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Mladen Kolar
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2020 – today
- 2024
- [j22]Luofeng Liao, Li Shen
, Jia Duan, Mladen Kolar, Dacheng Tao:
Local AdaGrad-type algorithm for stochastic convex-concave optimization. Mach. Learn. 113(4): 1819-1838 (2024) - [j21]Sen Na
A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming. Math. Oper. Res. 49(2): 1012-1044 (2024) - [j20]Yuchen Fang, Sen Na
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems. SIAM J. Optim. 34(2): 2007-2037 (2024) - [c38]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS 2024: 3691-3699 - [c37]Philipp Dettling, Mathias Drton, Mladen Kolar:
On the Lasso for Graphical Continuous Lyapunov Models. CLeaR 2024: 514-550 - [c36]Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang:
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. ICML 2024 - [i48]Tim Tsz-Kit Lau
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods. CoRR abs/2402.11215 (2024) - [i47]Boxin Zhao, Weishi Wang, Dingyuan Zhu, Ziqi Liu, Dong Wang, Zhiqiang Zhang, Jun Zhou, Mladen Kolar:
Personalized Binomial DAGs Learning with Network Structured Covariates. CoRR abs/2406.06829 (2024) - [i46]Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar:
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods. CoRR abs/2406.13936 (2024) - [i45]Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang:
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. CoRR abs/2407.07631 (2024) - [i44]Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar:
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models. CoRR abs/2409.15734 (2024) - [i43]Daniel J. Williams, Leyang Wang, Qizhen Ying, Song Liu, Mladen Kolar:
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching. CoRR abs/2410.10637 (2024) - 2023
- [j19]Sen Na
An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians. Math. Program. 199(1): 721-791 (2023) - [j18]Sen Na
Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming. Math. Program. 202(1): 279-353 (2023) - [j17]Filip Hanzely, Boxin Zhao, Mladen Kolar:
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. Trans. Mach. Learn. Res. 2023 (2023) - [j16]Boxin Zhao, Boxiang Lyu, Mladen Kolar:
L-SVRG and L-Katyusha with Adaptive Sampling. Trans. Mach. Learn. Res. 2023 (2023) - [c35]Shuang Qiu, Xiaohan Wei, Mladen Kolar:
Gradient-Variation Bound for Online Convex Optimization with Constraints. AAAI 2023: 9534-9542 - [c34]Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar:
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. AISTATS 2023: 1965-2001 - [c33]Lingxiao Wang, Boxin Zhao, Mladen Kolar:
Differentially Private Matrix Completion through Low-rank Matrix Factorization. AISTATS 2023: 5731-5748 - [c32]Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. ICML 2023: 13174-13198 - [c31]Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar:
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. ICML 2023: 42081-42097 - [i42]Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar:
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. CoRR abs/2305.18379 (2023) - [i41]Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar:
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. CoRR abs/2306.02543 (2023) - [i40]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR abs/2312.17047 (2023) - 2022
- [j15]Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting. J. Mach. Learn. Res. 23: 82:1-82:82 (2022) - [j14]Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo:
A Nonconvex Framework for Structured Dynamic Covariance Recovery. J. Mach. Learn. Res. 23: 200:1-200:91 (2022) - [j13]Yuwei Luo, Varun Gupta, Mladen Kolar:
Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems. Proc. ACM Meas. Anal. Comput. Syst. 6(1): 9:1-9:72 (2022) - [c30]Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. ICML 2022: 14601-14638 - [c29]Yuwei Luo, Varun Gupta, Mladen Kolar:
Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems. SIGMETRICS (Abstracts) 2022: 75-76 - [i39]Boxin Zhao, Boxiang Lyu, Mladen Kolar:
L-SVRG and L-Katyusha with Adaptive Sampling. CoRR abs/2201.13387 (2022) - [i38]Boxiang Lyu, Filip Hanzely, Mladen Kolar:
Personalized Federated Learning with Multiple Known Clusters. CoRR abs/2204.13619 (2022) - [i37]Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. CoRR abs/2205.02450 (2022) - [i36]Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar:
One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning. CoRR abs/2205.15891 (2022) - [i35]Katherine Tsai, Boxin Zhao, Oluwasanmi Koyejo, Mladen Kolar:
Latent Multimodal Functional Graphical Model Estimation. CoRR abs/2210.17237 (2022) - 2021
- [j12]You-Lin Chen
Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees. J. Comput. Graph. Stat. 30(3): 728-744 (2021) - [c28]Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar
Robust Inference for High-Dimensional Linear Models via Residual Randomization. ICML 2021: 10805-10815 - [i34]Sen Na, Mihai Anitescu, Mladen Kolar:
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians. CoRR abs/2102.05320 (2021) - [i33]Filip Hanzely, Boxin Zhao, Mladen Kolar:
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques. CoRR abs/2102.09743 (2021) - [i32]Luofeng Liao, Zuyue Fu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning. CoRR abs/2102.09907 (2021) - [i31]Boxin Zhao, Shengjun Zhai, Y. Samuel Wang, Mladen Kolar:
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach. CoRR abs/2105.02487 (2021) - [i30]Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, Dacheng Tao:
Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems. CoRR abs/2106.10022 (2021) - [i29]Sen Na, Mihai Anitescu, Mladen Kolar:
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming. CoRR abs/2109.11502 (2021) - [i28]Katherine Tsai, Oluwasanmi Koyejo, Mladen Kolar:
Joint Gaussian Graphical Model Estimation: A Survey. CoRR abs/2110.10281 (2021) - [i27]Yuwei Luo, Varun Gupta, Mladen Kolar:
Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems. CoRR abs/2111.03772 (2021) - [i26]Boxin Zhao, Ziqi Liu, Chaochao Chen, Mladen Kolar, Zhiqiang Zhang, Jun Zhou:
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback. CoRR abs/2112.14332 (2021) - 2020
- [j11]Ming Yu, Varun Gupta, Mladen Kolar:
Estimation of a Low-rank Topic-Based Model for Information Cascades. J. Mach. Learn. Res. 21: 71:1-71:47 (2020) - [j10]Ming Yu, Varun Gupta, Mladen Kolar:
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching. J. Mach. Learn. Res. 21: 91:1-91:51 (2020) - [c27]Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. ICML 2020: 7141-7152 - [c26]Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang:
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach. NeurIPS 2020 - [i25]Yulong Zhang, Mingxuan Yi, Song Liu, Mladen Kolar:
Posterior Ratio Estimation for Latent Variables. CoRR abs/2002.06410 (2020) - [i24]Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. CoRR abs/2003.01013 (2020) - [i23]Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves. CoRR abs/2003.05402 (2020) - [i22]Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, Mladen Kolar:
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach. CoRR abs/2007.01290 (2020) - [i21]Xu Wang, Mladen Kolar, Ali Shojaie:
Statistical Inference for Networks of High-Dimensional Point Processes. CoRR abs/2007.07448 (2020) - [i20]Katherine Tsai, Mladen Kolar, Oluwasanmi Koyejo:
A Nonconvex Framework for Structured Dynamic Covariance Recovery. CoRR abs/2011.05601 (2020) - [i19]You-Lin Chen, Zhaoran Wang, Mladen Kolar:
Provably Training Neural Network Classifiers under Fairness Constraints. CoRR abs/2012.15274 (2020)
2010 – 2019
- 2019
- [j9]Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
High-dimensional Varying Index Coefficient Models via Stein's Identity. J. Mach. Learn. Res. 20: 152:1-152:44 (2019) - [c25]Ming Yu, Varun Gupta, Mladen Kolar:
Learning Influence-Receptivity Network Structure with Guarantee. AISTATS 2019: 1476-1485 - [c24]Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo:
Partially Linear Additive Gaussian Graphical Models. ICML 2019: 2180-2190 - [c23]Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
Direct Estimation of Differential Functional Graphical Models. NeurIPS 2019: 2571-2581 - [c22]Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Convergent Policy Optimization for Safe Reinforcement Learning. NeurIPS 2019: 3121-3133 - [c21]Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo:
Joint Nonparametric Precision Matrix Estimation with Confounding. UAI 2019: 378-388 - [i18]Sinong Geng, Minhao Yan, Mladen Kolar, Oluwasanmi Koyejo:
Partially Linear Additive Gaussian Graphical Models. CoRR abs/1906.03362 (2019) - [i17]You-Lin Chen, Mladen Kolar, Ruey S. Tsay:
Tensor Canonical Correlation Analysis. CoRR abs/1906.05358 (2019) - [i16]Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
Direct Estimation of Differential Functional Graphical Models. CoRR abs/1910.09701 (2019) - [i15]Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Convergent Policy Optimization for Safe Reinforcement Learning. CoRR abs/1910.12156 (2019) - [i14]Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator. CoRR abs/1912.06875 (2019) - 2018
- [c20]Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang:
Provable Gaussian Embedding with One Observation. NeurIPS 2018: 6765-6775 - [i13]Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Stochastic Multi-Task Learning with Graph Regularization. CoRR abs/1802.03830 (2018) - [i12]Ming Yu, Varun Gupta, Mladen Kolar:
Learning Influence-Receptivity Network Structure with Guarantee. CoRR abs/1806.05730 (2018) - [i11]Sen Na, Zhuoran Yang, Zhaoran Wang, Mladen Kolar:
High-dimensional Varying Index Coefficient Models via Stein's Identity. CoRR abs/1810.07128 (2018) - [i10]Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo:
