Estimates non-parametric logistic model using case-control data and external summary info.
problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
problem Improving statistical inference using summary-level external machine-learning predictions.
method Empirical-likelihood framework incorporating moment constraints from external nonparametric machine-learning predictions.
result Fused estimator achieves strict efficiency gain over primary-only estimator under mild conditions.
A method for logistic regression inference using both internal and external data.
problem Inability to estimate intercept and marginal case proportion in case-control logistic regression.
method Empirical likelihood approach integrating internal and external data.
result Intercept parameter becomes identifiable with external information, and all parameters are estimable consistently.
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
Bayesian neural networks improve with summary information and Dirichlet process.
problem Lack of prior knowledge in BNNs for complex architectures.
method Incorporates external summary information about predicted probabilities using a Dirichlet process.
result Improves model accuracy, uncertainty calibration, and robustness.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of k-sparse signals b…
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
Pointer generator networks have been used successfully for abstractive summarization. Along with the capability to generate novel words, it also allows the model to copy from the input text to handle out-of-vocabulary words. In this paper, we point out two key shortcomings of the summaries generated with this framework…
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is determinis…
Exact learning of tree-structured models with side info and noise.
problem Learning tree-structured graphical models with side information and noise.
method Probabilistic tools from strong large deviations theory.
result Exact asymptotics of structure learning from samples.
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its …
A new method interprets astrophysical spectra using geometric paths to distinguish line profiles.
problem Tackling the indistinguishability of spectral line profiles under scalar summaries.
method Introduces a geometric representation of line profiles using rough path theory, mapping profiles to a common velocity grid and defining descriptors from path properties.
result Compact descriptors separate morphologies with similar scalar summaries, revealing ordered line structures.
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate. In particular, we consider a setup where the signal is low-rank Gaussian and the measurements are taken in the dire…
Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreover, actions in such games typically involve particular sequential action orders, which also makes the…
New mechanism for pure differential privacy on functional summaries using Laplace-like process.
problem Challenges in achieving differential privacy for complex, structured functional summaries.
method Independent Component Laplace Process (ICLP) mechanism for infinite-dimensional Hilbert space.
result Effective enhancement of utility of private summaries through oversmoothing.
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary statistic, but outside of special cases where the optimal summary statis…
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific a…
LIDS assesses LLM summaries with interpretable key words.
problem Challenges in evaluating the quality of LLM summaries.
method BERT-SVD-based direction metric and SOFARI for key word extraction.
result LIDS provides interpretable key words for layered themes.
Unified approach for selecting summary statistics in ABC.
problem Efficient inference from large datasets in likelihood-free methods.
method Characterizing and unifying three classes of summary statistics, minimizing expected posterior entropy.
result EPE-minimizing summaries lead to competitive posterior inference.
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consi…
Enhances weak lensing inference with neural summaries.
problem Extracting additional information from weak lensing convergence maps.
method Hybrid approach combining physics-based and neural summaries.
result Neural summaries extract up to 8 times more information than angular power spectra.
Approximate Bayesian computation is an established and popular method for likelihood-free inference with applications in many disciplines. The effectiveness of the method depends critically on the availability of well performing summary statistics. Summary statistic selection relies heavily on domain knowledge and care…
Improves inference from sparse data with hybrid summary statistics.
problem Robust simulation-based inference from limited data.
method Augment traditional summary statistics with neural network outputs to maximize mutual information.
result Improves information extraction and makes inference robust in low-data settings.
Automatically learns summary features from time series data for likelihood-free inference.
problem Necessity of hand-tailored summary features for time series data in likelihood-free inference.
method Data-driven approach to automatically learn summary features.
result Learning summary features from data can outperform hand-crafted values in likelihood-free inference.
Text clustering method replaces centroids with summaries for interpretability and scalability.
problem Efficiently clustering text data while maintaining interpretability and scalability.
method k-NLPmeans and k-LLMmeans, which periodically replace numeric centroids with textual summaries.
result Consistently outperforms classical baselines and recent LLM-based clustering methods.
Plug-in robust NPE method adapts summaries independently of pretrained NPE.
problem Misspecification of neural posterior estimators under test data distribution.
method Minimum-distance summaries using maximum mean discrepancy (MMD).
result Substantial robustness gains with minimal additional overhead.
Few summaries enable automatic summarization of product reviews.
problem Lack of large labeled datasets for training supervised models in opinion summarization.
method Conditional Transformer model trained to generate summaries given other reviews, fine-tuned to predict summary properties.
result Few summaries (5-10) are sufficient to generate fluent, informative, and sentiment-preserving summaries.
