Proposes a robust estimator for RD designs.
problem Estimating treatment effects in RD designs.
method Doubly robust estimator combining two estimators.
result Enhances robustness of treatment effect estimators.
On-device research index
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “RDS”
Extended RDS filtering for positions and orientations, improving crossing structure enhancement and inpainting.
problem Enhancing and inpainting images with crossing structures.
method Extended RDS filtering to space, using gauge frames to mitigate issues.
result RDS filtering outperforms existing techniques in denoising and inpainting images with crossing structures.
Extends RDS filtering to position-orientation space for better image processing.
problem Enhancing and inpainting images with crossing structures.
method Created a version of RDS filtering using gauge frames, studying generalised diffusion.
result RDS filtering on position-orientation space improves denoising and inpainting of crossing structures.
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender Systemcs.LG
A new method trains compact ranking models for recommender systems.
problem Training efficient ranking models for recommender systems.
method Ranking Distillation (RD) technique to train a smaller model with similar performance to a larger teacher model.
result The student model achieves a similar ranking performance to the teacher model with significantly less model size.
This work evaluates deep generative models using RD curves, providing a more comprehensive quality assessment.
problem Quantitative evaluation of deep generative models is challenging, especially for implicit models.
method Proposes using rate distortion (RD) curves to evaluate and compare deep generative models, approximating the entire curve with similar computations to log-likelihood estimation.
result Approximating the entire RD curve provides a more comprehensive quality assessment than scalar-valued metrics.
Proposes a new learning method for RBMs that combines strengths of forward and reverse KLD.
problem Underfitting and mode-collapse issues in RBM learning.
method Ratio divergence learning using target energy.
result Significantly outperforms other learning methods in energy function fitting, mode-covering, and stability.
We prove that the braid group on 4 strings, as well as its central quotient , have the property RD of Haagerup-Jolissaint. It follows that the automorphism group $\Aut(F_2)$ of the free group on 2 generators has property RD. We also prove that the braid group is a group of intermediate rank …
Robust STAP with coprime arrays reduces clutter using sparse modeling.
problem Limited performance due to training samples support in practical applications.
method Two-stage approach: 1) RD virtual snapshot, 2) RD sparse measurement modeling with OMP-like recovery.
result Robust to prior knowledge errors, good clutter suppression performance.
Modified BA algorithm computes RD and DR functions efficiently.
problem Computing rate-distortion and distortion-rate functions.
method A novel modification of the BA algorithm using Newton's method for root-finding.
result The modified algorithm converges to RD and DR function solutions with rate and provides -approximations.
C3 compresses images and videos with low complexity and high performance.
problem High complexity and low performance in neural compression models.
method Overfits a small model to each image or video separately, improving RD performance with low complexity.
result Matches the RD performance of state-of-the-art neural and video codecs with significantly lower decoding complexity.
R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimizationq-fin.CP
RD-Agent(Q) automates quantitative finance research and development.
problem Challenges in asset return prediction due to high dimensionality and volatility.
method Data-centric multi-agent framework for automated research and development of quantitative strategies.
result Up to 2X higher annualized returns with 70% fewer factors.
RTNet uses both deep and pixel-level features for partial domain adaptation.
problem Selecting relevant source samples for knowledge transfer in partial domain adaptation.
method Reinforced Transfer Network (RTNet) with a reinforced data selector (RDS) and domain adaptation model.
result RTNet achieves state-of-the-art performance on benchmark datasets.
Algorithm uncovers treatment effect heterogeneity in educational RD designs.
problem Discovering sources of treatment effect heterogeneity in regression discontinuity designs.
method Causal supervised machine learning algorithm to build a 'regression discontinuity tree'.
result Algorithm uncovers various sources of heterogeneity in the impact of attending a better secondary school.
SciRE-Solver accelerates DMs sampling by recursively calculating the score function derivative.
problem Slow iterative process of diffusion models due to estimating the score function derivative.
method Recursive Difference (RD) method combined with truncated Taylor expansion of score-integrand.
result SciRE-Solver achieves state-of-the-art FIDs with significantly fewer score function evaluations.
This paper proposes RDS to improve model diversity in data sampling.
problem Data selection process can lead to performance issues in machine learning models.
method Formulates optimisation problem for model diversification, introduces diverse base learners and ensemble reward mechanisms.
result RDS method enhances model performance on various datasets.
