Bayesian method improves EEG source imaging accuracy by estimating skull conductivity.
problem Sensitivity of EEG source imaging to skull conductivity modeling.
method Bayesian uncertainty modeling to estimate skull conductivity and focal sources.
result Clear improvements in source localization accuracy and feasible skull conductivity estimates.
GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.
problem Uncertainty in atrial conduction velocity calculations.
method Gaussian Process Manifold Interpolation (GPMI) on human atrial manifolds.
result GPMI accounts for atrial topology and calculates CV uncertainty.
Develops a novel method to estimate non-Gaussian hydraulic conductivities efficiently.
problem Estimation of non-Gaussian hydraulic conductivity fields in subsurface flow models.
method Integrates adversarial autoencoders with residual dense convolutional networks for parameterization and surrogate modeling.
result Significantly reduces computation time for accurate inversion results.
Method reconstructs neuron models from spike times efficiently.
problem Reconstructing neuron models from spike times in degenerate populations.
method Combining deep learning with DICs to map spike times to DIC densities and generate degenerate CBM populations.
result Fast and scalable reconstruction of degenerate populations from spike recordings.
Graph-based method predicts business conduct risk from incomplete data.
problem Sparse and biased data limits risk assessment.
method Visibility-aware GCNII framework on corporate graph.
result Graph-based approach outperforms non-graph methods in predicting future incidents.
Physics-informed neural networks improve subsurface transport parameter estimation from sparse data.
problem Estimating subsurface transport parameters from sparse measurements.
method Physics-informed neural networks (DNNs) for joint inversion of conductivity, hydraulic head, and concentration fields.
result Physics-informed DNNs yield significantly more accurate parameter estimates than standard DNNs.
Bayesian modeling predicts hydroxide ion conductivity in polymer membranes.
problem Quantitative relationship between hydrophilic domain size and hydroxide ion conductivity in polymer membranes is unknown.
method Bayesian sparse modeling applied to copolymer composition data.
result Composition-derived features are identified as critical for predicting hydroxide ion conductivity.
Proposes a new method to improve selective inference for Lasso models.
problem Over-conditioning due to conditioning on feature signs in selective inference for Lasso.
method Parametric programming approach to avoid conditioning on signs and identify feature selection events.
result Improves power and practicality of selective inference for Lasso models.
Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way. Conductance is a popular objective function used in many algorithms for local community detection. This paper studies a continuous relaxation of conductance. We show that con…
Study examines how twisting graphene nanoribbons affects their thermal conductivity.
problem Understanding how twisting affects thermal conductivity in graphene nanoribbons.
method Calculated geometric parameters of TGNRs, including twist and writhe, and used molecular dynamics simulations.
result Twisted graphene nanoribbons require at least two parameters to accurately describe their thermal conductivity.
New conditions for parabolicity and hyperbolicity of conductive Riemannian manifolds established.
problem Conditions for parabolicity and hyperbolicity of conductive Riemannian manifolds.
method Established new conditions for parabolicity and hyperbolicity of conductive Riemannian manifolds based on the metric and conductivity tensor.
result New conditions for parabolicity and hyperbolicity of conductive Riemannian manifolds.
Method generates intermediate domains to align source and target domains.
problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.
Bayesian optimization improves multi-start global optimization.
problem Global optimization challenges in real-world applications.
method Bayesian optimization framework to determine local search starting points.
result Bayesian optimization enhances the efficiency of multi-start local searches.
The problem of attributing a deep network's prediction to its \emph{input/base} features is well-studied. We introduce the notion of \emph{conductance} to extend the notion of attribution to the understanding the importance of \emph{hidden} units. Informally, the conductance of a hidden unit of a deep network is the \e…
Paper proposes a neural network for estimating brain conductivity without segmentation.
problem Accurate head model generation for personalized TMS with realistic conductivity.
method Convolutional neural network estimating conductivity from MRI data.
result Smooth electric field results similar to conventional methods without segmentation.
This paper uses the relationship between graph conductance and spectral clustering to study (i) the failures of spectral clustering and (ii) the benefits of regularization. The explanation is simple. Sparse and stochastic graphs create a lot of small trees that are connected to the core of the graph by only one edge. G…
In this investigation, symmetry properties of the nonlinear heat conductivity equations of general form ut=[E(x,u)ux]x+H(x,u) are studied. The point symmetry analysis of these equations is considered as well as an equivalence classification which admits an extension by one dimension of the principal Lie alge…
This paper tackles bias in federated learning without compromising data privacy.
problem Bias in federated learning models.
method Three pre-processing and in-processing methods to mitigate bias.
result Proposed methods are effective even with skewed data distributions or a small number of participating parties.
DIN framework directly models hydraulic conductivity and uncertainty.
problem Modeling hydraulic conductivity and uncertainty in groundwater flow.
method DIN utilizes DDPM as a prior learner, incorporating observational data through conditional injection mechanisms.
result DIN generates multiple constraint-satisfying realizations and accurate uncertainty quantification.
Proposes a new optimization method for local graph clustering.
problem Local graph clustering and community detection.
method Convex optimization with p-norm network flow.
result Achieves quadratic approximation of conductance for p=2 and constant factor approximation for p→∞.
Deep learning upscales geologic models efficiently.
problem Upscaling large-scale geologic models for efficient simulation.
method Theory-guided convolutional neural network (TgCNN) trained to approximate hydraulic conductivity relationships.
result Deep learning method achieves equivalent upscaling accuracy to numerical methods but with significantly improved efficiency.
