Statistical framework for inexact graph matching with errorfully observed graphs.
problem Finding a correspondence between graphs with errors.
method Introducing a corrupting channel model and using maximum likelihood estimation.
result Maximum likelihood estimation is a solution to the inexact graph matching problem.
While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understand…
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. In many applications, we are faced with the reality of errorfully observed graphs. That is, the existence of an edge between two vert…
ASAC uses actor-critic models to optimize observation selection in medical settings.
problem Optimizing observation selection in costly sequential observation scenarios.
method ASAC framework with selector and predictor networks, using actor-critic models for training.
result ASAC significantly outperforms state-of-the-art methods in real-world medical datasets.
Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the obs…
Consider observing an undirected network that is `noisy' in the sense that there are Type I and Type II errors in the observation of edges. Such errors can arise, for example, in the context of inferring gene regulatory networks in genomics or functional connectivity networks in neuroscience. Given a single observed ne…
Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown. In this paper we formalize the Adaptive Graph Exploring problem. We assume that we are …
GWINs improve classifier accuracy by translating uncertain observations.
problem Improving accuracy of uncertain observations in classifiers.
method Generative network recovers correct observation distributions, reject option allows for uncertain predictions.
result GWINs significantly improve classifier accuracy on benchmark datasets.
A controller learns to control a nonlinear plant with unknown model and partial observation using continuous deep Q-learning.
problem Designing a controller for a nonlinear plant with unknown model and partial sensor observation under network delays.
method Continuous deep Q-learning applied to an extended state including past control inputs and outputs.
result The controller can learn a robust control policy to network delays with partial sensor observation.
Theoretical analysis of deep neural networks for time series data.
problem Theoretical development for deep neural networks on temporally dependent observations is lacking.
method Established non-asymptotic bounds for prediction error of deep neural networks under mixing-type assumptions.
result Deep neural networks can model non-linear time series data with additional logarithmic factors due to dependence.
The paper explores how to learn from incomplete online social networks.
problem Learning from partially observed networks via node querying.
method Developed algorithms NOL* for sequential node querying to maximize network observability.
result It is possible to sequentially learn which nodes to query for maximal network observability.
Temporal-difference (TD) networks are a class of predictive state representations that use well-established TD methods to learn models of partially observable dynamical systems. Previous research with TD networks has dealt only with dynamical systems with finite sets of observations and actions. We present an algorithm…
PCBM improves neural network generalization by partially observing concepts.
problem Decreased generalization performance due to observing all concepts in CBM.
method Developed a theoretical analysis of PCBM's Bayesian generalization error.
result PCBM's generalization error is lower than CBM's due to partial concept observation.
A novel disentangled graph autoencoder improves treatment effect estimation from networked observational data.
problem Treatment effect estimation from observational data is challenging due to unconfoundedness assumption and latent confounders.
method Proposes a disentangled variational graph autoencoder to disentangle latent factors and enforce factor independence.
result Extensive experiments show superior performance compared to state-of-the-art approaches.
New method for inferring network topology from partial data.
problem Inferring network topology from limited node data.
method Vector autoregressive model and Gaussian mixture algorithm.
result The proposed method converges to the network combination matrix in probability.
Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., information diffusion, virus propagation) occurring over the unobserved ne…
New method uncovers hidden causal connections in multivariate point process networks.
problem Unobserved hidden variables confound causal discovery in high-dimensional point process networks.
method Proposes a deconfounding procedure to estimate causal interactions among observed nodes with unknown unobserved processes.
result The method accurately identifies causal interactions among observed processes, even with hidden variables.
Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network structure from noisy observations at a subset of nodes. Available methods for topology inference t…
Develops a new method for network-linked data using Gaussian graphical models.
problem Graphical models for network-linked data violate independence and identically distributed assumptions.
method Proposes a Gaussian graphical model for network-linked data with varying mean vectors and smooth transitions over the network. Efficient estimation algorithm developed.
result Demonstrates effectiveness on simulated and real data, obtaining meaningful results on a statistics coauthorship network.
CONE evaluates treatment assignment functions using networked observational data to mitigate hidden confounding bias.
problem Evaluate treatment assignment functions using networked observational data with hidden confounders.
method CONE framework that learns partial representations of latent confounders and combines them for counterfactual evaluation.
result Network information mitigates hidden confounding bias in counterfactual evaluation.
Paper shows minimum observation time for network recovery.
problem Inferring latent networks from event-based observations.
method Two-stage estimator using clipped and binned event data.
result Observation time of order log(d) is sufficient and necessary.
New approach to neural networks by incorporating observation noise and arbitrary prior means.
problem Misspecification on noisy data and limitations of NTK-GP equivalence.
method Introducing a regularizer for observation noise and proposing a shifted network for arbitrary prior means.
result Removes key obstacles to practical Gaussian process modeling in neural networks.
Networks capture our intuition about relationships in the world. They describe the friendships between Facebook users, interactions in financial markets, and synapses connecting neurons in the brain. These networks are richly structured with cliques of friends, sectors of stocks, and a smorgasbord of cell types that go…
The study explores how partial observations of network nodes can infer the graph structure.
problem How much information does observing a limited fraction of network nodes provide about the underlying graph structure?
method The article examines the inverse problem of graph learning under partial observability, focusing on decentralized processing strategies and large-scale networks.
result Partial observations of network nodes can still be sufficient to discover the graph linking the probed nodes, despite the presence of unobserved nodes.
