BALSON optimizes parameters with Bayesian approach and Dirichlet distribution.
problem Data fitting with nonnegative L1-norm constraints.
method Bayesian approach, Gaussian likelihood, Dirichlet distribution, sampling methods.
result BALSON outperforms conventional methods in polynomial fitting.
A method for converting NIW parameters for better estimation.
problem Estimating parameters of multivariate normal distribution.
method Convergent procedure for converting mean parameters to natural parameters in NIW family.
result Maximum likelihood estimation of natural parameters from observed statistics.
Lapse improves parameter servers by dynamically allocating parameters, achieving near-linear scaling.
problem Efficiently managing distributed training with reduced communication overhead.
method Integrate dynamic parameter allocation into parameter servers, proposing Lapse.
result Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers.
A quick gamma approximation speeds up Bayesian inference.
problem Inconvenient gamma shape parameter conjugate priors in Bayesian models.
method Introduced an easy algorithm to approximate gamma shape parameter full conditional by another gamma distribution.
result The approximation is accurate and fast, even for small sample sizes.
Proposes a new measure to evaluate stability of statistical parameters under distributional shifts.
problem Difficulty in transferring knowledge across data sets due to distributional changes.
method Introduces a measure of instability quantifying sensitivity of statistical parameters to Kullback-Leibler divergence and directional shifts.
result The proposed measure can elucidate the type of shifts a parameter is sensitive to and improve estimation accuracy under shifted distributions.
EPD method accurately captures parameter distributions from RCS data.
problem Limitations of traditional methods in estimating parameter distributions from RCS data.
method EPD method generates synthetic trajectories, estimates parameters, and selects parameters based on discrepancy.
result EPD provides accurate distribution of parameters without data loss.
Transformer improves parameter estimation without needing closed-form solutions.
problem Parameter estimation in statistics, especially for complex distributions.
method Transformer-based approach for parameter estimation without closed-form solutions or derivations.
result Transformer-based approach achieves similar or better accuracy than maximum likelihood estimation.
Investigates tempered stable distributions and processes, including density transformations and parameter estimation.
problem Understanding the properties and applications of tempered stable distributions and processes.
method Analysis of limit distributions, parameter estimation, density transformations, and computation of p-variation indices. result Computed p-variation indices for tempered stable processes and discussed exponential stock models driven by these processes. New Riemannian radial distributions help estimate parameters on symmetric spaces.
problem Challenges in manifold data analysis due to lack of parametric distributions.
method Introduced Riemannian radial distributions on symmetric spaces, utilized symmetry, and developed M-estimators.
result MLE achieves root-n convergence rate up to logarithmic terms, demonstrating optimality.
Bayesian method estimates LTLL distribution parameters for time-to-event data.
problem Parameter estimation for time-to-event data with left truncation.
method Bayesian inference using Metropolis-Hastings algorithm.
result Bayesian estimation provides more stable and reliable parameter estimates.
Paper introduces a new IV estimator using ridge regression for better performance.
problem Improving IV estimator performance in linear models with endogeneity.
method Uses ridge regression with an empirically selected regularization parameter.
result The ridge estimator outperforms two-stage least squares under certain conditions.
Machine learning improves accuracy in estimating cosmological parameters from dark matter distribution.
problem Accurately estimating cosmological parameters from the dark matter distribution.
method Application of deep 3D convolutional networks and distribution regression framework to volumetric dark-matter simulations.
result Machine learning techniques can estimate cosmological parameters with comparable or higher accuracy than maximum-likelihood methods.
Study shows how numerical discretization affects reconstructions and parameter distributions in nano metrology.
problem Impact of numerical discretization on parameter reconstructions and model parameter distributions.
method Bayesian target vector optimization, finite element model, Gaussian process, stochastic machine learning surrogate models, Markov chain Monte Carlo sampler.
result Numerical discretization parameters impact the accuracy and distribution of reconstructed model parameters.
Random variables of the generalized Pareto distribution, can be transformed to that of the Pareto distribution. Explicit expressions exist for the maximum likelihood estimators of the parameters of the Pareto distribution. The performance of the estimation of the shape parameter of generalized Pareto distributed using …
The paper fits a seven-parameter GTS distribution to financial data.
problem Nonexistence of GTS probability density function makes MLE inadequate.
method Used fractional Fourier transform to circumvent MLE and provide good parameter estimation.
result The GTS distribution fits financial data significantly better than other models.
