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.
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris (1972). Since the EB algorithm is essentially the EM algorithm applied to a simple model, it does not require heuristic paramete…
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the c…
Propose an XMSE-aware mixed estimator for EB that interpolates between ML and EB shrinkage.
EB-TCε identifies the best arm with ε confidence in stochastic bandits.
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Mean…
EB-GFN models discrete data with amortized MCMC sampling.
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as -greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well …
EB-PCA reduces noise in high-dimensional PCA by estimating a joint prior distribution.
EB improves asset pricing by mining large strategies without lookahead bias.
Empirical Bayes improves causal representation learning across multiple domains.
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of…
Transformer pretraining yields strong EB performance without explicit adaptation.
EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
Physics-informed learning framework for pH systems and EB-PBC control.
Transformers solve Poisson means estimation via empirical Bayes.
This paper explores adiabatic solutions of Haydys-Witten equations for knot homology.
Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of …
Proposes using external data to improve predictions in medical applications with limited samples.
The betting CI outperforms classical methods in constructing confidence intervals for bounded means.
In \cite{Boed}, C.-F. Bödigheimer constructed a finite cell-complex $\mf{Par}_{g,n,m}$ and a bijective map $\cH: \mf{Dip}_{g,n,m} \to \mf{Par}_{g,n,m}$ (the Hilbert-uniformization) from the moduli space of dipole functions on Riemann surfaces with directions and punctures to $\mf{Par}_{g,n,m}$. In \cite{Boed} a…
A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark …
Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy suffers. We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analys…
We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best …
Minimum numbers of fixed points or of coincidence components (realized by maps in given homotopy classes) are the principal objects of study in topological fixed point and coincidence theory. In this paper we investigate fiberwise analoga and represent a general approach e.g. to the question when two maps can be deform…
In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encounters in the past while navigating a new task. Given an optimal policy in a related task-environment, we show that its bisimulation distance…
Efficient Bitwidth Search optimizes neural network quantization for better performance.
Model predicts stock prices using GAN and RoI Pooling.
PRCD-MAP learns to trust imperfect priors in causal discovery, improving accuracy and robustness.