arXiv research

HTFM improves mode coverage and tail-statistic recovery for heavy-tailed data.

A federated learning framework using superquantile aggregation for robust performance across heterogeneous data.

Improved Clipped-SGD achieves near-optimal heavy-tailed statistical estimation in streaming settings.

In complex systems such as turbulent flows and financial markets, the dynamics in long and short time-lags, signaled by Gaussian and fat-tailed statistics, respectively, calls for a unified description. To address this issue we analyze a real dataset, namely, price fluctuations, in a wide range of temporal scales to em…

New algorithm improves heavy-tailed statistical estimation in streaming data.

New method optimizes language model performance for test-time strategies.

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.

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.

This paper improves support recovery in universal one-bit compressed sensing.

This work provides a guaranteed tensor recovery method by combining low-rankness and smoothness priors.

We discuss a general notion of "sparsity structure" and associated recoveries of a sparse signal from its linear image of reduced dimension possibly corrupted with noise. Our approach allows for unified treatment of (a) the "usual sparsity" and "usual $\ell_1$ recovery," (b) block-sparsity with possibly overlapping blo…

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…

IRKSN algorithm achieves sparse recovery with wider applicability conditions.

Higher-order tensors can represent scores in a rating system, frames in a video, and images of the same subject. In practice, the measurements are often highly quantized due to the sampling strategies or the quality of devices. Existing works on tensor recovery have focused on data losses and random noises. Only a few …

Study finds the cutoff for exact recovery in Gaussian mixture models.

In recent years research on credit risk modelling has mainly focused on default probabilities. Recovery rates are usually modelled independently, quite often they are even assumed constant. Then, however, the structural connection between recovery rates and default probabilities is lost and the tails of the loss distri…

Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.

The paper improves conditions for unique recovery in homomorphic sensing of subspaces.

HSNLD solves robust Hankel recovery efficiently and robustly.

New method improves dictionary recovery from over-realized models.

Unified framework for pattern recovery in penalized and thresholded estimation.

Paper explores exact recovery of communities in weighted graphs using Gaussian and exponential distributions.

Guarantees sparse recovery for neural networks with iterative hard thresholding.

We propose and analyze a generic method for community recovery in stochastic block models and degree corrected block models. This approach can exactly recover the hidden communities with high probability when the expected node degrees are of order $\log n$ or higher. Starting from a roughly correct community partition …

New risk measure improves creditor protection in financial regulation.

We introduce a general framework to handle structured models (sparse and block-sparse with possibly overlapping blocks). We discuss new methods for their recovery from incomplete observation, corrupted with deterministic and stochastic noise, using block-$\ell_1$ regularization. While the current theory provides promis…

New method improves traffic data recovery for streaming data.

Develops a new method to discover stochastic systems with non-Gaussian noise.

We consider the effect of recovery rates on a pool of credit assets. We allow the recovery rate to depend on the defaults in a general way. Using the theory of large deviations, we study the structure of losses in a pool consisting of a continuum of types. We derive the corresponding rate function and show that it has …

Unified framework for constructing nonconvex sparse recovery methods.

Improves sparse recovery with non-linear Fourier features.

We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…

While defaults are rare events, losses can be substantial even for credit portfolios with a large number of contracts. Therefore, not only a good evaluation of the probability of default is crucial, but also the severity of losses needs to be estimated. The recovery rate is often modeled independently with regard to th…

Study exact community recovery in noisy SBM with limited queries.

The paper tracks patient recovery using graphs of joint movement data.

Modified model for Quanto CDS pricing with stochastic recovery and reduced complexity.

This paper investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavytailed impulsive noise is well modelled with stable distributions. Since there is no explicit formulation for the probability density function of $SαS$ distribution, alternative approximations like Genera…

Improves graph recovery in Gaussian graphical modeling.

Flat minima lead to better generalization in low-rank matrix recovery models.

Study shows DNNs can recover functions with fewer samples than model parameters at overparameterization.

Study on sparse recovery with mixed-quality data, establishing sample-size conditions.

New algorithm recovers model coefficients and supports from noisy data.

We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems. Under this framework, we are able to study the recovery of clustering labels, ranks of players, signs of regression coefficients, cyclic shifts, and even group elements from a unified…

Paper reconciles minimax rates and optimal recovery rates for noisy observations.

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited, which generalizes the bandlimited class and is similar to the globally smooth class. We then propose two recovery s…

Paper connects neural network hyperparameter optimization and NAS to structured sparse recovery.

Efficient algorithms for sparse parameter recovery in mixture models.