165 citations · 249 across the 25 of their papers we have counts for
17 papers · 1 filter
Building Socially-Equitable Public Models
Yejia Liu, Jianyi Yang, Pengfei Li +2
Public models offer predictions to a variety of downstream tasks and have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. Ho…
Anytime-Competitive Reinforcement Learning with Policy Prior
Jianyi Yang, Pengfei Li, Tongxin Li +2
This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP). Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the expected…
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
Pengfei Li, Jianyi Yang, Adam Wierman +1
We study a challenging form of Smoothed Online Convex Optimization, a.k.a. SOCO, including multi-step nonlinear switching costs and feedback delay. We propose a novel machine learn…
Learning-Augmented Decentralized Online Convex Optimization in Networks
Pengfei Li, Jianyi Yang, Adam Wierman +1
This paper studies decentralized online convex optimization in a networked multi-agent system and proposes a novel algorithm, Learning-Augmented Decentralized Online optimization (…
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Pengfei Li, Jianyi Yang, Shaolei Ren
Many problems, such as online ad display, can be formulated as online bipartite matching. The crucial challenge lies in the nature of sequentially-revealed online item information,…
Robustified Learning for Online Optimization with Memory Costs
Pengfei Li, Jianyi Yang, Shaolei Ren
Online optimization with memory costs has many real-world applications, where sequential actions are made without knowing the future input. Nonetheless, the memory cost couples the…
Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
Pengfei Li, Jianyi Yang, Mohammad A. Islam +1
The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of…
Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints
Jianyi Yang, Shaolei Ren
Online optimization with multiple budget constraints is challenging since the online decisions over a short time horizon are coupled together by strict inventory constraints. The e…
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity
Jianyi Yang, Shaolei Ren
By integrating domain knowledge with labeled samples, informed machine learning has been emerging to improve the learning performance for a wide range of applications. Nonetheless,…
Expert-Calibrated Learning for Online Optimization with Switching Costs
Pengfei Li, Jianyi Yang, Shaolei Ren
We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping…
Learning for Robust Combinatorial Optimization: Algorithm and Application
Zhihui Shao, Jianyi Yang, Cong Shen +1
Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering l…
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
Bingqian Lu, Jianyi Yang, Weiwen Jiang +2
Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on variou…
Robust Bandit Learning with Imperfect Context
Jianyi Yang, Shaolei Ren
A standard assumption in contextual multi-arm bandit is that the true context is perfectly known before arm selection. Nonetheless, in many practical applications (e.g., cloud reso…
A Quantitative Perspective on Values of Domain Knowledge for Machine Learning
Jianyi Yang, Shaolei Ren
With the exploding popularity of machine learning, domain knowledge in various forms has been playing a crucial role in improving the learning performance, especially when training…
Scaling Up Deep Neural Network Optimization for Edge Inference
Bingqian Lu, Jianyi Yang, Shaolei Ren
Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference directly…
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring
Zhihui Shao, Jianyi Yang, Shaolei Ren
Inference accuracy of deep neural networks (DNNs) is a crucial performance metric, but can vary greatly in practice subject to actual test datasets and is typically unknown due to…
Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets
Zhihui Shao, Jianyi Yang, Shaolei Ren
To increase the trustworthiness of deep neural network (DNN) classifiers, an accurate prediction confidence that represents the true likelihood of correctness is crucial. Towards t…