Boxiang Lyu; Mladen Kolar (Trans. Mach. Learn. Res., 2023)

An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Program., 2023)

Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Program., 2023)

Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. - Zhao Lyu; Wai Ming Tai; Mladen Kolar; Bryon Aragam (AISTATS, 2024)

On the Lasso for Graphical Continuous Lyapunov Models. - Philipp Dettling; Mathias Drton; Mladen Kolar (CLeaR, 2024)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. - Dake Zhang; Boxiang Lyu; Shuang Qiu; Mladen Kolar; Tong Zhang 001 (ICML, 2024)

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods. - Tim Tsz-Kit Lau; Han Liu 001; Mladen Kolar (CoRR, 2024)

Personalized Binomial DAGs Learning with Network Structured Covariates. - Boxin Zhao; Weishi Wang; Dingyuan Zhu; Ziqi Liu; Dong Wang 062; Zhiqiang Zhang 012; Jun Zhou 011; Mladen Kolar (CoRR, 2024)

Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods. - Tim Tsz-Kit Lau; Weijian Li 002; Chenwei Xu; Han Liu 001; Mladen Kolar (CoRR, 2024)

Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (CoRR, 2024)

Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation. - Boxin Zhao; Cong Ma 001; Mladen Kolar (CoRR, 2024)

Convergence Analysis of Accelerated Stochastic Gradient Descent Under the Growth Condition. - You-Lin Chen; Sen Na; Mladen Kolar (Math. Oper. Res., 2024)

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming. - Sen Na; Mihai Anitescu; Mladen Kolar (Math. Oper. Res., 2024)

Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. - Yuchen Fang; Sen Na; Michael W. Mahoney; Mladen Kolar (SIAM J. Optim., 2024)

Local AdaGrad-type algorithm for stochastic convex-concave optimization. - Luofeng Liao; Li Shen 008; Jia Duan; Mladen Kolar; Dacheng Tao (Mach. Learn., 2024)

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching. - Daniel J. Williams; Leyang Wang; Qizhen Ying; Song Liu 002; Mladen Kolar (AISTATS, 2025)

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism. - Tim Tsz-Kit Lau; Weijian Li 002; Chenwei Xu; Han Liu 001; Mladen Kolar (CPAL, 2025)

High-Dimensional Markov-switching Ordinary Differential Processes. - Katherine Tsai; Mladen Kolar; Sanmi Koyejo (CoRR, 2025)