Conventional multiclass conditional probability estimation methods, such as Fisher's discriminate analysis and logistic regression, often require restrictive distributional model assumption. In this paper, a model-free estimation method is proposed to estimate multiclass conditional probability through a series of cond…
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48 results for “conditional probability estimation”
V-matrix method fails to consistently estimate conditional probabilities.
problem Inconsistent solutions in V-matrix method for conditional probability estimation.
method Construct constrained quadratic programming problems with inconsistent inequality constraints.
result V-matrix method may not always have a consistent solution for conditional probability estimation.
We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distribution of returns.
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tablesstat.ML
New method improves classification performance in Bayesian networks.
problem Estimating conditional probability tables in Bayesian networks.
method Hierarchical Multinomial-Dirichlet model for joint estimation of conditional distributions.
result Significantly improved classification performance compared to traditional methods.
This work assesses DNNs for estimating conditional probabilities.
problem Lack of uncertainty characterization in DNNs for probabilistic applications.
method Investigates DNNs' ability to estimate conditional probabilities using synthetic and real-world datasets.
result DNNs' precision in estimating conditional probabilities is influenced by probability density and inter-categorical sparsity.
Paper improves VaR risk allocation by avoiding zero probability events.
problem Computing VaR contributions for zero probability events.
method Reformulates Euler contributions to a ratio of conditional expectations with strictly positive probability events.
result Proposed estimator outperforms standard Monte Carlo methods in bias and variance.
New method estimates and samples high-dimensional probability distributions avoiding optimization and approximation curse.
problem Estimating high-dimensional probability distributions from data samples.
method Hierarchic probability flow from coarse to fine scales, defined by conditional probabilities across scales.
result Sampling hierarchic models avoids critical slowing down at phase transitions and generates turbulence and dark matter images.
This paper improves learning uncertain Bayesian networks from incomplete data.
problem Learning conditional probabilities in Bayesian networks with limited data.
method Develops methods to estimate and quantify uncertainty in conditional probabilities with incomplete data.
result Improves state-of-the-art approaches for handling uncertain Bayesian networks with incomplete data.
Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.
problem Standard Bayesian filtering assumes exact conditional probabilities, limiting its applicability.
method Introducing inequality conditions transforms conditional probabilities into convolutional forms, expanding the filtering framework.
result Convolutional Bayesian filtering encompasses standard Bayesian filtering and allows for more nuanced model consideration.
A coin-flipping game paradox illustrates how conditional probability estimation can distort risk assessment.
problem Distortion of risk assessment due to incorrect conditional probability estimation.
method A coin-flipping game to illustrate the paradox of conditional probability estimation.
result Incorrect conditional probability estimation can lead to excessive risk bearing.
A new model for estimating multivariate densities efficiently.
problem Estimating complex multivariate densities efficiently.
method CDO model based on kernel mean embeddings and RKHS.
result Competitive performance with neural models and Gaussian processes.
New technique for multiple-source adaptation without density estimation.
problem Multiple-source adaptation problem.
method Discriminative technique that uses conditional probabilities from unlabeled data.
result Our technique outperforms previous generative solutions and other domain adaptation baselines.
In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimalitystat.ML
Paper analyzes risk bounds for in-context learning in multiclass classification.
problem Risk bounds for in-context learning in multiclass classification.
method Formalizes tasks as sequences of labeled examples and queries, estimates conditional class probabilities, establishes oracle inequality for KL divergence.
result ICL achieves minimax optimal rate for conditional probability estimation.
The Law of Total Oddsmath.PR
The law of total probability may be deployed in binary classification exercises to estimate the unconditional class probabilities if the class proportions in the training set are not representative of the population class proportions. We argue that this is not a conceptually sound approach and suggest an alternative ba…
New method for generating images with conditional probability models.
problem Generating images with specific conditions.
method Score-based diffusion models with theoretical analysis and new estimator.
result New estimator for conditional score performs similarly to state-of-the-art.
The paper proves convex risk minimization selects a unique conditional probability model.
problem General conditional probability estimation in various settings.
method Convex risk minimization and empirical risk minimization.
result The unique conditional probability model is selected by convex risk minimization.
New framework estimates treatment effects based on preferences.
problem Estimating treatment effects with flexible outcomes.
method Preference-based Conditional Treatment Effect (CPTE) framework.
result CPTE provides interpretable targets and new identifiability conditions.
