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.
Two EM algorithms estimate prior distributions in mixture of linear regressions.
Bayesian metalearning improves performance in linear bandits with misspecified priors.
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all -approximate algorithms are …
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…
Two algorithms improve GP bandits by selecting priors and minimizing regret.
We construct geometric shrinkage priors for Kählerian signal filters. Based on the characteristics of Kähler manifolds, an efficient and robust algorithm for finding superharmonic priors which outperform the Jeffreys prior is introduced. Several ansätze for the Bayesian predictive priors are also suggested. In particul…
This paper analyzes and guarantees convergence of prior-guided ZO algorithms.
Bayesian algorithms perform well even with misspecified priors, especially in meta-learning.
Corrected and improved simulation methods for Dirichlet-Laplace prior.
Sharp asymptotics derived for phase retrieval and compressed sensing with random generative priors.
Proposes PE-GP-UCB for time-varying Bayesian optimisation.
The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more eff…
New method samples Jeffreys prior for objective Bayesian inference.
We present an iterative Markov chainMonte Carlo algorithm for computingreference priors and minimax risk forgeneral parametric families. Ourapproach uses MCMC techniques based onthe Blahut-Arimoto algorithm forcomputing channel capacity ininformation theory. We give astatistical analysis of the algorithm,bounding the n…
Thompson sampling has impressive empirical performance for many multi-armed bandit problems. But current algorithms for Thompson sampling only work for the case of conjugate priors since these algorithms require to infer the posterior, which is often computationally intractable when the prior is not conjugate. In this …
Develops a new algorithm to calibrate signed datasets to specified marginals.
Paper shows how meta-learning can reduce prior learning cost.
We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated U…
Compressive sensing is an impressive approach for fast MRI. It aims at reconstructing MR image using only a few under-sampled data in k-space, enhancing the efficiency of the data acquisition. In this study, we propose to learn priors based on undecimated wavelet transform and an iterative image reconstruction algorith…
New algorithms improve Bayesian linear regression with spike-and-slab priors.
Method recovers complex-valued signals from speckle-noised measurements.
In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsity priors for natural signals and images suffer from poor discriminative capability, while learning-based methods seldom provide concrete the…
DPPS uses DP priors for Bayesian non-parametric multi-arm bandits.
Low-rank matrix estimation from incomplete measurements recently received increased attention due to the emergence of several challenging applications, such as recommender systems; see in particular the famous Netflix challenge. While the behaviour of algorithms based on nuclear norm minimization is now well understood…
The vast majority of optimization and online learning algorithms today require some prior information about the data (often in the form of bounds on gradients or on the optimal parameter value). When this information is not available, these algorithms require laborious manual tuning of various hyperparameters, motivati…
Plug-and-play L-GM-AMP improves CS recovery for any i.i.d. source prior.
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstructi…
Exemplar-based clustering methods have been shown to produce state-of-the-art results on a number of synthetic and real-world clustering problems. They are appealing because they offer computational benefits over latent-mean models and can handle arbitrary pairwise similarity measures between data points. However, when…
In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly …
New method learns fusion rules from few images using granular ball priors.
Algorithm estimates graph structure with prior information and Langevin diffusion.
Proposes a new model for community detection using neural priors.
Proposes NUV priors for half-space and box constraints.
Paper formulates mutual information optimal control for discrete-time systems.
The paper proposes algorithms to learn priors for model averaging.
A new framework improves tensor completion accuracy by considering numerical priors.
New method improves Robbins-Monro algorithm convergence with prior information.
New method needed for class prior estimation when covariates are reduced.
The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.
Improved Langevin algorithms with prior diffusion achieve dimension-independent convergence for non-log-concave distributions.
A novel minimax classifier tackles imbalanced datasets with few minority samples.
While Bayesian methods are praised for their ability to incorporate useful prior knowledge, in practice, convenient priors that allow for computationally cheap or tractable inference are commonly used. In this paper, we investigate the following question: for a given model, is it possible to compute an inference result…
We propose a novel approach for nonlinear regression using a two-layer neural network (NN) model structure with sparsity-favoring hierarchical priors on the network weights. We present an expectation propagation (EP) approach for approximate integration over the posterior distribution of the weights, the hierarchical s…
New algorithm reduces regret in multi-armed bandit problems with Gaussian rewards.
Paper presents a robust transfer learning method for active level set estimation.
PIPA aligns preferences without reinforcement learning, improving language model performance.
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can no…
A new algorithm discovers causal factors between T2DM and bone mineral density.