arXiv subjects

Patrick Stinson

Publications and source records attributed to Patrick Stinson.

4 recordsLinked to original sources

arXiv2026

Dual-Primal Graph VAEs for Noisy Label Aggregation

Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alternative to classical Bayesian models which require specifying a family of generative models used for inference. However, current models either still rely on fairly simple generative models for inference or require pseudo-labels or synthetic data to train the aggregate classifier. We propose a graph VAE architecture in which the decoder and encoder use GAT-based message passing on the adjacency graph of a crowdsourced dataset and its dual, respectively. The ground-truth labels are treated as latent variables, enabling unsupervised representation learning without needing to train a separate classifier. We show our model achieves state of the art performance on crowdsourcing benchmarks. We then demonstrate the generality of our approach by showing how the original crowdsourcing graph can be augmented to incorporate side information such as representations from neural network classifiers trained on the noisy labels to substantially boost their classification performance at test time.

cs.LG↗

arXiv2025

Collective inference of the truth of propositions from crowd probability judgments

Every day, we judge the probability of propositions. When we communicate graded confidence (e.g. "I am 90% sure"), we enable others to gauge how much weight to attach to our judgment. Ideally, people should share their judgments to reach more accurate conclusions collectively. Peer-to-peer tools for collective inference could help debunk disinformation and amplify reliable information on social networks, improving democratic discourse. However, individuals fall short of the ideal of well-calibrated probability judgments, and group dynamics can amplify errors and polarize opinions. Here, we connect insights from cognitive science, structured expert judgment, and crowdsourcing to infer the truth of propositions from human probability judgments. In an online experiment, 376 participants judged the probability of each of 1,200 general-knowledge claims for which we have ground truth (451,200 ratings). Aggregating binary judgments by majority vote already exhibits the "wisdom of the crowd"--the superior accuracy of collective inferences relative to individual inferences. However, using continuous probability ratings and accounting for individual accuracy and calibration significantly improves collective inferences. Peer judgment behavior can be modeled probabilistically, and individual parameters capturing each peer's accuracy and miscalibration can be inferred jointly with the claim probabilities. This unsupervised approach can be complemented by supervised methods relying on truth labels to learn models that achieve well-calibrated collective inference. The algorithms we introduce can empower groups of collaborators and online communities to pool their distributed intelligence and jointly judge the probability of propositions with a well-calibrated sense of uncertainty.

q-bio.QM↗

arXiv2016

Partition Functions from Rao-Blackwellized Tempered Sampling

Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliary inverse temperature variable. Our method exploits the multinomial probability law of the inverse temperatures, and provides estimates of the partition function in terms of a simple quotient of Rao-Blackwellized marginal inverse temperature probability estimates, which are updated while sampling. We show that the method has interesting connections with several alternative popular methods, and offers some significant advantages. In particular, we empirically find that the new method provides more accurate estimates than Annealed Importance Sampling when calculating partition functions of large Restricted Boltzmann Machines (RBM); moreover, the method is sufficiently accurate to track training and validation log-likelihoods during learning of RBMs, at minimal computational cost.

stat.ML↗

arXiv2014

A shotgun sampling solution for the common input problem in neural connectivity inference

Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The `common input' problem presents the major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons from correlations induced by common input from unobserved neurons. Since available recording techniques allow us to sample from only a small fraction of large networks simultaneously with sufficient temporal resolution, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a `shotgun' experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe most of it during the entire experiment. Using a generalized linear model for a spiking recurrent neural network, we develop scalable approximate Bayesian methods to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that, using this method: (1) The shotgun experimental design can eliminate the biases induced by common input effects. (2) Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, could be quickly and accurately estimated. (3) Performance can be improved if we exploit prior information about the probability of having a connection between two neurons, its dependence on neuronal cell types (e.g., Dale's law), or its dependence on the distance between neurons.

q-bio.NC↗