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.
Proposes using equivariant generative models for compressed sensing with unknown orientations.
Bayesian neural networks' performance varies with prior choice, affecting their ability to identify unknowns.
Proposes PE-GP-UCB for time-varying Bayesian optimisation.
We study the problem of learning shared structure \emph{across} a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynami…
Thompson sampling used for linear bandits with normal-gamma priors.
Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…
The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.
MetaTS learns to explore better by meta-learning prior from bandit instances.
Bayesian framework integrates prior and data knowledge for nonlinear dynamical systems.
Unrolled networks learn optimal Bayesian inference for unknown priors.
Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong theoretical guarantees in Bayesian optimization are often regrettably compromised in practice because of unknown parameters in the prior. In this paper, we adopt a variant of empirical Bayes and show that, by estimating the Gaussi…
Method learns SDEs from one trajectory using GP priors and randomized cross-validation.
Two algorithms improve GP bandits by selecting priors and minimizing regret.
Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.
Proposes a learned Bayesian Cramér-Rao bound for unknown measurement models.
The paper deals with learning probability distributions of observed data by artificial neural networks. We suggest a so-called gradient conjugate prior (GCP) update appropriate for neural networks, which is a modification of the classical Bayesian update for conjugate priors. We establish a connection between the gradi…
Proposes NUV priors for half-space and box constraints.
MAXENT method outperforms ML in sparse data with specific prior correlations.
New method models unknown systems with hidden parameters using neural networks.
Paper develops PAC-Bayes bounds for unknown linear systems.
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to determine how likely an estimation of the unknown parameters is via formulating the posterior distribution. Unfortunately, it is often not possibl…
This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a…
Bayesian method mitigates negative transfer in unknown source data.
Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential …
This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: (1) how to estimate an unknown eigenvector when the eigen-gap (i.e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; (2) …
Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean. This assumption naturally leads to the common choice of a standard Gaussian prior over continuous latent variables. Recent work has, however, shown that this prior has a detrimental ef…
PGD algorithms solve nonlinear inverse problems with generative priors using noisy measurements.
This paper learns prior models from indirect data efficiently.
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
Identifies root causes of outliers in unknown cyclic graphs.
Solving inverse problems continues to be a central challenge in computer vision. Existing techniques either explicitly construct an inverse mapping using prior knowledge about the corruption, or learn the inverse directly using a large collection of examples. However, in practice, the nature of corruption may be unknow…
In this paper, we derive Hybrid, Bayesian and Marginalized Cramér-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compr…
Bayesian method synthesizes barrier certificates for unknown systems with latent states.
One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utili…
Bayesian neural network achieves nearly optimal performance in Besov space.
The paper proposes a semi-parametric Bayesian network model using Gaussian Processes and Horseshoe priors.
Weak diffusion priors can still perform well in inverse problems.
The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999) can incorporate knowledge about the learning algorithm and (data) distribution through the use of distribution-dependent priors, yielding tighter generalization bounds on data-dependent posteriors. Using this flexibility, however, is difficult,…
Optimality of TS with noninformative priors proven for Pareto model.
The paper extends and applies a new shrinkage prior in Bayesian factor analysis.
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowl…
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
DS-TS adapts to abrupt and smooth changes in bandit problems.
Oracle inequality for sparse neural nets adapts to unknown structure.
Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression models, which allow an unknown p …
Paper uses SGLD to recover signals from generative models, proving convergence under mild conditions.
In this paper we study the problem of recovering a structured but unknown parameter from nonlinear observations of the form for . We develop a framework for characterizing time-data tradeoffs for a variety of parameter estimation algorithms when…