Paper introduces a new robust loss function for RL.
problem Heuristic selection of threshold parameters in quantile Huber loss.
method Derived from Wasserstein distance, captures noise in quantile values.
result Enhances robustness against outliers and enables parameter adjustment.
We solve the mean parametrization of von Mises-Fisher distribution.
problem No closed-form normalization function for mean parameters exists.
method Derived a second-order ODE for mean normalizer and provided approximations.
result Rapid evaluation of densities and natural parameters in terms of mean parameters.
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
HyperVAE encodes distributions of distributions using variational inference.
problem Modeling distributions of distributions efficiently and preserving information.
method Variational inference with Gaussian mixture models and matrix-network decoders.
result HyperVAE encodes parameters of a VAE in a low-dimensional Gaussian distribution, preserving more information.
DSCOVR improves distributed optimization for big data with less communication and synchronization.
problem Efficiently optimizing large linear models with convex loss functions over distributed systems.
method Randomized primal-dual block coordinate algorithms with doubly stochastic coordinate optimization and variance reduction.
result DSCOVR algorithms require less overall computation and communication compared to other first-order distributed algorithms.
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimator…
Develops EM algorithm for estimating Yule-Simon distribution parameters.
problem Estimating the λ parameter of Yule-Simon distribution.
method Expectation Maximization (EM) algorithm for frequentist and Bayesian estimation.
result Derives standard errors and proves convergence of the Yule-Simon EM algorithm.
Many banks adopt the Loss Distribution Approach to quantify the operational risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution paramet…
Paper fine-tunes a simulation-driven estimator to reduce out-of-distribution errors.
problem Out-of-distribution errors in simulation-driven parameter estimators.
method Fine-tuning a Two-Stage estimator to improve accuracy for true parameters outside the sampled range.
result The fine-tuning approach reduces out-of-distribution errors and improves accuracy.
Two approaches improve parameter learning in various mixture models.
problem Parameter learning in mixture models.
method Complex-analytic and algebraic-combinatorial methods.
result Improved sample sufficiency for parameter estimation in specific mixture models.
New wealth distribution model based on κ-deformation of Gamma distribution.
problem Modeling wealth distribution in heterogeneous kinetic exchange models.
method Proposed a new four-parameter statistical distribution based on κ-deformation of the Generalized Gamma distribution. result The new distribution accurately represents wealth distribution in heterogeneous kinetic exchange models.
In the context of the Dragulescu-Yakovenko (2000) model, we show that empirical income distribution with truncated datasets, cannot be properly modeled by the one-parameter exponential distribution. However, a truncated version characterized by an exponential distribution with two parameters gives an accurate fit.
Develops methods for constructing parameter priors in DAG models.
problem Constructing parameter priors for model choice among DAG models.
method Introduces assumptions and methods for parameter priors construction and marginal likelihood computation.
result The only parameter prior for complete Gaussian DAG models that satisfies assumptions is the normal-Wishart distribution.
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
problem Uncertainty in HK-distribution parameter estimation for quantitative ultrasound.
method Bayesian Neural Networks (BNNs) for parameter estimation and uncertainty decomposition.
result Decomposes total predictive uncertainty into epistemic and aleatoric components.
New algorithm uses PSO to optimize DNN training parameters in distributed systems.
problem Reducing synchronization frequency in DNN training leads to poor convergence.
method Integrates PSO into distributed training to automatically compute new parameters.
result Proposed algorithm outperforms synchronous methods in distributed DNN training.
HyperGAN generates diverse neural network parameters for improved performance and uncertainty.
problem Overconfidence of neural networks in out-of-distribution data.
method Generative model using a novel mixer to learn a distribution of neural network parameters.
result HyperGAN can generate parameters that perform competitively with fully supervised learning and provide better uncertainty estimates.
The modelling of data on a spherical surface requires the consideration of directional probability distributions. To model asymmetrically distributed data on a three-dimensional sphere, Kent distributions are often used. The moment estimates of the parameters are typically used in modelling tasks involving Kent distrib…
A new method distills material models from noisy data without prior selection.
problem Uncertainty in material model discovery from noisy data.
method Augmenting data with Gaussian process, approximating parameter distribution with normalizing flow, distilling by matching stress-deformation functions, performing sensitivity analysis.
result Sparse and interpretable material models discovered from experimental data.
