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.
The chapter compares Gaussian process models for stochastic simulators with varying noise.
Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes. However, a large class of problems comprises underlying temporal dependency structures in a sequence of stochastic processes that Neural Processes (NP) do not e…
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is applicable to both finite hidden Markov models and hierarchical D…
Stochastic gradient descent approximates Gaussian process posteriors efficiently.
We consider an optimal investment and consumption problem for a Black-Scholes financial market with stochastic volatility and unknown stock appreciation rate. The volatility parameter is driven by an external economic factor modeled as a diffusion process of Ornstein-Uhlenbeck type with unknown drift. We use the dynami…
We solve a broad class of sequential decision-making problems with partially observed states.
Algorithm solves job acceptance problem with random arrivals and values.
There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization. We focus on two of the most important fields: stochastic optimal control, with its roots in deterministic optimal control, and reinfo…
PASOA optimizes Bayesian design by improving SMC samplers and EIG.
This work introduces a new model for complex stochastic processes.
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
This paper speeds up iterative GP inference with warm starting.
We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capture sequential struc…
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore, Attentive Neural Process (ANP) improved the prediction accuracy of NPs by incorporating attention mechanism among contexts and targets. In …
We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities. In the framework of stochastic maximum likelihood estimation, we explore the associated risk bound. Further, we consider the superposition o…
Optimal sequential testing for Markovian data with lower and upper bounds.
Model change points in time-series data with neural SDEs and variational autoencoders.
Stochastic gradient descent (SGD) is a well known method for regression and classification tasks. However, it is an inherently sequential algorithm at each step, the processing of the current example depends on the parameters learned from the previous examples. Prior approaches to parallelizing linear learners using SG…
Warm-start strategies speed up GP inference by 19x.
We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic samplers, in particular with heavy-tailed simulation noise and low signal-to-no…
Efficient methods for answering complex probabilistic queries in sequential data.
This study models target trajectories using stochastic processes for efficient tracking.
The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has b…
Online VSMC efficiently learns SSM parameters in streaming data.
We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently…
Combines Bézier curves with Gaussian processes for better sequential data modeling.
Several important families of computational and statistical results in machine learning and randomized algorithms rely on uniform bounds on quadratic forms of random vectors or matrices. Such results include the Johnson-Lindenstrauss (J-L) Lemma, the Restricted Isometry Property (RIP), randomized sketching algorithms, …
In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), which is a novel variant of GPDSs. The proposed model assumes that the output on each dimension is controlled by a shared global latent process and a private local latent process…
Enhances interpolation paths in latent space using particle filters.
Generative adversarial network for probabilistic forecasting of random systems.
State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series. The challenge is two-fold: not only do computations scale linearly with t…
Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with the minimum and maximum p…
Method solves complex optimization problems with high probability bounds.
Signature kernel handles sequential data with theoretical and practical advantages.
We develop a probabilistic framework for sequential random projection.
A training-free method for conditional sampling using flow matching.
The paper develops a state-space approach to deep Gaussian processes for efficient state estimation.
A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.
New SMC samplers improve stochastic optimisation efficiency.
Gaussian processes help in modeling complex, nonlinear relationships in signal processing.
This paper shows how to combine optimal tests into log-optimal processes.
New method solves optimization problems with stochastic objectives and constraints.
In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite …
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
New algorithm solves stochastic optimization problems with unknown gradients.
A new Bayesian filtering method speeds up stochastic Newton optimization.
How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model capacity can be significantly enhanced by introducing stochastic latent variables in the hidden states of recurrent neural networks. Simultan…
A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.