20 papers
Identifiability of Latent Space Network Models on Anisotropic Thurston Geometries
Marios Papamichalis, Regina Ruane
A latent space network model places the nodes in a metric space and lets the probability of a tie decrease with distance. In a space of constant curvature, pairwise distances deter…
What Does Chain-of-Thought Entropy Measure? A Channel Audit of Scaffolding, Routing, and Content
Marios Papamichalis, Regina Ruane
Entropy over chain-of-thought tokens decides which tokens receive the policy gradient, which get pruned, and whether a run has collapsed, yet each such statistic reads a next-token…
Replicable Conformal Prediction
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomn…
Separating Time-Varying Network Composition from Predictive Dependence under Noisy Network Measurement
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice ins…
Which Question Is Your Attention Metric Answering? Attention Rows as Compositional Data
Marios Papamichalis, Regina Ruane
Each row of a transformer's attention matrix is a probability distribution over tokens, and in trained models most of that probability lands on a single \emph{sink} token, usually…
Bayesian Predictive Synthesis for Dynamic Networks: Forecasting and Identifying Structural Mechanisms
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
Networks are shaped by competing structural mechanisms, such as communities, geometry, or hubs. In a dynamic network the most predictive mechanism can change, and a model tied to o…
Minimax Synthesis of Network Mechanisms
Marios Papamichalis, Regina Ruane
A single observed network reflects several mechanisms at once: communities, hubs, and clustering coexist in one graph, each a different model. We treat the network as a combination…
How Eviction Court Governs: A Statistical Analysis of Bargaining, Templates, and Debt in Philadelphia
Marios Papamichalis, Regina Ruane
We analyze downstream courtroom governance in Philadelphia eviction cases using 755,004 Municipal Court landlord--tenant records filed from 1969 through 2022. Post-filing case proc…
High-Volume Plaintiff-Side Counsel and Single-Appearance Eviction Cases in Philadelphia
Marios Papamichalis, Regina Ruane
Among 755,004 Philadelphia landlord--tenant records filed during 1969-2022, 396,163 residential cases involve tenants who appear exactly once in the observed docket. In unadjusted…
Legal Infrastructure Organizes Eviction: Evidence from Philadelphia
Marios Papamichalis, Regina Ruane
We analyze the filing-side legal infrastructure of eviction using 755,004 Philadelphia Municipal Court landlord-tenant records filed between 1969 and 2022, of which 747,125 are res…
Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
Marios Papamichalis, Regina Ruane
Contact-rich robot dynamics are hybrid: a single observation can match several latent states and contact regimes (free, impact, stick--slip). A standard amortized filter that place…
Effects of Training Data Quality on Classifier Performance
Alan F. Karr, Regina Ruane
We describe extensive numerical experiments assessing and quantifying how classifier performance depends on the quality of the training data, a frequently neglected component of th…
Evidence thresholds of collapsed structured block models
Marios Papamichalis, Regina Ruane
A stochastic block model explains a network by splitting its nodes into communities and a matrix of tie probabilities between them. Collapsing those probabilities out scores each s…
Decision-Theoretic Robustness for Network Models
Marios Papamichalis, Regina Ruane, Simon Lunagomez +1
Bayesian network models (Erdos Renyi, stochastic block models, random dot product graphs, graphons) are widely used in neuroscience, epidemiology, and the social sciences, yet real…
Multiscale Localized Inference for Networks with a Measured Vertex Coordinate
Marios Papamichalis, Regina Ruane
In many networks each vertex has a position measured from outside the network: a neuron's location along the body axis, a residue's index along a protein sequence, a genomic bin's…
Graphon-Level Bayesian Predictive Synthesis for Random Network
Marios Papamichalis, Regina Ruane
Analysts often fit several models to the same network. Each estimates a graphon, the function giving the probability of a link between two nodes. Reporting one predictive graphon m…
Decomposing Degree Assortativity in Sparse Spatial Networks
Marios Papamichalis, Regina Ruane
Spatial networks are typically assortative: well-connected nodes link to other well-connected nodes, and the usual reading is sorting, popular nodes seeking each other out. In spac…
State-Space Modeling of Time-Varying Spillovers on Networks
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
Crime counts in city neighbourhoods, disease counts in counties, and sales at firms joined by trade are naturally represented as counts on the nodes of a network. In each case a hi…
Radial Compensation: The Inverse Base-Distribution Problem for Chart-Based Generative Models on Riemannian Manifolds
Marios Papamichalis, Regina Ruane
Latent-variable models on spheres and hyperbolic spaces usually draw a Gaussian in the tangent space at a base point and push it onto the manifold. On these spaces the distance fro…
Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier
Alan F. Karr, Zac Bowen, Adam A. Porter +1
For a Bayes classifier whose input space is a graph, we study the structure of the boundary, which comprises those points for which at least one neighbor is classified differently.…