可信AI:可信人工智能最新议题 | 第16届中国R会议暨2023X-AGI大会
作者：统计之都第16届中国R会议暨2023X-AGI大会将于2023年11月25-30日在中国人民大学召开，本次会议由中国人民大学统计学院、中国人民大学应用统计科学研究中心、统计之都、原灵科技和中国商业统计学会人工智能分会（筹）主办，由中国人民大学统计学院数据科学与大数据统计系承办，得到宽德投资、明汯投资、和鲸科技、子博设计赞助支持，将以线上会议和线下会议相结合的方式举办。
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下面为您奉上本次中国R会议暨2023X-AGI大会可信AI专场演讲介绍， 本会场主席为常象宇。可信AI专场
时间：2023年11月29日 晚上20:00-22:15
会议地点：
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会场内容介绍
Fair conformal prediction and risk control
Linjun Zhang
个人简介：
Linjun Zhang is an Assistant Professor in the Department of Statistics, at Rutgers University. He obtained his Ph.D. in Statistics at the Wharton School, the University of Pennsylvania in 2019, and received J.Parker Bursk Memorial Prize and Donald S. Murray Prize for excellence in research and teaching, respectively upon graduation. His current research interests include algorithmic fairness, privacy-preserving data analysis, deep learning theory, and high-dimensional statistics.
报告摘要：
Multi-calibration is a powerful and evolving concept originating in the field of algorithmic fairness. For a predictor f that estimates the outcome y given covariates x, and for a function class C, multi-calibration requires that the predictor f(x) and outcome y are indistinguishable under the class of auditors in C. Fairness is captured by incorporating demographic subgroups into the class of functions C. Recent work has shown that, by enriching the class C to incorporate appropriate propensity re-weighting functions, multi-calibration also yields target-independent learning, wherein a model trained on a source domain performs well on unseen, future target domains(approximately) captured by the re-weightings. The multi-calibration notion is extended, and the power of an enriched class of mappings is explored. HappyMap, a generalization of multi-calibration, is proposed, which yields a wide range of new applications, including a new fairness notion for uncertainty quantification (conformal prediction), a novel technique for conformal prediction under covariate shift, and a different approach for fair risk control, while also yielding a unified understanding of several existing seemingly disparate algorithmic fairness notions and target-independent learning approaches. A single HappyMap meta-algorithm is given that captures all these results, together with a sufficiency condition for its success.
Data Intelligence in Machine Learning: A New Pathway Towards Responsible AI
Ruoxi Jia
个人简介：
Ruoxi Jia is an assistant professor in the the Bradley Department of Electrical and Computer Engineering at Virginia Tech. She earned her PhD in the EECS Department from UC Berkeley and a B.S. from Peking University. Jia's research interest lies broadly in the span of machine learning, security, privacy, and cyber-physical systems. Jia's recent work focuses on data-centric and trustworthy machine learning. Ruoxi is the recipient of the NSF CAREER Award, the Chiang Fellowship for Graduate Scholars in Manufacturing and Engineering, the 8108 Alumni Fellowship, and the Okamatsu Fellowship, Virginia’s Commonwealth Cyber Initiative award, Cisco Research Awards, and Amazon-VT Initiative Research Awards. She was selected for the Rising Stars in EECS in 2017. Ruoxi’s work has been featured in multiple media outlets such as New York Times, MIT Technology Review, IEEE Spectrum, and Wired.
报告摘要：
The pivotal role of large datasets in propelling the advancements of modern machine learning applications such as computer vision, natural language processing, and multi-modal learning cannot be overstated. While machine learning algorithms are often indiscriminate aggregators of given data sources, resulting in 'bad data leading to bad outcomes,' the central theme of our research advocates for a more deliberate approach. This involves understanding how data is transduced within a machine learning system, its impact on outcomes, and how one can actively select data for creating an efficient and robust machine learning solution. In pursuit of this goal, we will present a series of our recent works regarding principled frameworks for data sourcing, data selection, and data-based model behavior attribution.
Private Estimation and Inference in HighDimensional Regression with FDR Control
Zhanrui Cai
个人简介：
蔡占锐现为香港大学经管学院助理教授，研究兴趣包括differential privacy, distribution-free inference 等。之前他曾是爱荷华州立大学统计系助理教授，并于卡耐基梅隆大学任博士后研究员。他于宾夕法尼亚州立大学获得博士学位。
报告摘要：
In this paper, we presents novel methodologies for conducting practical differentially private (DP) estimation and inference in high-dimensional linear regression. We start by proposing a differentially private Bayesian Information Criterion (BIC) for selecting the unknown sparsity parameter in DP-Lasso, eliminating the need for prior knowledge of model sparsity, a requisite in the existing literature. Then we propose a differentially private debiased LASSO algorithm that enables privacy-preserving inference on regression parameters. Our proposed method enables accurate and private inference on the regression parameters by leveraging the inherent sparsity of highdimensional linear regression models. Additionally, we address the issue of multiple testing in high-dimensional linear regression by introducing a differentially private multiple testing procedure that controls the false discovery rate (FDR). This allows for accurate and privacy-preserving identification of significant predictors in the regression model. Through extensive simulations and real data analysis, we demonstrate the efficacy of our proposed methods in conducting inference for high-dimensional linear models while safeguarding privacy and controlling the FDR.
通用人工智能时代的人类未来
Xiaohu Zhu
个人简介：
Uplifts Life、Center for Safe AGI和University AI创始人，中国首位通用人工智能安全研究员，谷歌机器学习GDE，Future Forum ’22 中国唯一入选人，Foresight Institute Fellow in Safe AGI、 中国伦理学会技术伦理分会成员。与Foresight Institute、DeepMind、OpenAI、Center for Human-Compatible AI、Future of Humanity Institute 和 Future of Life Institute 长期交流合作。《深入浅出神经网络与深度学习》、《人工智能缔造师》、《人工智能对齐通讯》和《人工智能蓝图》译者。《通用人工智能安全和治理》作者。
报告摘要：
为了确保通用人工智能成为一种释放人类潜能的工具，而不是束缚甚至掌控人类未来的不可控技术，我们必须以开放的心态和坚定的决心面对这个时代，理解挑战，拥抱可能性，并始终努力确保通用人工智能服务于增强，而不是取代，我们共享的人类体验。
关于会议
主办方：
中国人民大学统计学院
中国人民大学应用统计科学研究中心
统计之都
原灵科技
中国商业统计学会人工智能分会（筹）
赞助方：
宽德投资
明汯投资
和鲸科技
子博设计
联系方式
公众号：统计之都
会议邮箱：chinar-2023@cosx.org
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