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.
Probabilistic proof of smooth boundaries in optimal stopping problems.
Introduces statistical optimal transport for probabilistic lectures.
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. W…
Proposes VSGD optimizer combining probabilistic and gradient-based methods.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled …
Unified approach for sequence design combining likelihood-free inference and black-box optimization.
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly struc…
Unified approach to non-standard classification tasks.
We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on t…
Bayesian optimization tackles expensive discrete and mixed parameter spaces.
In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a syst…
Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning. Given the intractability of the global control problem, state-of-the-art algorithms focus on approximate sequential optimization techniques, that heavily rely on heuristics for regularization in order to achi…
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a prob…
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a prob…
Study probabilistic safety of BNNs under adversarial attacks.
Adaptive volatility method improves probabilistic financial forecasting.
Solves optimal control with state constraints using probabilistic methods.
New scheme optimizes BMI through probabilistic and geometric shaping.
Simple probabilistic solution for optimal liquidation with linear price impact.
The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the const…
A new probabilistic framework for optimal transport using collective graphical models.
DALTON improves ODE parameter estimation by learning from noisy data.
Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA). However, standard PCA and PPCA are not robust, as they are sensitive to outliers. To alleviate this problem, this paper i…
New theorem connects probabilistic permanental point processes to Monge-Ampère equation.
Probabilistic programming is a powerful abstraction for statistical machine learning. Applying static analysis methods to probabilistic programs could serve to optimize the learning process, automatically verify properties of models, and improve the programming interface for users. This field of static analysis for pro…
Paper optimizes material microstructures with limited data using probabilistic methods.
New method for efficient probabilistic inference using masked language modeling.
Solves probabilistic Lambert problem connecting astrodynamics with optimal mass transport.
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box…
SOBER framework optimizes Bayesian optimization tasks efficiently.
Paper optimizes demand aggregation for low-level electricity markets.
This manuscript proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems with positive definite for . The goal is to replace the point estimates returned by existing methods with a Gaussian posterior belief over the elements of the inverse of , which can …
Study optimal stopping times for multi-dimensional processes with non-exponential discounting.
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corres…
In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffus…
Paper develops a dual formulation for PCA in Hilbert spaces.
This work proposes an unsupervised neural network framework for solving combinatorial optimization problems on graphs.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.
This paper optimizes kernel and acquisition functions for high-dimensional Bayesian Optimization.
CMDNet simplifies MAP detection for large systems with probabilistic relaxation.
Paper presents a probabilistic model to improve LLM cascade performance.
New method uses backward SDEs for deep learning uncertainty.
We consider the problem of optimal risk sharing in a pool of cooperative agents. We analyze the asymptotic behavior of the certainty equivalents and risk premia associated with the Pareto optimal risk sharing contract as the pool expands. We first study this problem under expected utility preferences with an objectivel…
Paper shows how gradient concentration helps in learning from inexact data.
Paper introduces probabilistic digital twins for optimal decision making under uncertainty.