Validates neural networks inputs to protect against adversarial examples.
problem Ensuring neural networks robustness against adversarial attacks.
method Runtime local robustness verification based on normal distribution of robustness radii.
result Improves neural network accuracy and protects against adversarial examples.
Study examines consistent graph recovery from partial network data.
problem Consistent graph recovery from partially observed diffusion networks.
method Developed estimators for combination matrix under partial observability.
result Node degrees' statistical concentration enables consistent graph learning.
Deep learning predicts fluid dynamics parameters from few samples.
problem Challenging computational fluid dynamics problems requiring many expensive PDE solutions.
method Deep artificial neural networks trained on a few samples to predict input parameters to observable.
result Robust and efficient neural network approximations of parameters to observable map, with low prediction errors and computational savings.
New method combines observational and interventional data for causal model learning.
problem Identifying causal structures from observational data alone is limited.
method Continuous optimization and neural networks for integrating observational and interventional data.
result Strong benchmark results on structure recovery tasks.
Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of …
New algorithm learns Bayesian network structures with fewer samples.
problem Learning Bayesian network structures with limited observational data.
method Active sampling strategy to select variables for observation.
result Active algorithm finds structures close to optimal with fewer samples.
DeepTMR reorders matrices without prior knowledge of structural patterns.
problem Matrix reordering without prior structural knowledge.
method DeepTMR uses a neural network to automatically extract features and reorder matrices.
result Trained network produces denoised mean matrix for visualization.
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architec…
Recurrent networks learn beliefs from history in partially observable environments.
problem Learning optimal policies in partially observable environments.
method Trained recurrent neural networks to approximate value functions, measuring mutual information between hidden states and beliefs.
result Recurrent networks' hidden states correlate with beliefs of relevant state variables, improving expected return.
The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.
problem Nonparametric estimation of drift function for diffusion processes from high-frequency discrete observations.
method Neural network-based estimator for drift function estimation.
result Derives a non-asymptotic convergence rate for the neural network estimator.
Spectral method for detecting communities in time-varying networks from noisy signals.
problem Detect communities in time-varying networks from noisy signals.
method Spectral algorithm based on latent stochastic blockmodel.
result Consistent recovery of community structure in time-varying networks.
Detects adversarial inputs in deep learning models without modifying the main network.
problem Vulnerability of deep learning models to adversarial inputs.
method Augment main network with observer networks that classify inputs as clean or adversarial.
result 99.5% detection accuracy on MNIST and 97.5% on CIFAR-10 datasets.
We consider several estimation and learning problems that networked agents face when making decisions given their uncertainty about an unknown variable. Our methods are designed to efficiently deal with heterogeneity in both size and quality of the observed data, as well as heterogeneity over time (intermittence). The …
Paper uses surprisal to dynamically allocate computation between fast and slow models.
problem Dynamic allocation of computation in neural networks.
method Surprisal-based dynamic model selection.
result Model can match baseline performance with 15% fewer FLOPs.
In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only …
The paper uses neural networks to forecast time series data.
problem Forecasting high-dimensional stationary processes.
method Encoder-decoder neural network structure to model past observations.
result Upper bounds for forecast error under specific assumptions.
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to be inferred, and its hyper-parameters are determined by cross validation. Then we formulate network inference as a matrix completion problem…
Defines complexity measure for neural networks and feature representations, revealing scaling patterns.
problem Understanding the nonlinearity and dimensionality of neural network computations and feature representations.
method Introduces complexity and effective dimension measures, investigates their dynamics during training, and analyzes their scaling properties.
result Power law scaling of complexity and effective dimension during training, revealing hidden structure of datasets.
New method uses neural networks for unbiased physical observable estimation.
problem Estimating physical observables with neural samplers.
method Asymptotically unbiased estimators for observables, including partition function-dependent ones.
result Superiority over existing methods in numerical experiments for the 2d Ising model.
Network models have been popular for modeling and representing complex relationships and dependencies between observed variables. When data comes from a dynamic stochastic process, a single static network model cannot adequately capture transient dependencies, such as, gene regulatory dependencies throughout a developm…
TGNN4I model forecasts irregularly observed graph data using ODEs.
problem Forecasting graph-structured data with irregular time steps and partial observations.
method Introduces a time-continuous latent state in each node using ODEs and GRUs, integrating graph neural network layers.
result Validated usefulness of graph structure and time-continuous dynamics in irregular observation settings.
Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture of uncertainty. We therefore use principled Bayesian methods to capture…
R package abn fits Bayesian models to observational data.
problem Analyzing complex observational datasets with Bayesian networks.
method Bayesian and information theoretic scoring, exact and greedy search algorithms.
result Effective modeling of mixed data types and prior knowledge integration.
Nonparametric neural-network estimation of current-status data
problem Estimation of conditional cumulative distribution function with current-status data
method Neural-network sieve maximum likelihood estimator
result Explicit convergence rate for Hölder smoothness