Directed graphical models provide a useful framework for modeling causal or directional relationships for multivariate data. Prior work has largely focused on identifiability and search algorithms for directed acyclic graphical (DAG) models. In many applications, feedback naturally arises and directed graphical models …
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48 results for “Model Assumptions”
Paper relaxes independence assumption for non-centered data.
problem Failing to account for dependencies in data leads to model failures.
method Proposes 'Kronecker-sum-structured mean' assumption to relax zero-mean requirement.
result Models with nonconvex but unimodal log-likelihoods can be solved efficiently.
Neurosymbolic predictors fail to model uncertainty under independence assumption.
problem Neurosymbolic predictors' reliance on independence assumption limits their ability to model uncertainty.
method Formal analysis of NeSy predictors under independence assumption.
result Assuming independence among symbolic concepts prevents NeSy predictors from representing uncertainty.
New algorithm estimates causal effects for non-Gaussian data.
problem Estimating causal effects in non-Gaussian distributions.
method Generalized k-Triangle Faithfulness Assumption and Edge Estimation Algorithm.
result Uniformly consistent estimates of causal effects.
Bayesian model selection improves causal discovery in complex datasets.
problem Identifying causal direction in Markov equivalence classes with realistic assumptions.
method Incorporating causal assumptions within Bayesian framework for model selection.
result Bayesian model selection outperforms previous methods on various datasets.
Bagging stabilizes models without distributional assumptions.
problem Stability of machine learning models without distributional assumptions.
method Derives a finite-sample guarantee on bagging stability for any model.
result Guarantee applies to many bagging variants and is optimal.
Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are me…
This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.
problem Evaluation of knowledge graph completion models often assumes a closed-world assumption, which can lead to misleading results.
method The paper studies KGC evaluation under the open-world assumption, analyzing the behavior of metrics like MRR and Hits@K.
result Metrics like MRR and Hits@K can show significant degradation under the open-world assumption, leading to incorrect model comparisons.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Improves online learning algorithms for functional models with capacity assumptions.
problem Convergence rates of online stochastic gradient descent algorithms for functional linear models.
method Characterizations of slope function regularity, kernel space capacity, and sampling process covariance operator.
result Capacity assumptions can alleviate saturation of convergence rates as function regularity increases.
Emputation learns imputation models guided by missingness assumptions.
problem Learning imputation models for missing data given observed data.
method Guided by specific missingness assumptions, Emputation trains a deep generative model to learn the extrapolation distribution of missing variables.
result The population minimizer of the emputation risk recovers the target extrapolation distribution under various identification assumptions.
A new learning method uses data to learn from large model sets.
problem Learning with large sets of candidate models where uniform convergence is hard.
method Data-dependent learning that incorporates empirical data less reliant on prior assumptions.
result Demonstrates improved generalization in various learning assumptions.
We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model parameters using the observed noisy labels. A traditional assumption underlying previously introduced generative models is that each object has…
Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mism…
Bayesian model selection improves multivariate causal discovery without restrictive assumptions.
problem Real-world causal discovery requires flexible assumptions to avoid restrictive model assumptions.
method Continuous relaxation of discrete model selection problem, using Causal Gaussian Process Conditional Density Estimator (CGP-CDE).
result Bayesian approach outperforms traditional methods in multivariate causal discovery.
The paper defines conditions for learning causal graphs from data with unobserved variables.
problem Learning causal graphs from data with unobserved variables.
method Formalizes constraint-based structure learning algorithms under conditions and assumptions.
result Natural family of algorithms output Markov equivalent graphs to the causal graph under faithfulness assumption.
The paper introduces tests for missing data models based on graph assumptions.
problem Verification of assumptions in missing data models is insufficiently addressed.
method The paper explores three classes of missing data models and designs goodness-of-fit tests.
result The paper provides new insights and tests for missing data graphical models.
Study examines robustness of NPI effectiveness models against COVID-19.
problem How do NPI effectiveness estimates vary with model assumptions and data?
method Investigated 2 NPI effectiveness models and 6 variants, evaluated robustness to unseen countries, parameters, and data.
result NPI effectiveness estimates are remarkably robust to different variables.
We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns.…
Bounds on factual and counterfactual distributions under measurement error in discrete models.
problem Measurement errors in discrete data and their impact on inference.
method Expressing modeling assumptions as linear constraints and using linear programming to derive bounds.
result Sharp bounds on factual and counterfactual distributions for various models, including instrumental variable scenarios.
Investor optimizes investment strategy under model uncertainty and random utility.
problem Optimizing investment under model ambiguity and random utility.
method Proves existence of optimal strategy using primal methods, with assumptions on market and utility function.
result Existence of optimal investment strategy proven.
Proposes new methods for inference in GLMs without assuming model correctness.
problem Inference for GLMs assumes model correctness, leading to uncertainty and bias.
method Develops nonparametric estimands and uses influence curves with flexible procedures.
result Inference for GLM parameters is improved without model correctness assumptions.
