Search Results for author: Jialu Wang

Found 19 papers, 9 papers with code

WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization

no code implementations22 Mar 2024 Jialu Wang, Kaichen Zhou, Andrew Markham, Niki Trigoni

Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor.

Camera Relocalization Image Reconstruction +1

Fair Classifiers Without Fair Training: An Influence-Guided Data Sampling Approach

no code implementations20 Feb 2024 Jinlong Pang, Jialu Wang, Zhaowei Zhu, Yuanshun Yao, Chen Qian, Yang Liu

A fair classifier should ensure the benefit of people from different groups, while the group information is often sensitive and unsuitable for model training.

Attribute Fairness

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

1 code implementation19 Nov 2023 Zhaowei Zhu, Jialu Wang, Hao Cheng, Yang Liu

Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification.

Language Modelling

Procedural Fairness Through Decoupling Objectionable Data Generating Components

1 code implementation5 Nov 2023 Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i. e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals.

Decision Making Fairness

T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation

no code implementations1 Jun 2023 Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu, Xin Eric Wang

In this work, we seek to measure more complex human biases exist in the task of text-to-image generations.

Text-to-Image Generation

Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment

1 code implementation2 May 2023 Zhen Zhang, Jialu Wang, Xin Eric Wang

Extensive experiments on XTD and Multi30K datasets, covering 11 languages under zero-shot, few-shot, and full-dataset learning scenarios, show that our framework significantly reduces the multilingual disparities among languages and improves cross-lingual transfer results, especially in low-resource scenarios, while only keeping and fine-tuning an extremely small number of parameters compared to the full model (e. g., Our framework only requires 0. 16\% additional parameters of a full-model for each language in the few-shot learning scenario).

Cross-Lingual Transfer Few-Shot Learning +1

Deep Learning based Multi-Label Image Classification of Protest Activities

no code implementations10 Jan 2023 Yingzhou Lu, Kosaku Sato, Jialu Wang

With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication.

Multi-Label Image Classification

JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents

no code implementations28 Aug 2022 Kaizhi Zheng, Kaiwen Zhou, Jing Gu, Yue Fan, Jialu Wang, Zonglin Di, Xuehai He, Xin Eric Wang

Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc.

Action Generation Common Sense Reasoning +1

Understanding Instance-Level Impact of Fairness Constraints

1 code implementation30 Jun 2022 Jialu Wang, Xin Eric Wang, Yang Liu

A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias.


Fairness Transferability Subject to Bounded Distribution Shift

1 code implementation31 May 2022 Yatong Chen, Reilly Raab, Jialu Wang, Yang Liu

Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound?

BIG-bench Machine Learning Fairness

Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

2 code implementations2 Feb 2022 Zhaowei Zhu, Jialu Wang, Yang Liu

We observe that tasks with lower-quality features fail to meet the anchor-point or clusterability condition, due to the coexistence of both uninformative and informative representations.

text-classification Text Classification

Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search

1 code implementation EMNLP 2021 Jialu Wang, Yang Liu, Xin Eric Wang

Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good.

Image Retrieval Natural Language Queries

Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial

1 code implementation NeurIPS 2021 Yang Liu, Jialu Wang

In this paper, we first quantify the trade-offs introduced by increasing a certain group of instances' label noise rate w. r. t.

Fairness Informativeness +1

Assessing Multilingual Fairness in Pre-trained Multimodal Representations

no code implementations Findings (ACL) 2022 Jialu Wang, Yang Liu, Xin Eric Wang

To answer these questions, we view language as the fairness recipient and introduce two new fairness notions, multilingual individual fairness and multilingual group fairness, for pre-trained multimodal models.


Fair Classification with Group-Dependent Label Noise

1 code implementation31 Oct 2020 Jialu Wang, Yang Liu, Caleb Levy

We begin by presenting analytical results which show that naively imposing parity constraints on demographic disparity measures, without accounting for heterogeneous and group-dependent error rates, can decrease both the accuracy and the fairness of the resulting classifier.

Classification Fairness +1

Linear Classifiers that Encourage Constructive Adaptation

no code implementations31 Oct 2020 Yatong Chen, Jialu Wang, Yang Liu

Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome.

Decision Making General Classification

AceKG: A Large-scale Knowledge Graph for Academic Data Mining

no code implementations23 Jul 2018 Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Wei-Nan Zhang, Xinbing Wang

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.

Community Detection Entity Alignment +3

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