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1. Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise

   Tjandra, Donna; Wiens, Jenna (June 2023, Conference on Health, Inference, and Learning)

   Noisy training labels can hurt model performance. Most approaches that aim to address label noise assume label noise is independent from the input features. In practice, however, label noise is often feature or \textit{instance-dependent}, and therefore biased (i.e., some instances are more likely to be mislabeled than others). E.g., in clinical care, female patients are more likely to be under-diagnosed for cardiovascular disease compared to male patients. Approaches that ignore this dependence can produce models with poor discriminative performance, and in many healthcare settings, can exacerbate issues around health disparities. In light of these limitations, we propose a two-stage approach to learn in the presence instance-dependent label noise. Our approach utilizes \textit{\anchor points}, a small subset of data for which we know the observed and ground truth labels. On several tasks, our approach leads to consistent improvements over the state-of-the-art in discriminative performance (AUROC) while mitigating bias (area under the equalized odds curve, AUEOC). For example, when predicting acute respiratory failure onset on the MIMIC-III dataset, our approach achieves a harmonic mean (AUROC and AUEOC) of 0.84 (SD [standard deviation] 0.01) while that of the next best baseline is 0.81 (SD 0.01). Overall, our approach improves accuracy while mitigating potential bias compared to existing approaches in the presence of instance-dependent label noise. 

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2. Optimal Budget Allocation for Crowdsourcing Labels for Graphs

   Adithya Kulkarni, Mohna Chakraborty (July 2023, Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI))

   Crowdsourcing is an effective and efficient paradigm for obtaining labels for unlabeled corpus employing crowd workers. This work considers the budget allocation problem for a generalized setting on a graph of instances to be labeled where edges encode instance dependencies. Specifically, given a graph and a labeling budget, we propose an optimal policy to allocate the budget among the instances to maximize the overall labeling accuracy. We formulate the problem as a Bayesian Markov Decision Process (MDP), where we define our task as an optimization problem that maximizes the overall label accuracy under budget constraints. Then, we propose a novel stage-wise reward function that considers the effect of worker labels on the whole graph at each timestamp. This reward function is utilized to find an optimal policy for the optimization problem. Theoretically, we show that our proposed policies are consistent when the budget is infinite. We conduct extensive experiments on five real-world graph datasets and demonstrate the effectiveness of the proposed policies to achieve a higher label accuracy under budget constraints. 

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3. Optimal Budget Allocation for Crowdsourcing Labels for Graphs

   Adithya Kulkarni, Mohna Chakraborty (July 2023, Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI))

   Crowdsourcing is an effective and efficient paradigm for obtaining labels for unlabeled corpus employing crowd workers. This work considers the budget allocation problem for a generalized setting on a graph of instances to be labeled where edges encode instance dependencies. Specifically, given a graph and a labeling budget, we propose an optimal policy to allocate the budget among the instances to maximize the overall labeling accuracy. We formulate the problem as a Bayesian Markov Decision Process (MDP), where we define our task as an optimization problem that maximizes the overall label accuracy under budget constraints. Then, we propose a novel stage-wise reward function that considers the effect of worker labels on the whole graph at each timestamp. This reward function is utilized to find an optimal policy for the optimization problem. Theoretically, we show that our proposed policies are consistent when the budget is infinite. We conduct extensive experiments on five real-world graph datasets and demonstrate the effectiveness of the proposed policies to achieve a higher label accuracy under budget constraints. 

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4. Optimal Budget Allocation for Crowdsourcing Labels for Graphs

   Kulkarni, Adithya; Chakraborty, Mohna; Xie, Sihong; Li, Qi (July 2023, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)

   Evans, Robin J.; Shpitser, Ilya (Ed.)

   Crowdsourcing is an effective and efficient paradigm for obtaining labels for unlabeled corpus employing crowd workers. This work considers the budget allocation problem for a generalized setting on a graph of instances to be labeled where edges encode instance dependencies. Specifically, given a graph and a labeling budget, we propose an optimal policy to allocate the budget among the instances to maximize the overall labeling accuracy. We formulate the problem as a Bayesian Markov Decision Process (MDP), where we define our task as an optimization problem that maximizes the overall label accuracy under budget constraints. Then, we propose a novel stage-wise reward function that considers the effect of worker labels on the whole graph at each timestamp. This reward function is utilized to find an optimal policy for the optimization problem. Theoretically, we show that our proposed policies are consistent when the budget is infinite. We conduct extensive experiments on five real-world graph datasets and demonstrate the effectiveness of the proposed policies to achieve a higher label accuracy under budget constraints. 

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5. Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

   Zhu, Zhaowei; Song, Yiwen; Liu, Yang (July 2021, roceedings of the 38th International Conference on Machine Learning)

   Meila, Marina; Zhang, Tong (Ed.)

   The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy labels. Existing works heavily rely on finding “anchor points” or their approximates, defined as instances belonging to a particular class almost surely. Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points. In this paper, we propose an alternative option to the above task. Our main contribution is the discovery of an efficient estimation procedure based on a clusterability condition. We prove that with clusterable representations of features, using up to third-order consensuses of noisy labels among neighbor representations is sufficient to estimate a unique transition matrix. Compared with methods using anchor points, our approach uses substantially more instances and benefits from a much better sample complexity. We demonstrate the estimation accuracy and advantages of our estimates using both synthetic noisy labels (on CIFAR-10/100) and real human-level noisy labels (on Clothing1M and our self-collected human-annotated CIFAR-10). Our code and human-level noisy CIFAR-10 labels are available at https://github.com/UCSC-REAL/HOC. 

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