A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New framework improves EM algorithm convergence under log-Sobolev inequality.
DeepPPMNet forecasts EMS demand and performs causal analyses for policy-making.
Paper introduces deterministic EM approximations for non-convex likelihood functions.
Paper uses machine learning in EM framework for better nowcasting.
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [Kivinen & Warmuth, 1994]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is c…
We consider the geometric formulation of the Hamiltonian formalism for field theory in terms of {\em Hamiltonian connections} and {\em multisymplectic forms}. In this framework the covariant Hamilton equations for Mechanics and field theory are defined in terms of multisymplectic --forms, where is the dimens…
Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires Gaussian assumption, however, natural images are highly non-Gaussian so that GMM cannot be applied to perform clustering task on pixel space…
In this paper we develop an Expectation Maximization(EM) algorithm to estimate the parameter of a Yule-Simon distribution. The Yule-Simon distribution exhibits the "rich get richer" effect whereby an 80-20 type of rule tends to dominate. These distributions are ubiquitous in industrial settings. The EM algorithm presen…
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise from experiments in which the samples are shuffled or permuted during the protocol. In this work, we propose a framework that treats the un…
Develops a high-dimensional differentially-private EM algorithm with near-optimal statistical guarantees.
A new diffusion model improves cryo-EM structure sampling.
Proves EM algorithm guarantees for hierarchical imitation learning.
Paper shows DMS as an EM algorithm with improved convergence.
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…
Tensor-EM method learns MoLDS from complex, noisy data.
We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of infinite data), followed by results that apply to updates based on a …
Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely comput…
A new EM framework for goal-conditioned RL improves performance on sparse reward tasks.
Paper analyzes EM algorithm's trajectory in 2MLR, revealing cycloid behavior.
Proposes new methods for Markov chain choice models with panel data.
DO-EM framework for quantum models improves generative tasks.
Gradient EM converges globally for over-parameterized Gaussian mixtures.
ES improves training efficiency by dynamically selecting data samples.
In the classical Lagrangian approach to conservation laws of gauge-natural field theories a suitable (vector) density is known to generate the so--called {\em conserved Noether currents}. It turns out that along any section of the relevant gauge--natural bundle this density is the divergence of a skew--symmetric (tenso…
Cryo-EM reconstruction is reformulated as a stochastic inverse problem to handle structural heterogeneity.
Gradient EM converges exponentially to optimal solution in agnostic mixtures.
Marginal MAP problems are notoriously difficult tasks for graphical models. We derive a general variational framework for solving marginal MAP problems, in which we apply analogues of the Bethe, tree-reweighted, and mean field approximations. We then derive a "mixed" message passing algorithm and a convergent alternati…
Latent variable models are a fundamental modeling tool in machine learning applications, but they present significant computational and analytical challenges. The popular EM algorithm and its variants, is a much used algorithmic tool; yet our rigorous understanding of its performance is highly incomplete. Recently, wor…
Optimizing distributed learning systems is an art of balancing between computation and communication. There have been two lines of research that try to deal with slower networks: {\em communication compression} for low bandwidth networks, and {\em decentralization} for high latency networks. In this paper, We explore a…
To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on lar…
Gradient-EM Bayesian meta-learning accelerates adaptation with reduced computation and improved robustness.
We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thorough…
New accelerators for EM improve convergence speed in complex mixture models.
This paper uses dynamical systems to analyze and ensure convergence of the Bayesian EM algorithm.
FIEM accelerates EM for large datasets with nonasymptotic convergence bounds.
New method optimizes clustering with better log-likelihood landscape.
Integrates VAEs into EM for deep clustering and generation.
New saliency evaluations focus on completeness and soundness, improving explanations.
Regression mixture models are widely studied in statistics, machine learning and data analysis. Fitting regression mixtures is challenging and is usually performed by maximum likelihood by using the expectation-maximization (EM) algorithm. However, it is well-known that the initialization is crucial for EM. If the init…
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To alleviate this problem, Neal and Hinton have proposed an incremental ver…
This paper proposes an EM approach to reduce inference latency in NAR sequence generation.
New DP EM algorithm with statistical guarantees for mixture models.
New EM algorithm for mixtures of elliptical distributions handles missing data and outliers.
SOLVAR efficiently analyzes cryo-EM data's structural variability.
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility. To generalize it, we present a fully Bayesian nonparametric model, namely Gaussian process modulated Hawkes process and propose an EM-variation…
Study reveals decurve flows in graph propagation models.
A new algorithm for cryo-EM data collection that balances reward and latency.
DM framework improves robustness and efficiency in latent-mixture models.