no code implementations • SmiLa (LREC) 2022 • Ruihan Wu, Antonia Hamilton, Sarah White
We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups.
no code implementations • 5 Feb 2024 • Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging.
no code implementations • 4 Aug 2023 • Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
In this work, we look at how to use generic large-scale public data to improve the quality of differentially private image generation in Generative Adversarial Networks (GANs), and provide an improved method that uses public data effectively.
1 code implementation • 19 Oct 2022 • Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy.
no code implementations • 16 Jun 2022 • Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.
1 code implementation • 25 Feb 2022 • Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.
no code implementations • NeurIPS 2021 • Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger
Machine learning models often encounter distribution shifts when deployed in the real world.
1 code implementation • NeurIPS 2021 • Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc.
1 code implementation • 9 Feb 2021 • Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
1 code implementation • 6 Nov 2020 • Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
no code implementations • 24 Feb 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
Modern neural networks often contain significantly more parameters than the size of their training data.
no code implementations • ICLR 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
The complexity of large-scale neural networks can lead to poor understanding of their internal details.
1 code implementation • 24 Feb 2018 • Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs).
no code implementations • 13 Feb 2018 • Ruihan Wu, Zheng Yu, Wei Chen
In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$.
Social and Information Networks Data Structures and Algorithms
no code implementations • 18 Feb 2017 • Lunjia Hu, Ruihan Wu, Tianhong Li, Li-Wei Wang
The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\mathcal C$ according to the recursive teaching model.