Joint Nonparametric Precision Matrix Estimation with Confounding. CoRR abs/1810.07147 (2018) - [i9]Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang:
Provable Gaussian Embedding with One Observation. CoRR abs/1810.11098 (2018) - [i8]Sen Na, Mladen Kolar:
High-dimensional Index Volatility Models via Stein's Identity. CoRR abs/1811.10790 (2018) - 2017
- [j8]Junwei Lu, Mladen Kolar, Han Liu:
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models. J. Mach. Learn. Res. 18: 203:1-203:78 (2017) - [c19]Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nati Srebro:
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. AISTATS 2017: 1150-1158 - [c18]Ming Yu, Varun Gupta, Mladen Kolar:
An Influence-Receptivity Model for Topic Based Information Cascades. ICDM 2017: 1141-1146 - [c17]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. ICML 2017: 3636-3645 - [c16]Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar:
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. NIPS 2017: 4446-4456 - [i7]Ming Yu, Varun Gupta, Mladen Kolar:
An Influence-Receptivity Model for Topic based Information Cascades. CoRR abs/1709.01919 (2017) - 2016
- [c15]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multi-Task Learning. AISTATS 2016: 751-760 - [c14]Jialei Wang, Mladen Kolar:
Inference for High-dimensional Exponential Family Graphical Models. AISTATS 2016: 1042-1050 - [c13]Ming Yu, Mladen Kolar, Varun Gupta:
Statistical Inference for Pairwise Graphical Models Using Score Matching. NIPS 2016: 2829-2837 - [i6]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multi-Task Learning with Shared Representation. CoRR abs/1603.02185 (2016) - [i5]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. CoRR abs/1605.07991 (2016) - [i4]Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan Srebro:
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. CoRR abs/1610.03045 (2016) - 2015
- [j7]Mladen Kolar, Han Liu:
Optimal Feature Selection in High-Dimensional Discriminant Analysis. IEEE Trans. Inf. Theory 61(2): 1063-1083 (2015) - [c12]Siqi Sun, Mladen Kolar, Jinbo Xu:
Learning structured densities via infinite dimensional exponential families. NIPS 2015: 2287-2295 - [i3]Rina Foygel Barber, Mladen Kolar:
ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models. CoRR abs/1502.07641 (2015) - [i2]Jialei Wang, Mladen Kolar, Nathan Srebro:
Distributed Multitask Learning. CoRR abs/1510.00633 (2015) - 2014
- [j6]Mladen Kolar, Han Liu, Eric P. Xing:
Graph estimation from multi-attribute data. J. Mach. Learn. Res. 15(1): 1713-1750 (2014) - 2013
- [b1]Mladen Kolar:
Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems. Carnegie Mellon University, USA, 2013 - [c11]Mladen Kolar, Han Liu, Eric P. Xing:
Markov Network Estimation From Multi-attribute Data. ICML (3) 2013: 73-81 - [c10]Mladen Kolar, Han Liu:
Feature Selection in High-Dimensional Classification. ICML (1) 2013: 329-337 - [i1]Larry A. Wasserman, Mladen Kolar, Alessandro Rinaldo:
Estimating Undirected Graphs Under Weak Assumptions. CoRR abs/1309.6933 (2013) - 2012
- [c9]Mladen Kolar, James Sharpnack:
Variance Function Estimation in High-dimensions. ICML 2012 - [c8]Mladen Kolar, Eric P. Xing:
Consistent Covariance Selection From Data With Missing Values. ICML 2012 - [c7]Mladen Kolar, Han Liu:
Marginal Regression For Multitask Learning. AISTATS 2012: 647-655 - 2011
- [j5]Mladen Kolar, John D. Lafferty, Larry A. Wasserman:
Union Support Recovery in Multi-task Learning. J. Mach. Learn. Res. 12: 2415-2435 (2011) - [c6]Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh:
Minimax Localization of Structural Information in Large Noisy Matrices. NIPS 2011: 909-917 - [c5]Mladen Kolar, Eric P. Xing:
On Time Varying Undirected Graphs. AISTATS 2011: 407-415 - 2010
- [c4]Mladen Kolar, Ankur P. Parikh, Eric P. Xing:
On Sparse Nonparametric Conditional Covariance Selection. ICML 2010: 559-566 - [c3]Mladen Kolar, Eric P. Xing:
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach. AISTATS 2010: 413-420
2000 – 2009
- 2009
- [j4]Le Song, Mladen Kolar, Eric P. Xing:
KELLER: estimating time-varying interactions between genes. Bioinform. 25(12) (2009) - [c2]Mladen Kolar, Le Song, Eric P. Xing:
Sparsistent Learning of Varying-coefficient Models with Structural Changes. NIPS 2009: 1006-1014 - [c1]Le Song, Mladen Kolar, Eric P. Xing:
Time-Varying Dynamic Bayesian Networks. NIPS 2009: 1732-1740 - 2008
- [j3]Pradipta Ray
CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing. PLoS Comput. Biol. 4(6) (2008) - 2006
- [j2]Sasa Petrovic, Jan Snajder
Comparison of Collocation Extraction Measures for Document Indexing. J. Comput. Inf. Technol. 14(4): 321-327 (2006) - 2005
- [j1]Mladen Kolar, I. Vukmirovic, Bojana Dalbelo Basic, Jan Snajder
Computer-Aided Document Indexing System. J. Comput. Inf. Technol. 13(4): 299-305 (2005)
Coauthor Index
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