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been show…
Item response theory (IRT) models for categorical response data are widely used in the analysis of educational data, computerized adaptive testing, and psychological surveys. However, most IRT models rely on both the assumption that categories are strictly ordered and the assumption that this ordering is known a priori…
As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one often needs approximation inference if distribution assumption is beyond Gaussian distribution. In this paper, we propose a Bayesian linear reg…
Improved Gaussian Process model for predicting trajectories without independence assumption errors.
problem Incorrect independence assumption in previous work on Gaussian Process uncertainty propagation.
method Proposed a novel piecewise linear approximation to correct the independence assumption in continuous models.
result Corrected the independence assumption in Gaussian Process models for predicting trajectories.
New method speeds up diffusion models without requiring complex assumptions.
problem Slow sampling in diffusion models due to high computational cost.
method Training-free acceleration scheme under minimal assumptions.
result Provable acceleration within iterations.
New model for pairwise comparisons without stochastic transitivity.
problem Suboptimal performance of models assuming stochastic transitivity in real-world scenarios.
method Proposes a general family of statistical models using a skew-symmetric matrix.
result Achieves minimax-rate optimality and adapts to data sparsity.
New active learning framework for multiclass classification beyond realizability assumption.
problem Active learning in non-realizable settings with convex model classes.
method Surrogate risk minimization, epoch-based fitting, aggregation of models.
result Achieves label and sample complexity comparable to prior work in non-realizable settings.
Study highlights how model choice affects uncertainty estimation in neural network regression.
problem Uncertainty estimation under model misspecification in neural network regression.
method Analyzed the impact of model choice on uncertainty estimation in neural network regression, focusing on aleatoric and epistemic uncertainties.
result Model misspecification leads to unreliable uncertainty estimates, highlighting the importance of choosing appropriate models.
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Modelcs.LG
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental …
In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …
Unified HS and related methods with explicit modeling assumptions.
problem Lack of clear assumptions in HS methods for Value-at-Risk.
method Explicitly defined parametric model for asset returns and extraction of innovation process.
result HS and related methods require more assumptions than commonly acknowledged.
Improved model for non-smooth signals with complex spectra.
problem Current models struggle with non-smooth signals and complex spectral structures.
method CGPCM and RGPCM models with causality and Bayesian nonparametric interpretations, improved variational inference.
result Proposed models show better performance on synthetic and real-world data.
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarioscs.LG
CausalCompass evaluates TSCD robustness under violations of modeling assumptions.
problem Widespread adoption of TSCD is hindered by untestable causal assumptions and lack of robustness evaluation.
method CausalCompass is a flexible benchmark framework for assessing TSCD robustness under violations of modeling assumptions.
result No single method consistently attains optimal performance across all settings, but deep learning-based methods perform well.
When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…
This paper tackles non-convex phase retrieval with structured assumptions.
problem Phase retrieval with limited measurements and structure assumptions.
method Non-convex approaches with sample complexity guarantees.
result Sample-efficient recovery with structured signals/images.
New Gamma-Poisson model improves topic selection for short text.
problem Topic modelling for short text using Poisson distribution.
method Gamma-Poisson mixture model with collapsed Gibbs sampler.
result Gamma-Poisson model selects more accurate number of topics.
GALA framework learns invariant graph representations via environment augmentation with minimal assumptions.
problem Learning invariant graph representations from different environments without additional assumptions.
method Developed GALA framework with minimal assumptions of variation sufficiency and consistency. Uses an assistant model to differentiate graph environment changes.
result Extracting maximally invariant subgraphs to proxy predictions identifies underlying invariant subgraphs for successful out-of-distribution generalization.
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.
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may h…
Improved peak detection in ChIP-seq data reduces over-dispersion.
problem Over-dispersion in ChIP-seq data reduces peak detection accuracy.
method Supervised segmentation models with alternative noise assumptions.
result Improved peak detection accuracy compared to natural assumptions.
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
We use copulas to improve SLAM in uncertain environments.
problem Uncertain data association and nonlinear transition models in SLAM.
method Integrate copulas into a Sequential Monte Carlo estimator for SLAM.
result Our method effectively handles SLAM in uncertain environments.
The ability to adequately model risks is crucial for insurance companies. The method of "Copula-based hierarchical risk aggregation" by Arbenz et al. offers a flexible way in doing so and has attracted much attention recently. We briefly introduce the aggregation tree model as well as the sampling algorithm proposed by…
In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirica…
Study improves denoising score matching under relaxed manifold assumptions.
problem Improving denoising score matching under relaxed manifold assumptions.
method Model density with nonparametric Gaussian mixtures, relax manifold assumption, derive non-asymptotic bounds.
result Non-asymptotic bounds on approximation and generalization errors, rates of convergence determined by intrinsic dimension.