Study on estimating class probabilities using empirical risk minimization.
problem Estimating class probabilities within binary classification.
method Empirical risk minimization (ERM) for class probability estimation.
result The estimator converges to true class probabilities under certain conditions.
Paper develops NW kernel estimator for LSPs with Wasserstein bounds.
problem Capturing nuanced dynamics in time series data with evolving statistical characteristics.
method Nadaraya-Watson kernel smoothing for conditional probability estimation, using Wasserstein and sliced Wasserstein distances.
result Established convergence rates and bounds for NW-based conditional probability estimator in LSPs.
A Longitudinal Attribute-Conditioned Neural Network (LANTERN) framework for modeling health-state transition probabilities in irregular longitudinal data.
problem Estimating long-term care transition probabilities in irregular longitudinal health data.
method A neural network that learns from individual health history, incorporates time elapsed, and conditions on demographic and socioeconomic attributes.
result Improves severe disability discrimination and maintains strong calibration.
New method estimates selection event for black-box models.
problem Infeasibility of conditional selective inference due to unavailable selection event.
method Bootstrapping to estimate selection event and conditional distribution.
result Feasibility of conditional selective inference for models without exact selection event.
New framework models uncertainty with uncertainty variables.
problem Modeling uncertainty in state estimation and inference.
method Developed uncertainty variables, sets, and graphical models.
result Preserves independence properties and builds useful concepts.
CSI method learns conditional distributions by estimating flow equations.
problem Learning conditional distributions in generative models.
method Estimates probability flow equations to transport reference to target distribution.
result Derives explicit expressions for conditional drift and score functions.
Paper presents a method to estimate mixed-variable distributions.
problem Estimating joint, conditional, and marginal distributions from mixed data.
method Graph representation of data, eigenvector equations for distribution estimation.
result Method successfully estimates distributions for various machine learning tasks.
New linear algorithms improve wSVMs for multiclass probability estimation.
problem Estimating conditional probabilities for multiclass problems.
method Proposed baseline learning and OVA learning schemes to improve wSVMs.
result Linear algorithms achieve optimal computational efficiency and good estimation accuracy.
Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performancestat.ML
Proposes a method to create shorter, more accurate prediction intervals.
problem Challenges in achieving both conditional validity and interval efficiency in complex settings.
method Uses a conformal-style calibration method for neural network responses, adjusting to empirical PIT distribution.
result Demonstrates better conditional calibration and shorter intervals than existing methods.
The paper develops a method to estimate conditional survival probabilities under noisy firm value data.
problem Estimating conditional default probabilities in models with partial information about firm value.
method Recursive quantization method to approximate conditional survival probabilities.
result The recursive quantization method provides a way to approximate conditional survival probabilities under noisy data.
New methods for estimating causal effects with limited overlap, using Stable Probability Weighting.
problem Estimating causal effects with limited overlap in multivalued treatments.
method Stable Probability Weighting (SPW) and Finite-Sample Stable Probability Weighting (FPW) methods.
result SPW and FPW provide practical solutions for estimating and inferring causal effects with limited overlap.
Paper proposes a new ML approach to estimate g-vulnerability without estimating conditional probabilities.
problem Estimating information leakage in black-box systems with large output domains.
method Developed a novel ML-based approach to estimate g-vulnerability without estimating conditional probabilities.
result The proposed method outperforms frequentist approach when dealing with large output domains.
The paper develops methods to predict the probability of achieving a user goal in a task, ensuring the system alerts when the probability falls below a threshold.
problem Ensuring an autonomous system achieves the user's goal with calibrated probability estimates.
method Invertible conformal prediction using Probability-space Conformalized Quantile Regression (PCQR) to produce well-calibrated conditional prediction intervals.
result The method produces well-calibrated probabilities that the cumulative reward will fall within a user-specified target interval, with finite-sample guarantees.
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and algorithm.
result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
The paper analyzes Tikhonov regularization in Hilbert scales for statistical inverse problems.
problem Statistical inverse problems in Hilbert scales with general noise.
method Tikhonov regularization scheme with conditional stability estimates and high probability error bounds.
result Explicit rates of convergence for oversmoothing and regular cases over defined regularity classes.
