no code implementations • 7 Feb 2024 • Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang, Zhi-Quan Luo
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making.
no code implementations • 14 Sep 2023 • Yu Gao, Lutong Su, Hao Liang, Yufeng Yue, Yi Yang, Mengyin Fu
In this paper, we propose MC-NeRF, a method that enables joint optimization of both intrinsic and extrinsic parameters alongside NeRF.
no code implementations • ICCV 2023 • Hao Liang, Pietro Perona, Guha Balakrishnan
We validate our method quantitatively by evaluating race and gender biases of three research-grade face recognition models.
no code implementations • 12 Jun 2023 • Hao Liang, Zhi-Quan Luo
Unlike traditional approaches that add or subtract a confidence radius from the empirical risk measures, our proposed schemes evaluate a specific transformation of the empirical distribution based on the distance.
no code implementations • 4 Jun 2023 • Hao Liang, Zhi-Quan Luo
We study finite episodic Markov decision processes incorporating dynamic risk measures to capture risk sensitivity.
no code implementations • 29 Apr 2023 • Hao Liang, Kevin Ni, Guha Balakrishnan
Recent research demonstrates that deep learning models are capable of precisely extracting bio-information (e. g. race, gender and age) from patients' Chest X-Rays (CXRs).
no code implementations • 29 Apr 2023 • Hao Liang, Kevin Ni, Guha Balakrishnan
Recent work demonstrates that images from various chest X-ray datasets contain visual features that are strongly correlated with protected demographic attributes like race and gender.
no code implementations • 7 Feb 2023 • Hao Liang, Josue Ortega Caro, Vikram Maheshri, Ankit B. Patel, Guha Balakrishnan
Our framework is experimental, in that we train several versions of a network with an intervention to a specific hyperparameter, and measure the resulting causal effect of this choice on performance bias when a particular out-of-distribution image perturbation is applied.
no code implementations • 25 Oct 2022 • Hao Liang, Zhi-Quan Luo
We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distributional reinforcement learning (DRL) methods.
Computational Efficiency Distributional Reinforcement Learning +2
no code implementations • 31 Mar 2022 • Guanxing Zhou, Hao Liang, Xinghao Ding, Yue Huang, Xiaotong Tu, Saqlain Abbas
Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location.
1 code implementation • 20 Mar 2022 • Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar
We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget.
no code implementations • 9 Mar 2022 • YuAn Liu, Omid Ardakanian, Ioanis Nikolaidis, Hao Liang
With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market.
no code implementations • 16 Jul 2021 • Hao Liang, Lulan Yu, Guikang Xu, Bhiksha Raj, Rita Singh
With this in perspective, we propose a framework to morph a target face in response to a given voice in a way that facial features are implicitly guided by learned voice-face correlation in this paper.
no code implementations • 10 Mar 2021 • Daniel Groves, Michael Hull, Hao Liang
We prove foundational results about the set of homomorphisms from a finitely generated group to the collection of all fundamental groups of compact 3-manifolds and answer questions of Reid-Wang-Zhou and Agol-Liu.
Geometric Topology Group Theory
1 code implementation • NeurIPS 2020 • Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round.
no code implementations • ACL 2020 • Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, Ambreen Nazir
Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims.
1 code implementation • 21 Feb 2020 • Jianyu Wang, Hao Liang, Gauri Joshi
In this paper, we propose an algorithmic approach named Overlap-Local-SGD (and its momentum variant) to overlap the communication and computation so as to speedup the distributed training procedure.