Improved likelihood-free inference by localizing and refining low-dimensional approximations.
problem Poor performance of common likelihood-free methods in high-dimensional models.
method Localisation followed by refinement of low-dimensional summaries.
result Improved accuracy in marginal posteriors through localized and refined approximations.
New summary measures reveal geometric structure in weighted measures on manifolds.
problem Lack of geometric information in standard weight-only summaries.
method Heat-kernel entropy profiles, tracking nonuniformity across scales.
result Geometric effective sample size discounts nearby or duplicate particles.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
problem Lack of infrastructure and pooled vehicle info in existing AV datasets.
method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.
result VTrackIt reduces high-risk edge cases in AV trajectory predictions.
A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.
problem Incorrectly assuming normally distributed summary statistics in likelihood-free Bayesian inference.
method Wasserstein Gaussianization transformation combined with robust BSL and efficient Variational Bayes.
result Highly efficient and reliable approximate Bayesian inference for likelihood-free problems.
Study achieves fairness without demographic info, improving regression tasks.
problem Achieve fairness in models without prior demographic info.
method VFair method to minimize training loss variance, dynamic update approach.
result Regression tasks can achieve significant fairness improvement without prior demographics.
Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the mod…
Paper uses learned summary statistics for Bayesian inference with difficult likelihood functions.
problem Difficult to obtain exact likelihood function for observation data and simulation model.
method Simulation-based inference with learned summary statistics, using Cressie-Read discrepancy criterion.
result Effective inference performed over selected sample sets of observation data.
Flexible multi-task learning framework using summary statistics.
problem Data-sharing constraints in healthcare settings.
method Proposes a flexible multi-task learning framework utilizing summary statistics and adaptive parameter selection.
result Systematic non-asymptotic analysis and simulations demonstrate the method's performance.
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.
method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.
result Accurately infers external potentials and density profiles with uncertainty quantification.
Develops methods to learn centre groupings from summary statistics in multi-centre studies.
problem Violation of homogeneity of parameters across centres in multi-centre studies.
method Clusters-of-Centres (CoC) algorithm that merges centres based on multivariate Cochran-type tests.
result Golden-partition recovery as the number of rounds grows with sample size.
TopoFisher learns topological summaries by maximizing Fisher information, improving parameter efficiency and inference quality.
problem Simulation-based inference misses key information in low-order statistics, especially for non-Gaussian fields.
method TopoFisher uses a differentiable persistent-homology pipeline that learns topological summaries by maximizing local Gaussian Fisher information.
result TopoFisher recovers much of the available information and outperforms fixed topological vectorizations in weak gravitational lensing.
The paper proposes using Autoencoders to learn summary statistics for Bayesian inference.
problem Approximating posterior distributions for models with intractable likelihood functions.
method Using Autoencoders to extract summary statistics that retain parameter information and cancel noise.
result The approach effectively learns summary statistics that improve posterior approximation.
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
problem Burden of creating digital SOAP notes by physicians.
method Cluster2Sent algorithm for summarizing patient-physician conversations.
result Cluster2Sent algorithm outperforms existing methods by 8 ROUGE-1 points.
Most classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification techniques that are able to detect the occurrence of a change in the underlying data distribution. Ignoring possible changes in the underly…
MFAI uses gradient boosted trees to leverage auxiliary info for scalable Bayesian matrix factorization.
problem Matrix factorization struggles with poor data quality, especially high sparsity and low SNR.
method Integrates gradient boosted trees into probabilistic matrix factorization framework.
result MFAI effectively leverages auxiliary information, improving model performance.
New clustering method reduces data redundancy for better summaries.
problem Redundancies in data summaries limit their effectiveness in large datasets.
method Khatri-Rao clustering extends centroid-based clustering to produce more succinct summaries.
result Khatri-Rao k-Means and deep clustering frameworks produce more succinct summaries with similar accuracy.
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood appro…
For long time the measurement of innovation has been in the forefront of policy makers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. However, there is no c…
Paper tackles circularity issues in machine learning predictions.
problem Circularity problems in machine learning predictions.
method Not specified in the abstract.
result Not specified in the abstract.
Paper introduces a novel framework for set input tasks in meta-learning.
problem Meta-learning problems with set inputs often require efficient summary networks.
method Prototype-oriented optimal transport (POT) framework to improve summary networks.
result Significantly improves summary statistics from sets in meta-learning.
Develops methods for GWAS of high dimensional phenotypes using summary statistics.
problem Lack of methods to model pleiotropy in multi-phenotype GWAS.
method Bayesian inference model using summary statistics, fast computation, and biologically informed priors.
result Demonstrates utility in metabolite GWAS with interpretable pathway-level inference.