Extends likelihood ratio exponential families to analyze various optimization methods.
problem Analyzing optimization methods like rate-distortion and information bottleneck.
method Linking geometric mixture paths to exponential families and using hypothesis testing.
result Provides a common mathematical framework for understanding these methods.
VINE reconstructs networks from diffusion data efficiently.
problem Reconstructing networks from limited diffusion data.
method Variational Inference for Network Reconstruction (VINE).
result VINE accurately recovers connected graphs from diffusion data.
We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd poly log d) Pauli measurements. This has applications in quantum state tomography, and is a non-commutative analogue of a well-known problem in compressed sensing: recovering a sparse vector from a few of its Fourier coeffic…
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designsstat.ML
Bayesian estimators for causal inference using hierarchical Gaussian Processes.
problem Estimating causal effects in sharp and fuzzy RD/RK designs.
method Hierarchical Gaussian Process models for regression and classification.
result Hierarchical GP models improve precision and coverage of RD/RK estimations.
In this study, we have identified slant helix ( type slant helix, slant helix ( type slant helix) and attained some characteristic properties in the Euclidean 5-Space . In addition to this, we have proven that there are no other helices other than helix (inclined curve), sla…
New method for causal inference in survival outcomes using RDD.
problem Censoring in time-to-event analyses.
method Nonparametric approach with doubly robust censoring corrections.
result Higher efficiency and robustness to misspecification.
Paper proves Gromov's conjecture on manifolds with certain group properties.
problem Gromov's conjecture on positive scalar curvature and simplicial volume.
method Proves conjecture under a fundamental group decay property.
result Proves Gromov's conjecture for manifolds with a weakened rapid decay property.
The purpose of this paper is to identify a relevant statistical correlation between rate of default, RD, and loss given default, LGD, in a major Brazilian financial institution Retail Home Equity exposure rated using the IRB approach, so that we may find a causal relationship between the two risk parameters. Therefore,…
New method constructs bounded cohomology classes for specific transformation groups.
problem Constructing classes in bounded cohomology of transformation groups.
method New method of constructing classes in bounded cohomology of transformation groups.
result 3rd bounded cohomology of certain groups is infinite dimensional.
This study explains how adversarial interaction creates non-homogeneous patterns using a pseudo-Reaction-Diffusion model.
problem Understanding how adversarial interaction leads to non-homogeneous patterns in systems.
method Developed a pseudo-Reaction-Diffusion model to explain the mechanism.
result Turing instability is involved in creating non-homogeneous patterns.
Extra large Artin groups are simple and have unique geodesics.
problem Characterizing and understanding XXL type Artin groups.
method Simple locally CAT(0) classifying space and rank 1 periodic geodesic.
result Extra large type Artin groups are acylindrically hyperbolic.
Strong bolicity helps prove Baum-Connes conjecture for certain hyperbolic groups.
problem Proving the Baum-Connes conjecture for relatively hyperbolic groups.
method Constructing a strongly bolic metric and using masks for random coset representatives.
result Deduced the Baum-Connes conjecture for groups satisfying (RD) and certain parabolics.
New tensor completion method outperforms existing approaches with fewer samples.
problem Estimating low-rank tensors from a subset of revealed entries.
method Unfolding-based spectral algorithms, leveraging singular space estimation.
result Spectral methods can match or outperform sum-of-squares methods with fewer samples.
Paper models and compresses wideband CSI feedback in FDD MIMO systems.
problem Fundamental limits of channel state information (CSI) feedback in FDD massive MIMO systems.
method Modeling CSI as a Gaussian-mixture source with latent geometry states, proposing Gaussian-mixture transform coding (GMTC).
result Near-optimal CSI compression achieved through state-adaptive transform coding without large neural encoders.
This purpose of this write-up is to share an idea for accurate computation of Laplace eigenvalues on a broad class of smooth domains. We represent the eigenfunction as a linear combination of eigenfunctions corresponding to the common eigenvalue :\EQN{6}{1}{}{0}{\RD{\CELL{u(r,θ) =\sum_{n=0}^{N}P_{n}J_{n}(ρ) …
A car as parabolic geometrymath.DG
A car's motion shows flat parabolic geometry.
problem Understanding the geometry of a car's motion.
method Analyzing a car as a nonholonomic system.
result Shows a car's motion as a flat parabolic geometry.