Paper estimates AI hallucinations in conditional generation tasks.
problem Estimating the frequency of AI-generated incorrect responses.
method Developed a method to estimate hallucination probability from generated responses and log probabilities.
result Method accurately estimates hallucination rate in natural language and synthetic tasks.
New tractable density models from squaring neural networks.
problem Flexible models for probability distributions in machine learning.
method Squared Neural Family (SNEFY) models formed by squaring neural network outputs and normalizing.
result SNEFYs are fully tractable with closed form normalizing constants in many cases.
Develops a new framework for estimating joint probability distributions.
problem Estimating joint probability distributions from large sample sizes.
method Tensor product reproducing kernel Hilbert spaces (RKHS) with normalized and positive model.
result Fast computation and applicability to prediction and classification problems.
New methods estimate probabilities from pairwise comparisons, adapting to difficulty.
problem Estimating probabilities from pairwise comparisons with varying difficulty.
method Adaptive estimators using an adaptivity index based on indifference sets.
result CRL estimator has adaptivity index upper bounded by up to logarithmic factors.
A Unified Framework for Pattern Recovery in Penalized and Thresholded Estimation and its Geometrymath.ST
Unified framework for pattern recovery in penalized and thresholded estimation.
problem Pattern recovery in penalized and thresholded estimation methods.
method Defining a novel pattern notion based on subdifferentials, introducing accessibility and noiseless recovery conditions.
result Unified and extended conditions for pattern recovery in a broad class of penalized estimators.
SJS model predicts label shifts in multinomial datasets.
problem Predicting label shifts in multinomial datasets.
method Sparse joint shift model for dataset shift.
result Valid predictions and class prior probabilities estimates.
Proposes novel wSVMs for sparse learning and accurate probability estimation.
problem Sparse features with redundant noise limit the performance of existing wSVMs.
method Develops -norm and elastic net regularized wSVMs for automatic variable selection and probability estimation.
result Elastic net regularized wSVMs achieve superior performance in variable selection and probability estimation.
We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth of the tree. Mot…
Proposes a neural network method to combine nonprobability and probability survey samples.
problem Combining nonprobability and probability survey samples for accurate population mean estimation.
method Uses a deep neural network to estimate sampling scores from nonprobability samples and combines them with probability sample information.
result Proposed estimators improve robustness to parametric propensity-score misspecification, especially for nonlinear selection mechanisms.
Improved matrix completion for non-uniformly sampled data.
problem Estimating unobserved entries in a matrix with varying sampling probabilities.
method Developed entry-specific bounds for low-rank matrix completion under structured non-uniform sampling.
result Error bounds for each entry match minimax lower bounds under certain conditions.
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioningstat.ML
A deep learning method for estimating discrete conditional distributions efficiently.
problem Estimating discrete conditional probability distributions efficiently.
method Smoothed dyadic partitioning and graph-based smoothing.
result Significantly reduces error in conditional distribution estimation.
New nonparametric estimators improve causal effect estimation.
problem Estimation of causal effects with selection bias.
method Undersmoothing of the highly adaptive lasso for estimating the weighting mechanism.
result Asymptotic efficiency and convergence to nonparametric efficiency bound.
Framework for consistent binary classification with complex metrics.
problem Consistent binary classification for non-decomposable metrics like F-measure and Jaccard.
method General framework for batch and online learning, applies to linear and non-linear models. Uses thresholding and normalized gradient ascent for threshold estimation.
result Simple normalized gradient ascent updates for threshold estimation, with finite-sample regret analysis.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
problem Estimating non-linear functionals of probability distributions.
method Proposes a quantum-inside-quantum Monte Carlo algorithm for a broad class of non-linear estimation problems.
result Achieves a quadratic speedup for non-linear estimation problems, including nested conditional expectations and stochastic optimization.
New refit strategy improves probability estimation for multicategory angle-based classifiers.
problem Improving probability estimation for multicategory angle-based classifiers in high dimensional applications.
method Proposes a new refit strategy for multicategory angle-based classifiers, adding small computation cost.
result Significant improvement in probability estimation with minimal additional computation.
Kernel Density Machinesstat.ML
Kernel Density Machines learn probability densities without structural assumptions.
problem Learning probability densities under minimal assumptions.
method Kernel-based framework, agnostic to structural requirements.
result Consistency and functional central limit theorem for sample estimator.