1 code implementation • 4 Jun 2024 • Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren
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.
no code implementations • 17 Sep 2023 • Bintao He, Fa Zhang, Chenjie Feng, Jianyi Yang, Xin Gao, Renmin Han
Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps.
1 code implementation • 20 Jun 2023 • Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren
The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.
no code implementations • 16 Jun 2023 • Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren
This paper studies decentralized online convex optimization in a networked multi-agent system and proposes a novel algorithm, Learning-Augmented Decentralized Online optimization (LADO), for individual agents to select actions only based on local online information.
1 code implementation • 31 May 2023 • Pengfei Li, Jianyi Yang, Shaolei Ren
The key novelty of LOMAR is a new online switching operation which, based on a judicious condition to hedge against future uncertainties, decides whether to follow the expert's decision or the RL decision for each online item.
no code implementations • 1 May 2023 • Pengfei Li, Jianyi Yang, Shaolei Ren
In this paper, we propose a novel expert-robustified learning (ERL) approach, achieving {both} good average performance and robustness.
1 code implementation • 6 Apr 2023 • Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren
To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example by addressing their own water footprint.
no code implementations • 3 Dec 2022 • 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.
no code implementations • 2 Jul 2022 • Jianyi Yang, Shaolei Ren
Based on the theoretical analysis, we propose a generalized informed training objective to better exploit the benefits of knowledge and balance the label and knowledge imperfectness, which is validated by the population risk bound.
no code implementations • 18 Apr 2022 • Pengfei Li, Jianyi Yang, Shaolei Ren
Nonetheless, by using the standard practice of training an ML model as a standalone optimizer and plugging it into an ML-augmented algorithm, the average cost performance can be highly unsatisfactory.
no code implementations • 20 Dec 2021 • Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren
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 lower runtime complexity than conventional solvers.
1 code implementation • 1 Nov 2021 • Bingqian Lu, Jianyi Yang, Weiwen Jiang, Yiyu Shi, Shaolei Ren
A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures.
Hardware Aware Neural Architecture Search Neural Architecture Search
no code implementations • 12 Aug 2021 • Zhan Zhang, Yuehai Wang, Jianyi Yang
Computer-Assisted Pronunciation Training (CAPT) plays an important role in language learning.
no code implementations • 5 May 2021 • Zhan Zhang, Xi Chen, Yuehai Wang, Jianyi Yang
The performance of voice-controlled systems is usually influenced by accented speech.
no code implementations • 9 Feb 2021 • Jianyi Yang, Shaolei Ren
A standard assumption in contextual multi-arm bandit is that the true context is perfectly known before arm selection.
no code implementations • 17 Nov 2020 • 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 data is limited.
no code implementations • 1 Sep 2020 • Bingqian Lu, Jianyi Yang, Shaolei Ren
In the first approach, we reuse the performance predictors built on a proxy device, and leverage the performance monotonicity to scale up the DNN optimization without re-building performance predictors for each different device.
no code implementations • 28 Aug 2020 • Zhan Zhang, Yuehai Wang, Jianyi Yang
In this paper, we propose to use the target text as an extra condition for the Transformer backbone to handle the APED task.
no code implementations • 3 Jul 2020 • Zhihui Shao, Jianyi Yang, Shaolei Ren
In this paper, we address trustworthiness of DNNs by using post-hoc processing to monitor the true inference accuracy on a user's dataset.
Ranked #28 on Image Classification on STL-10
no code implementations • 16 Jun 2020 • Zhihui Shao, Jianyi Yang, Shaolei Ren
In this paper, we propose a new post-hoc confidence calibration method, called CCAC (Confidence Calibration with an Auxiliary Class), for DNN classifiers on OOD datasets.
no code implementations • 12 Mar 2018 • Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S. Pande
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.