The paper studies third-order PDEs invariant under affine transformations and connects them to the Fubini-Pick invariant.
problem Investigating third-order PDEs invariant under affine transformations.
method Using a general method introduced in [D.V. Alekseevsky, J. Gutt, G. Manno, and G. Moreno: A general method to construct invariant PDEs on homogeneous manifolds].
result Derives third-order PDEs from the Fubini-Pick invariant.
Study shows attention-style models learn pairwise interactions efficiently.
problem Learning pairwise interactions in attention-style models.
method Proved minimax rate of convergence for learning pairwise interactions.
result Minimax rate is independent of embedding dimension and token number.
This paper is a natural companion of [Alekseevsky D.V., Alonso Blanco R., Manno G., Pugliese F., Ann. Inst. Fourier (Grenoble) 62 (2012), 497-524, arXiv:1003.5177], generalising its perspectives and results to the context of third-order (2D) Monge-Ampère equations, by using the so-called "meta-symplectic structure" ass…
Convolutional neural networks improve human pose estimation from videos.
problem Estimating 3D pose of humans from monocular vision.
method 3D CNN applied to RGB videos, encoding time as the 3rd dimension.
result Achieves state-of-the-art performance on Human3.6M dataset.
The paper studies geometric properties of higher genera for proper Lie group actions.
problem Investigating geometric properties of higher genera for proper Lie group actions.
method Established index formulae for C^*-higher indices of G-equivariant Dirac-type operators.
result G-homotopy invariance of higher signatures and vanishing of A-hat genera for certain manifolds.
This paper describes magnitude homology of metric spaces using order complexes.
problem Magnitude homology of metric spaces and order complexes.
method Using tensor products, direct sums, and degree shifts from order complexes of interval posets.
result Magnitude homology groups carry information about the diameter of a hole and can have torsion.
Paper improves deep point cloud compression techniques.
problem Efficiently compressing 3D point cloud data for various applications.
method Integrates scale hyperprior model, deeper transforms, focal loss, and optimal thresholding.
result Achieves significant BD-PSNR gains over existing methods.
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
By using the relation between foliations and exotic R^4, orbifold -theory deformed by a gerbe can be interpreted as coming from the change in the smoothness of R^4. We give various interpretations of integral 3-rd cohomology classes on S^3 and discuss the difference between large and small exotic R^4. Then we show t…
Clarifies a conjecture about maps between disks and spheres.
problem Proving a conjecture about maps between disks and spheres.
method Analyzes conditions for a map to extend to a new configuration.
result A necessary condition for the conjecture to hold is established.
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
This paper improves fMRI analysis by modeling higher-order tensors.
problem Ineffective tensor-based methods in spatially folded fMRI data.
method Higher-order Block Term Decomposition (BTD) applied to 4 or 5 order tensors.
result Demonstrated effectiveness of BTD in fMRI analysis through simulations.
Survey on geometric foundations of data reduction methods.
problem High-dimensional data with intrinsic nonlinear structure.
method Spectral manifold learning methods.
result Derivation and convergence analysis of spectral manifold learning.
New algorithms update dynamic graph regression faster than existing methods.
problem Efficiently updating linear regression solutions for dynamic graphs.
method Subsampled randomized Hadamard transform and CountSketch.
result First sublinear update time randomized algorithms for dynamic graph regression.
Solves curvature invariant for -distributions, leading to new metrics.
problem Local equivalence problem for -distributions.
method Reduction of 6th and 7th order nonlinear ODEs to generalised Chazy equations.
result New metrics with split symmetries.
FLIPHAT addresses joint differential privacy for high-dimensional sparse linear bandits.
problem Efficient sequential decision-making with high-dimensional sparse features and privacy concerns.
method FLIPHAT combines iterative forgetting and N-IHT for sparse linear regression, achieving optimal regret.
result FLIPHAT achieves optimal regret in terms of privacy parameters, context dimension, and time horizon.
A new theorem eliminates higher-multiplicity intersections in manifold topology.
problem Eliminating intersections in manifold topology, especially in higher dimensions.
method Proved and applied the -fold Whitney trick for proper maps from disjoint unions of disks to -dimensional balls.
result A continuous map can be modified to avoid intersections in higher dimensions.