Mladen Kolar

According to our database, Mladen Kolar authored at least 86 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Local AdaGrad-type algorithm for stochastic convex-concave optimization.

Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao

Mach. Learn., April, 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

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming.

Sen Na, Mihai Anitescu, Mladen Kolar

Math. Oper. Res., 2024

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching.

Daniel J. Williams, Leyang Wang, Qizhen Ying, Song Liu, 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

Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods.

Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar

CoRR, 2024

Personalized Binomial DAGs Learning with Network Structured Covariates.

Boxin Zhao, Weishi Wang, Dingyuan Zhu, Ziqi Liu, Dong Wang, Zhiqiang Zhang, Jun Zhou, Mladen Kolar

CoRR, 2024

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods.

Tim Tsz-Kit Lau, Han Liu, Mladen Kolar

CoRR, 2024

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning.

Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Lasso for Graphical Continuous Lyapunov Models.

Philipp Dettling, Mathias Drton, Mladen Kolar

Proceedings of the Causal Learning and Reasoning, 2024

Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models.

Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming.

Sen Na, Mihai Anitescu, Mladen Kolar

Math. Program., November, 2023

An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians.

Sen Na, Mihai Anitescu, Mladen Kolar

Math. Program., May, 2023

L-SVRG and L-Katyusha with Adaptive Sampling.

Boxin Zhao, Boxiang Lyu, Mladen Kolar

Trans. Mach. Learn. Res., 2023

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques.

Filip Hanzely, Boxin Zhao, Mladen Kolar

Trans. Mach. Learn. Res., 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

Proceedings of the International Conference on Machine Learning, 2023

Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching.

Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar

Proceedings of the International Conference on Machine Learning, 2023

Differentially Private Matrix Completion through Low-rank Matrix Factorization.

Lingxiao Wang, Boxin Zhao, Mladen Kolar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Gradient-Variation Bound for Online Convex Optimization with Constraints.

Shuang Qiu, Xiaohan Wei, Mladen Kolar

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

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

A Nonconvex Framework for Structured Dynamic Covariance Recovery.

Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo

J. Mach. Learn. Res., 2022

Latent Multimodal Functional Graphical Model Estimation.

Katherine Tsai, Boxin Zhao, Oluwasanmi Koyejo, 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

Personalized Federated Learning with Multiple Known Clusters.

Boxiang Lyu, Filip Hanzely, Mladen Kolar

CoRR, 2022

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning.

Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang

Proceedings of the International Conference on Machine Learning, 2022

2021

Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees.

You-Lin Chen, Mladen Kolar, Ruey S. Tsay

J. Comput. Graph. Stat., 2021

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback.

Boxin Zhao, Ziqi Liu, Chaochao Chen, Mladen Kolar, Zhiqiang Zhang, Jun Zhou

CoRR, 2021

Joint Gaussian Graphical Model Estimation: A Survey.

Katherine Tsai, Oluwasanmi Koyejo, Mladen Kolar

CoRR, 2021

Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems.

Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao

CoRR, 2021

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach.

Boxin Zhao, Shengjun Zhai, Y. Samuel Wang, Mladen Kolar

CoRR, 2021

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning.

Luofeng Liao, Zuyue Fu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang

CoRR, 2021

Robust Inference for High-Dimensional Linear Models via Residual Randomization.

Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching.

Ming Yu, Varun Gupta, Mladen Kolar

J. Mach. Learn. Res., 2020

Estimation of a Low-rank Topic-Based Model for Information Cascades.

Ming Yu, Varun Gupta, Mladen Kolar

J. Mach. Learn. Res., 2020

Provably Training Neural Network Classifiers under Fairness Constraints.

You-Lin Chen, Zhaoran Wang, Mladen Kolar

CoRR, 2020

Statistical Inference for Networks of High-Dimensional Point Processes.

Xu Wang, Mladen Kolar, Ali Shojaie

CoRR, 2020

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach.

Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar

CoRR, 2020

FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves.

Boxin Zhao, Y. Samuel Wang, Mladen Kolar

CoRR, 2020

Posterior Ratio Estimation for Latent Variables.

Yulong Zhang, Mingxuan Yi, Song Liu, Mladen Kolar

CoRR, 2020

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach.

Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees.

Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

High-dimensional Varying Index Coefficient Models via Stein's Identity.

Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar

J. Mach. Learn. Res., 2019

Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator.

Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar

CoRR, 2019

Tensor Canonical Correlation Analysis.

You-Lin Chen, Mladen Kolar, Ruey S. Tsay

CoRR, 2019

Joint Nonparametric Precision Matrix Estimation with Confounding.

Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Direct Estimation of Differential Functional Graphical Models.

Boxin Zhao, Y. Samuel Wang, Mladen Kolar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Convergent Policy Optimization for Safe Reinforcement Learning.

Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Partially Linear Additive Gaussian Graphical Models.

Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Influence-Receptivity Network Structure with Guarantee.

Ming Yu, Varun Gupta, Mladen Kolar

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

High-dimensional Index Volatility Models via Stein's Identity.

Sen Na, Mladen Kolar

CoRR, 2018

Distributed Stochastic Multi-Task Learning with Graph Regularization.

Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2018

Provable Gaussian Embedding with One Observation.

Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models.

Junwei Lu, Mladen Kolar, Han Liu

J. Mach. Learn. Res., 2017

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities.

Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Efficient Distributed Learning with Sparsity.

Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

An Influence-Receptivity Model for Topic Based Information Cascades.

Ming Yu, Varun Gupta, Mladen Kolar

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

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

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Distributed Multi-Task Learning with Shared Representation.

Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2016

Statistical Inference for Pairwise Graphical Models Using Score Matching.

Ming Yu, Mladen Kolar, Varun Gupta

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Distributed Multi-Task Learning.

Jialei Wang, Mladen Kolar, Nathan Srebro

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Inference for High-dimensional Exponential Family Graphical Models.

Jialei Wang, Mladen Kolar

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Optimal Feature Selection in High-Dimensional Discriminant Analysis.

Mladen Kolar, Han Liu

IEEE Trans. Inf. Theory, 2015

Distributed Multitask Learning.

Jialei Wang, Mladen Kolar, Nathan Srebro

CoRR, 2015

ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models.

Rina Foygel Barber, Mladen Kolar

CoRR, 2015

Learning structured densities via infinite dimensional exponential families.

Siqi Sun, Mladen Kolar, Jinbo Xu

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Graph estimation from multi-attribute data.

Mladen Kolar, Han Liu, Eric P. Xing

J. Mach. Learn. Res., 2014

2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems.

Mladen Kolar

PhD thesis, 2013

Estimating Undirected Graphs Under Weak Assumptions.

Larry A. Wasserman, Mladen Kolar, Alessandro Rinaldo

CoRR, 2013

Markov Network Estimation From Multi-attribute Data.

Mladen Kolar, Han Liu, Eric P. Xing

Proceedings of the 30th International Conference on Machine Learning, 2013

Feature Selection in High-Dimensional Classification.

Mladen Kolar, Han Liu

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Marginal Regression For Multitask Learning.

Mladen Kolar, Han Liu

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Consistent Covariance Selection From Data With Missing Values.

Mladen Kolar, Eric P. Xing

Proceedings of the 29th International Conference on Machine Learning, 2012

Variance Function Estimation in High-dimensions.

Mladen Kolar, James Sharpnack

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

On Time Varying Undirected Graphs.

Mladen Kolar, Eric P. Xing

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Union Support Recovery in Multi-task Learning.

Mladen Kolar, John D. Lafferty, Larry A. Wasserman

J. Mach. Learn. Res., 2011

Minimax Localization of Structural Information in Large Noisy Matrices.

Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach.

Mladen Kolar, Eric P. Xing

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On Sparse Nonparametric Conditional Covariance Selection.

Mladen Kolar, Ankur P. Parikh, Eric P. Xing

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

KELLER: estimating time-varying interactions between genes.

Le Song, Mladen Kolar, Eric P. Xing

Bioinform., 2009

Time-Varying Dynamic Bayesian Networks.

Le Song, Mladen Kolar, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Sparsistent Learning of Varying-coefficient Models with Structural Changes.

Mladen Kolar, Le Song, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing.

Pradipta Ray, Suyash Shringarpure, Mladen Kolar, Eric P. Xing

PLoS Comput. Biol., 2008

2006

Comparison of Collocation Extraction Measures for Document Indexing.

Sasa Petrovic, Jan Snajder, Bojana Dalbelo Basic, Mladen Kolar

J. Comput. Inf. Technol., 2006

2005

Computer-Aided Document Indexing System.

Mladen Kolar, I. Vukmirovic, Bojana Dalbelo Basic, Jan Snajder

J. Comput. Inf. Technol., 2005