A fast method for estimating radar amplitude density parameters.
problem Accurate estimation of amplitude density function parameters in radar applications.
method Projecting amplitude data onto horizontal and vertical axes, then using MLE for α-stale distribution parameters. result The average of computed MLEs based on two projections is a fast and accurate estimator for amplitude distribution parameters.
Capital distribution curve is defined as log-log plot of normalized stock capitalizations ranked in descending order. The curve displays remarkable stability over periods of time. Theory of exchangeable distributions on set partitions, developed for purposes of mathematical genetics and recently applied in non-parametr…
Estimates changes in parameters from sparse binomial observations.
problem Sparse observations of binomial parameters over a large population.
method Two-step procedure: MLE for joint distribution, then for change distribution and magnitude.
result Achieves optimal error bounds for estimating change distribution and magnitude.
Paper characterizes sampling distributions of optimal portfolio weights and characteristics.
problem Characterizing sampling distributions of optimal portfolio weights and characteristics.
method Derives exact sampling distribution by stochastic representation.
result High-dimensional asymptotic distribution of optimal portfolio weights is multivariate normal.
Efficiently estimates GEV distribution parameters using neural networks.
problem Computational intensity of maximum likelihood estimation for GEV distribution.
method Neural network-based likelihood-free estimation method.
result Comparable accuracy to maximum likelihood method with significant speedup.
This paper describes how to convert a machine learning problem into a series of map-reduce tasks. We study logistic regression algorithm. In logistic regression algorithm, it is assumed that samples are independent and each sample is assigned a probability. Parameters are obtained by maxmizing the product of all sample…
Paper improves parameter estimation of continuous distributions using preference feedback.
problem Improving parameter estimation of continuous distributions.
method Preference-based M-estimators and deterministic preferences.
result Preference-based estimators achieve an estimation error scaling of O(1/n), significantly faster than sample-only methods.
Paper proposes using generalized lambda distributions for stochastic simulators.
problem Uncertainty quantification with complex stochastic models is computationally challenging.
method Flexible generalized lambda distribution approximates response PDF, parameters are sparse polynomial chaos expansions.
result Local inference of response PDF at each point of experimental design using replicated model evaluations.
Kernel conditional exponential family generalizes conditional distributions.
problem Modeling conditional distributions with flexibility and consistency.
method Introduces a nonparametric family using RKHS and functional parameters, with an algorithm for learning the natural parameter.
result Consistency of the estimator in well-specified cases, and superior performance in experiments.
Federated learning improves with adaptive hyper-parameters and representation matching.
problem Heterogeneous client data leads to divergent local models in federated learning.
method Representation matching and adaptive hyper-parameters.
result Significant performance and robustness improvements in federated learning.
The study reveals a persistent bias in the distribution of holonomy on compact hyperbolic 3-manifolds.
problem The distribution of holonomy on compact hyperbolic 3-manifolds is not uniformly distributed.
method An asymptotic count of closed geodesics by their length and holonomy, and analysis of spectral parameters.
result A normalized, smoothed bias count of holonomy is distributed according to a probability distribution, controlled by the number of zero spectral parameters.
Domain randomization improves RL policy performance on real robots by optimizing distribution parameters.
problem Improving RL policy performance on real robots using domain randomization.
method Optimizing the form and parameters of the distribution of simulated environments.
result Optimized distribution parameters significantly enhance policy performance in the real world.
Neural networks improve gravitational-wave parameter estimation.
problem Estimating parameters of binary black hole systems from gravitational-wave data.
method Autoregressive normalizing flows for likelihood-free inference.
result Performance comparable to current best deep-learning approaches, with fast sampling.
Integrates estimation and optimization for uncertain parameters.
problem Optimizing with uncertain parameters whose distributions can be estimated.
method Integrated Conditional Estimation-Optimization (ICEO) framework.
result Asymptotically consistent and provides finite performance guarantees.
New method improves neural network robustness by identifying functions rather than parameters.
problem Neural networks' lack of robustness to distribution shifts.
method Identify the function represented by quadratic networks, not their parameters.
result Obtain robust generalization bounds for neural networks.