The paper analyzes various normalization methods in deep learning.
problem Lack of mathematical tools to analyze normalization methods.
method Proposed a lemma to define tools, analyzed BN, LN, WN, GN.
result Normalization methods can be unified on a sphere, improving training stability and weight norm.
New group testing method uses Belief Propagation for accurate screening.
problem Efficiently identifying infected samples in large groups with minimal tests.
method Belief Propagation algorithm for inference in group testing schemes.
result Significantly increased accuracy of infection identification with fewer tests.
Study blockchain's impact on primary financial market challenges.
problem Challenges of blockchain in securities issuance and trading.
method Hybrid method combining interviews and surveys.
result Complex due diligence, mismatch, and difficult monitoring are significant challenges.
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hy…
Improves training GANs by escaping limit cycles.
problem Limit cycling behavior in training GANs.
method Predictive Centripetal Acceleration Algorithm (PCAA) combined with Adam.
result PCAA improves convergence rates and effectively trains GANs.
Bayesian model tackles mixed-type data challenges.
problem Challenges in EM algorithm sensitivity, biomarker LOD, and variable importance.
method Bayesian finite mixture model with variable selection, LOD handling, and spike-and-slab prior.
result Improved parameter estimates and variable importance.
Derivative-free optimization has become an important technique used in machine learning for optimizing black-box models. To conduct updates without explicitly computing gradient, most current approaches iteratively sample a random search direction from Gaussian distribution and compute the estimated gradient along that…
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node sequences changes. Second, in disequilibrium networks such as highly bi…
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we …
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that ou…
Proposes a method to infer ranking properties and top-K rankings with uncertainty quantification.
problem General uncertainty quantification in ranking problems.
method Combinatorial inference framework for the Bradley-Terry-Luce model, generalized to multiple testing.
result Minimax optimal method for inferring top-K rankings with FDR control.
Bayesian method improves EEG source localization and estimates skull conductivity.
problem Improving EEG source localization accuracy with unknown skull conductivity.
method Bayesian Approximation Error approach using conditional Gaussian regression, iterative optimization, and physics-informed learning.
result Clear improvements in EEG source localization accuracy and feasible estimates for unknown skull conductivity.
Method estimates heterogeneous causal effects using instrumental variables.
problem Estimating heterogeneity in causal effects using instrumental variables.
method Two-part method: 1) Discover effect modifiers using machine learning, 2) Test for significance using closed testing.
result Evidence of heterogeneity found in specific subgroups.
The challenge of assigning importance to individual neurons in a network is of interest when interpreting deep learning models. In recent work, Dhamdhere et al. proposed Total Conductance, a "natural refinement of Integrated Gradients" for attributing importance to internal neurons. Unfortunately, the authors found tha…
Paper introduces a method to assess the statistical reliability of changepoints using selective inference and dynamic programming.
problem Assessing the statistical reliability of detected changepoints.
method Selective inference framework combined with dynamic programming for exact p-value computation.
result Proposes a method with high statistical power and decent computational efficiency.
In this paper we study nonconvex penalization using Bernstein functions whose first-order derivatives are completely monotone. The Bernstein function can induce a class of nonconvex penalty functions for high-dimensional sparse estimation problems. We derive a thresholding function based on the Bernstein penalty and di…
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pre-trained on the source task using data from the target task. In this paper, we propose an…
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
New method disentangles mixed interventional and observational data in SEMs.
problem Learning causal relationships from mixed interventional and observational data.
method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.
result The method can identify causal graphs up to their interventional Markov Equivalence Class.
FGN improves Chinese NER by integrating glyph information and interactive context.
problem Chinese named entity recognition is challenging due to the complexity of characters and their glyphs.
method FGN uses a novel CGS-CNN structure to capture glyph and interactive information, and a sliding window method to fuse BERT and glyph representations.
result FGN achieves state-of-the-art performance on four NER datasets, improving over previous methods.
nFBST tests neural networks using Bayesian methods.
problem Traditional significance testing struggles with complex nonlinear relationships.
method nFBST uses Bayesian neural networks to test neural networks.
result nFBST can test global, local, and instance-wise significance.
We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multi-view subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications,…
Proposes a method to optimize budget allocation for collecting and analyzing streaming data.
problem Optimizing resource allocation for collecting and analyzing streaming data.
method Formulates optimization problems to allocate budgets for collecting input data and running simulations, characterizes asymptotic behavior of performance estimators, and develops a multi-stage simultaneous budget allocation procedure.
result Demonstrates competitive performance of the proposed procedure through numerical studies.
Proposes a method for two-sided clustering of co-occurrence data.
problem Efficient clustering of co-occurrence data in multi-view settings.
method Information-theoretic multi-view co-clustering (MV-ITCC).
result Demonstrates superior performance on text and image datasets.
Study examines neural network surrogate models for uncertainty in heat conduction problems.
problem Capturing the full distribution of solution fields, especially at the tails.
method Comparison of feed-forward fully connected network and Deep Operator Network architectures using data-driven and physics-informed loss functions.
result Worst-case prediction errors are significantly larger than mean field errors, highlighting the importance of extreme samples.
Develops methods to simulate option prices for a specific stochastic volatility model.
problem No method exists to compute option prices numerically for a non-martingale jump-type model.
method Develops two Monte Carlo simulation methods under change of measure.
result Conducts numerical experiments to validate the developed methods.