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.
StrTransformer recovers sources without labels by optimizing latent matrices and enforcing structural constraints.
StrADiff separates sources from mixtures without labels, using structured priors.
HTFM improves mode coverage and tail-statistic recovery for heavy-tailed data.
Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, i…
Transfer learning improves loan recovery rate forecasting under data scarcity.
Plug-and-play L-GM-AMP improves CS recovery for any i.i.d. source prior.
PDGMM-VAE uses adaptive priors for better ICA recovery.
The paper evaluates samplers on multi-modal targets, focusing on mode separation and recovery.
Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation…
In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vec…
Study on sparse recovery with mixed-quality data, establishing sample-size conditions.
Recovering edge activities from node activity data in temporal networks.
Study sparse function recovery from indirect noisy observations using -regularization.
We consider two areas of research that have been developing in parallel over the last decade: blind source separation (BSS) and electromagnetic source estimation (ESE). BSS deals with the recovery of source signals when only mixtures of signals can be obtained from an array of detectors and the only prior knowledge con…
Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals. We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gau…
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. I…
New framework extends ICA for non-independent variables, identifying pairwise mean independence.
Auto-Encoders are unsupervised models that aim to learn patterns from observed data by minimizing a reconstruction cost. The useful representations learned are often found to be sparse and distributed. On the other hand, compressed sensing and sparse coding assume a data generating process, where the observed data is g…
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features for each source signal and use convex methods to estimate the correlations between these features and the unobserved signal decomposition. We…
A new model integrates covariates with grade of membership analysis for better latent structure recovery.
SAHMM-VAE separates sources adaptively using hidden Markov priors.
Low-rank matrix factorizations arise in a wide variety of applications -- including recommendation systems, topic models, and source separation, to name just a few. In these and many other applications, it has been widely noted that by incorporating temporal information and allowing for the possibility of time-varying …
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We foc…
In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To spe…
Machine learning improves sub-hourly precipitation data recovery.
New method for separating mixed signals with nonlinear functions.
Bayesian method improves EEG source localization and estimates skull conductivity.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirica…
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal. Existing approaches leverage assumptions on the structure of these signals, such as sparsity or the availability of a generative model. A domain-specific generative model can provide a stronger prior and thus allo…
AVICA estimates noise levels for better group ICA source recovery.
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals reco…
This work tackles community detection in networks with node attributes, achieving exact recovery.
New method uses PINNs to solve complex PDEs with sparse measurements.
Paper proposes fast, robust methods for low-rank matrix recovery.
Unified framework for distribution shift estimation, explanation, and improvement.
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatia…
Federated online learning for streaming data with privacy and efficiency.
Identifiability, or recovery of the true latent representations from which the observed data originates, is de facto a fundamental goal of representation learning. Yet, most deep generative models do not address the question of identifiability, and thus fail to deliver on the promise of the recovery of the true latent …
Boosting improves ICA for better component recovery.
We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the expected recovery value.
New method controls false detections in brain activity localization.
New framework for understanding BSS robustness under model violations.
We study the problem of recovery both the attenuation and the source in the attenuated X-ray transform in the plane. We study the linearization as well. It turns out that there are natural Hamiltonian flow that determines which singularities we can recover. If the perturbations , are supported in a com…
Robust methods for high-dimensional linear learning improve performance under heavy-tailed distributions and outliers.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.
New model for recovering unverifiable signals from observers in decentralized networks.