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1. Learning from Label Proportions:A Mutual Contamination Framework

   Scott, Clayton; Zhang, Jianxin (January 2020, Advances in neural information processing systems)

   null (Ed.)

   Learning from label proportions (LLP) is a weakly supervised setting for classification in whichunlabeled training instances are grouped into bags, and each bag is annotated with the proportion ofeach class occurring in that bag. Prior work on LLP has yet to establish a consistent learning procedure,nor does there exist a theoretically justified, general purpose training criterion. In this work we addressthese two issues by posing LLP in terms of mutual contamination models (MCMs), which have recentlybeen applied successfully to study various other weak supervision settings. In the process, we establishseveral novel technical results for MCMs, including unbiased losses and generalization error bounds undernon-iid sampling plans. We also point out the limitations ofa common experimental setting for LLP,and propose a new one based on our MCM framework. 

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2. Hardness of Learning Boolean Functions from Label Proportions

   https://doi.org/10.4230/LIPIcs.FSTTCS.2023.37  

   Guruswami, Venkatesan; Saket, Rishi (January 2023, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bouyer, Patricia; Srinivasan, Srikanth (Ed.)

   In recent years the framework of learning from label proportions (LLP) has been gaining importance in machine learning. In this setting, the training examples are aggregated into subsets or bags and only the average label per bag is available for learning an example-level predictor. This generalizes traditional PAC learning which is the special case of unit-sized bags. The computational learning aspects of LLP were studied in recent works [R. Saket, 2021; R. Saket, 2022] which showed algorithms and hardness for learning halfspaces in the LLP setting. In this work we focus on the intractability of LLP learning Boolean functions. Our first result shows that given a collection of bags of size at most 2 which are consistent with an OR function, it is NP-hard to find a CNF of constantly many clauses which satisfies any constant-fraction of the bags. This is in contrast with the work of [R. Saket, 2021] which gave a (2/5)-approximation for learning ORs using a halfspace. Thus, our result provides a separation between constant clause CNFs and halfspaces as hypotheses for LLP learning ORs. Next, we prove the hardness of satisfying more than 1/2 + o(1) fraction of such bags using a t-DNF (i.e. DNF where each term has ≤ t literals) for any constant t. In usual PAC learning such a hardness was known [S. Khot and R. Saket, 2008] only for learning noisy ORs. We also study the learnability of parities and show that it is NP-hard to satisfy more than (q/2^{q-1} + o(1))-fraction of q-sized bags which are consistent with a parity using a parity, while a random parity based algorithm achieves a (1/2^{q-2})-approximation. 

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3. Learning from Label Proportions by Learning with Label Noise

   Zhang, Jianxin; Wang, Yutong; Scott, Clayton (January 2022, Conference on Neural Information Processing Systems (NeurIPS 2022))

   Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. The task is to learn a classifier to predict the labels of future individual instances. Prior work on LLP for multi-class data has yet to develop a theoretically grounded algorithm. In this work, we propose an approach to LLP based on a reduction to learning with label noise, using the forward correction (FC) loss of Patrini et al. [30]. We establish an excess risk bound and generalization error analysis for our approach, while also extending the theory of the FC loss which may be of independent interest. Our approach demonstrates improved empirical performance in deep learning scenarios across multiple datasets and architectures, compared to the leading methods. 

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4. Learning from Label Proportions by Learning with Label Noise

   Zhang, Jianxin; Wang, Yutong; Scott, Clayton (January 2022, 36th Conference on Neural Information Processing Systems (NeurIPS 2022))

   Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. The task is to learn a classifier to predict the labels of future individual instances. Prior work on LLP for multi-class data has yet to develop a theoretically grounded algorithm. In this work, we propose an approach to LLP based on a reduction to learning with label noise, using the forward correction (FC) loss of Patrini et al. [30]. We establish an excess risk bound and generalization error analysis for our approach, while also extending the theory of the FC loss which may be of independent interest. Our approach demonstrates improved empirical performance in deep learning scenarios across multiple datasets and architectures, compared to the leading methods. 

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5. Generalized Cycle Benchmarking Algorithm for Characterizing Midcircuit Measurements

   https://doi.org/10.1103/PRXQuantum.6.010310  

   Zhang, Zhihan; Chen, Senrui; Liu, Yunchao; Jiang, Liang (January 2025, PRX Quantum)

   Midcircuit measurements (MCMs) are crucial ingredients in the development of fault-tolerant quantum computation. While there have been rapid experimental progresses in realizing MCMs, a systematic method for characterizing noisy MCMs is still under exploration. In this work, we develop a cycle benchmarking (CB)-type algorithm to characterize noisy MCMs. The key idea is to use a joint Fourier transform on the classical and quantum registers and then estimate parameters in the Fourier space, analogous to Pauli fidelities used in CB-type algorithms for characterizing the Pauli-noise channel of Clifford gates. Furthermore, we develop a theory of the noise learnability of MCMs, which determines what information can be learned about the noise model (in the presence of state preparation and terminating measurement noise) and what cannot, which shows that all learnable information can be learned using our algorithm. As an application, we show how to use the learned information to test the independence between measurement noise and state-preparation noise in an MCM. Finally, we conduct numerical simulations to illustrate the practical applicability of the algorithm. Similar to other CB-type algorithms, we expect the algorithm to provide a useful toolkit that is of experimental interest. Published by the American Physical Society2025 

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