activity
20202025
most citedMaking AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models

165 citations · 249 across the 25 of their papers we have counts for

collaborators

25 papers

eess.SY2025

Learning-Augmented Online Control for Decarbonizing Water Infrastructures

Jianyi Yang, Pengfei Li, Tongxin Li +2

Water infrastructures are essential for drinking water supply, irrigation, fire protection, and other critical applications. However, water pumping systems, which are key to transp…

cs.GT2024

Online Budgeted Matching with General Bids

Jianyi Yang, Pengfei Li, Adam Wierman +1

Online Budgeted Matching (OBM) is a classic problem with important applications in online advertising, online service matching, revenue management, and beyond. Traditional online a…

cs.CY2024★ 1 cited

Towards Socially and Environmentally Responsible AI

Pengfei Li, Yejia Liu, Jianyi Yang +1

The sharply increasing sizes of artificial intelligence (AI) models come with significant energy consumption and environmental footprints, which can disproportionately impact certa…

cs.LG2024★ 1 cited

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…

cs.GT2024

Online Allocation with Replenishable Budgets: Worst Case and Beyond

Jianyi Yang, Pengfei Li, Mohammad Jaminur Islam +1

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online all…

cs.LG2023

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…

cs.LG2023★ 1 cited

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…

cs.AI2023★ 4 cited

Towards Environmentally Equitable AI via Geographical Load Balancing

Pengfei Li, Jianyi Yang, Adam Wierman +1

Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models' enormous environmental footprint has come u…

cs.LG2023★ 1 cited

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 (…

cs.LG2023★ 1 cited

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,…

cs.LG2023

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…

cs.LG2023★ 165 cited

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…

cs.LG2022

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…

cs.LG2022★ 1 cited

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,…

cs.LG2022★ 4 cited

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…

cs.LG2021

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…

cs.LG2021★ 27 cited

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…

eess.AS2021

Masked Acoustic Unit for Mispronunciation Detection and Correction

Zhan Zhang, Yuehai Wang, Jianyi Yang

Computer-Assisted Pronunciation Training (CAPT) plays an important role in language learning. Conventional ASR-based CAPT methods require expensive annotation of the ground truth p…

eess.AS2021

Accent Recognition with Hybrid Phonetic Features

Zhan Zhang, Xi Chen, Yuehai Wang +1

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have rece…

cs.LG2021

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…

cs.LG2020★ 3 cited

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…

cs.LG2020★ 1 cited

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…

eess.AS2020★ 32 cited

Text-Conditioned Transformer for Automatic Pronunciation Error Detection

Zhan Zhang, Yuehai Wang, Jianyi Yang

Automatic pronunciation error detection (APED) plays an important role in the domain of language learning. As for the previous ASR-based APED methods, the decoded results need to b…

cs.LG2020★ 3 cited

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…