illustration of intersecting lines with various shapes at the connecting points signifying algorithms on a green gradient background

Algorithms

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Publication

Bandits with Knapsacks beyond the Worst Case

Karthik Abinav Sankararaman, Aleksandrs Slivkins (slivkins)

NeurIPS 2021 | December 2021

Publication

On the Convergence of Typical Decomposition Approaches in MOEA/D for Many-objective Optimization Problems

Zhengxin Huang, Yuren Zhou, Chuan Luo, Qingwei Lin 林庆维

IJCAI 2021 | December 2021

Publication

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control.

A. Parayil, A. S. Bedi* (equal contribution), J. Zhang, M. Wang, A. Koppel

December 2021

Publication

Predictive Job Scheduling under Uncertain Constraints in Cloud Computing

Hang Dong, Boshi Wang, Bo Qiao, Wenqian Xing, Chuan Luo, Si Qin, Qingwei Lin 林庆维, Dongmei Zhang, Gurpreet Virdi, Thomas Moscibroda

IJCAI’21 | December 2021

Project Project Project

Publication

Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness

Ankit Garg, Robin Kothari, Praneeth Netrapalli, , Suhail Sherif

NeurIPS 2021 | December 2021

Publication

Going Beyond Linear RL: Sample Efficient Neural Function Approximation

Baihe Huang, Kaixuan Huang, Sham Kakade, Jason D. Lee, Qi Lei, Runzhe Wang, Jiaqi Yang

NeurIPS 2021 | December 2021

Publication

The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle

Fang Kong, Yueran Yang, Wei Chen, Shuai Li

Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS) | December 2021

Publication

RL for Latent MDPs: Regret Guarantees and a Lower Bound

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

NeurIPS 2021 | December 2021

Publication

Combinatorial Pure Exploration with Bottleneck Reward Function

Yihan Du, Yuko Kuroki, Wei Chen

Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS) | December 2021

Project

Optimization with Uncertainty

Classical algorithms (exact/ approximation) work with an input which is entirely specified up front. While this offline model is useful for static optimization problems, there are several domains which need algorithms to make decisions with…