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1. Experimental shot-by-shot estimation of quantum measurement accuracy

   N. Fajar R. Annafianto, Ivan A. (May 2021, CLEO 2021)

   null (Ed.)

   We show the direct correspondence between Bayesian probabilities obtained by the adaptive quantum measurement and experimentally observed Kholmogorov probabilities. We demonstrate the single-“shot” accuracy estimation for every individual quantum measurement outcome using these Bayesian probabilities. 

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2. Single-Shot VR

   https://doi.org/10.1145/3588037.3595396  

   Qin, Yingsi; Chen, Wei-Yu; O'Toole, Matthew; Sankaranarayanan, Aswin C. (July 2023, SIGGRAPH Emerging Technologies)
3. Zero-Shot Object Counting

   https://doi.org/10.1109/CVPR52729.2023.01492  

   Xu, Jingyi; Le, Hieu; Nguyen, Vu; Ranjan, Viresh; Samaras, Dimitris (June 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
4. Federated Few-shot Learning

   https://doi.org/10.1145/3580305.3599347  

   Wang, Song; Fu, Xingbo; Ding, Kaize; Chen, Chen; Chen, Huiyuan; Li, Jundong (August 2023, ACM)

   Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients can only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines. 

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5. Zero-shot causal learning

   Nilforoshan, Hamed; Moor, Michael; Roohani, Yusuf; Chen, Yining; Surina, Anja; Yasunaga, Michihiro; Oblak, Sara; Leskovec, Jure (December 2023, Advances in neural information processing systems)

   Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address. Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention’s effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug’s attributes) and individual features (e.g., a patient’s history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, CaML’s zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. 

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