| 2025 | AISTATS | High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching. | Daniel J. Williams, Leyang Wang, Qizhen Ying, Song Liu, Mladen Kolar |
| 2024 | AISTATS | Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. | Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam |
| 2024 | ICML | Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. | Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang |
| 2023 | AAAI | Gradient-Variation Bound for Online Convex Optimization with Constraints. | Shuang Qiu, Xiaohan Wei, Mladen Kolar |
| 2023 | AISTATS | 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 |
| 2023 | AISTATS | Differentially Private Matrix Completion through Low-rank Matrix Factorization. | Lingxiao Wang, Boxin Zhao, Mladen Kolar |
| 2023 | ICML | Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. | Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar |
| 2023 | ICML | Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. | Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar |
| 2022 | ICML | Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. | Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang |
| 2022 | SIGMETRICS | Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems. | Yuwei Luo, Varun Gupta, Mladen Kolar |
| 2021 | ICML | Robust Inference for High-Dimensional Linear Models via Residual Randomization. | Y. Samuel Wang, Si Kai Lee, Panos Toulis, Mladen Kolar |
| 2020 | ICML | Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees. | Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar |
| 2019 | AISTATS | Learning Influence-Receptivity Network Structure with Guarantee. | Ming Yu, Varun Gupta, Mladen Kolar |
| 2019 | ICML | Partially Linear Additive Gaussian Graphical Models. | Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo |
| 2019 | UAI | Joint Nonparametric Precision Matrix Estimation with Confounding. | Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo |
| 2017 | AISTATS | 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 |
| 2017 | ICDM | An Influence-Receptivity Model for Topic Based Information Cascades. | Ming Yu, Varun Gupta, Mladen Kolar |
| 2017 | ICML | Efficient Distributed Learning with Sparsity. | Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang |
| 2016 | AISTATS | Inference for High-dimensional Exponential Family Graphical Models. | Jialei Wang, Mladen Kolar |
| 2016 | AISTATS | Distributed Multi-Task Learning. | Jialei Wang, Mladen Kolar, Nathan Srebro |
| 2013 | ICML | Feature Selection in High-Dimensional Classification. | Mladen Kolar, Han Liu |
| 2013 | ICML | Markov Network Estimation From Multi-attribute Data. | Mladen Kolar, Han Liu, Eric P. Xing |
| 2012 | ICML | Variance Function Estimation in High-dimensions. | Mladen Kolar, James Sharpnack |
| 2012 | ICML | Consistent Covariance Selection From Data With Missing Values. | Mladen Kolar, Eric P. Xing |
| 2010 | ICML | On Sparse Nonparametric Conditional Covariance Selection. | Mladen Kolar, Ankur P. Parikh, Eric P. Xing |