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 method for eliciting non-parametric joint priors using normalizing flows.
SurvMixClust clusters survival data and predicts individual survival curves.
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequently employed, where a prominent class of approximation techniques is ba…
KL-regularized RL from expert demos can lead to slow, unstable learning.
A new method for sparse Gaussian process regression using correlated experts.
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. I…
Dagma-DCE improves causal discovery with interpretable measures and open-source code.
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan a…
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-parametric models of the environment such that the final value estimate has the least expected error. We do so by first estimating the local a…
Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heteroscedastic Gaussian process (HGP) regression which integrates the latent function and the noise function together in a unified non-parametric B…
Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website "Backpage"-- used for classified advertisement-- to discern potential patterns of human trafficking activities which…
The paper tackles optimal level set estimation in crowdsourcing and tournaments.
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent s…
Automatically counts microglial cells in rat spinal cord images, providing precise counts and uncertainty estimates.
LINTEL improves INTEL's time series prediction by optimizing computation and accuracy.
In this paper, we consider a model called CHARME (Conditional Heteroscedastic Autoregressive Mixture of Experts), a class of generalized mixture of nonlinear nonparametric AR-ARCH time series. Under certain Lipschitz-type conditions on the autoregressive and volatility functions, we prove that this model is stationary,…
A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency …
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local reg…
Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversio…
Multi-expert L2D underfits more severely, requiring new methods.
Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neig…
The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP) regression, a well-known non-parametric and interpretable Bayesian model, which suffers from cu…
NPOD algorithm improves efficiency in estimating pharmacokinetic parameters.
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
This study examines when non-parametric methods are robust to adversarial examples.
A key requirement for supervised machine learning is labeled training data, which is created by annotating unlabeled data with the appropriate class. Because this process can in many cases not be done by machines, labeling needs to be performed by human domain experts. This process tends to be expensive both in time an…
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixtur…
We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We p…
MAGI-X learns unknown dynamics from data without numerical integration.
Study provides guarantees for kernel clustering under non-parametric mixtures.
Develops flexible non-parametric ACFs using B-spline kernels.
Improved time series forecasting with expert loss integration.
HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.
NAMEx merges experts using Nash bargaining for improved performance.
Expert augmentation improves hybrid model generalization.
A novel MCMC method clusters data faster and more accurately.
New method calibrates Gaussian product experts for better predictions.
Meta-algorithm optimizes nonstochastic bandits with infinitely many experts.
One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data. Most prior art has focused on enforcing priors in the form of invariances to parametric nuisance transformations that are expected to be present in data. Le…
Study evaluates policies in partially observable environments without full model specification.
New algorithm reduces expert prediction regret for two experts.
The article improves prediction by aggregating Kalman recursions online.
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.