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arXiv research

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.

168,786 papers · 148 categories

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9 results for “DCD”

Robust DCD-Based Recursive Adaptive Algorithmscs.LG

Novel DCD-based algorithms improve RLS performance in noisy channels.

problem Improving recursive least squares performance in impulsive noise.

method Generalized DCD algorithm for RLS, robust strategies, variable forgetting factor.

result Unified update formula and improved tracking of abrupt changes.

DCD RLS impulsive noise recursive algorithms variable forgetting factor

2019-08-18abs ↗pdf ↗

LLM-initialized Differentiable Causal Discoverycs.LG

Proposes LLM-DCD for improved causal discovery from data.

problem Challenges in discovering causal relationships from observational data.

method Uses LLM to initialize DCD optimization, incorporating priors.

result Higher accuracy on benchmark datasets compared to state-of-the-art.

causal discovery differentiable causal discovery LLM adjacency matrix prior knowledge

2024-10-28abs ↗pdf ↗

Training Energy-Based Models with Diffusion Contrastive Divergencescs.LG

This paper proposes a more efficient training method for energy-based models.

problem The computational burden and validity trade-off in Contrastive Divergence training.

method Introducing Diffusion Contrastive Divergence (DCD) to replace Langevin dynamics with diffusion processes.

result The proposed DCDs are more computationally efficient and handle gradient terms better than Contrastive Divergence.

Energy-Based Models Diffusion Contrastive Divergence Contrastive Divergence Markov Chain Monte Carlo Image Denoising

2023-07-04abs ↗pdf ↗

Scalable Dual Coordinate Descent for Kernel Methodscs.DC

New DCD and BDCD methods for K-SVM and K-RR reduce communication costs.

problem Scalability issues in DCD and BDCD methods for kernel problems.

method Developed scalable

s

-step variants of DCD and BDCD methods.

result Achieved strong scaling speedups of up to 9.8x over existing methods.

Dual Coordinate Descent Kernel Support Vector Machines Kernel Ridge Regression Scalability Communication Bottleneck

2024-06-26abs ↗pdf ↗

DC Decomposition of Nonconvex Polynomials with Algebraic Techniquesmath.OC

We consider the problem of decomposing a multivariate polynomial as the difference of two convex polynomials. We introduce algebraic techniques which reduce this task to linear, second order cone, and semidefinite programming. This allows us to optimize over subsets of valid difference of convex decompositions (dcds) a…

2015-10-06abs ↗pdf ↗

A Novel Large-scale Ordinal Regression Modelcs.LG

Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused …

2018-12-19abs ↗pdf ↗

Large-Scale Differentiable Causal Discovery of Factor Graphsstat.ML

New method discovers causal relationships in large-scale data.

problem Discovering causal relationships in large datasets with thousands of variables.

method Factor Directed Acyclic Graphs (f-DAGs) combined with continuous optimization.

result Achieved causal discovery on thousands of variables.

causal discovery factor graphs large-scale data continuous optimization factor directed acyclic graphs

2022-06-15abs ↗pdf ↗

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Datacs.LG

Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.

problem Causal inference in non-stationary, autocorrelated time series data.

method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.

result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.

causal discovery time series analysis non-stationary data autocorrelation seasonality

2026-02-01abs ↗pdf ↗

Differentiable Causal Discovery Under Unmeasured Confoundingcs.LG

New method discovers causal relationships in confounded systems.

problem Discovering causal relationships in systems with unmeasured confounding variables.

method Differentiable algebraic constraints for continuous optimization of ADMGs.

result Effective method for causal discovery in confounded linear systems.

causal discovery confounding unmeasured variables differentiable optimization ADMG

2020-10-14abs ↗pdf ↗