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20122026
most citedFeature Clustering for Accelerating Parallel Coordinate Descent

39 citations · 200 across the 57 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

stat.ME2022★ 3 cited

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains

Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy +1

Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e.g., IID data). We generalize conformal predictio…

cs.DC2022

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

Xinyu Chen, Marco Minutoli, Jiannan Tian +3

Influence maximization aims to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. Although computing optimal seed se…

eess.SY2022★ 1 cited

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach

Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor +2

We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based m…

cs.LG2022★ 7 cited

Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications

Sai Munikoti, Deepesh Agarwal, Laya Das +2

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly,…

cs.LG2022★ 1 cited

GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

Sai Munikoti, Balasubramaniam Natarajan, Mahantesh Halappanavar

Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread o…

eess.SY2022★ 14 cited

Neural Lyapunov Differentiable Predictive Control

Sayak Mukherjee, Ján Drgoňa, Aaron Tuor +2

We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapuno…