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.
Developed mlf-core for deterministic machine learning.
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of …
We convert deterministic flow models to stochastic samplers.
Unified analysis for deterministic samplers in diffusion models.
This paper benchmarks speech LVMs against deterministic models and adapts a video model to speech.
We study a reinforcement learning setting, where the state transition function is a convex combination of a stochastic continuous function and a deterministic function. Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG). We firstl…
Study shows deterministic equivalent for neural network kernel convergence.
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input. The encoder achieves this by sampling from a distribution for every input, instead of outputting a deterministic c…
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. The latter enjoy asymptotic consistency, but can suffer …
Paper presents a deterministic method for diverse subset selection.
Develops DPG methods for continuous-time RL with deterministic policies.
We resolve the open problem of optimal sample complexity for multicalibration and deterministic predictors.
Improves efficiency of simulators that fail to return.
Variational autoencoders are prominent generative models for modeling discrete data. However, with flexible decoders, they tend to ignore the latent codes. In this paper, we study a VAE model with a deterministic decoder (DD-VAE) for sequential data that selects the highest-scoring tokens instead of sampling. Determini…
The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic le…
This study proposes an approach based on a perturbation technique to construct global solutions to dynamic stochastic general equilibrium models (DSGE). The main idea is to expand a solution in a series of powers of a small parameter scaling the uncertainty in the economy around a solution to the deterministic model, i…
Develops a deterministic method to approximate NSDEs for better uncertainty quantification.
Study on regret minimization in deterministic MDPs.
A deterministic apple tasting learner is developed, confirming a conjecture and providing tight bounds for mistake bounds.
Ever since the proof of asymptotic normality of maximum likelihood estimator by Cramer (1946), it has been understood that a basic technique of the Taylor series expansion suffices for asymptotics of -estimators with smooth/differentiable loss function. Although the Taylor series expansion is a purely deterministic …
The log returns of financial time series are usually modeled by means of the stationary GARCH(1,1) stochastic process or its generalizations which can not properly describe the nonstationary deterministic components of the original series. We analyze the influence of deterministic trends on the GARCH(1,1) parameters us…
Extends linear structural causal models to include deterministic relations and latent confounders for causal discovery.
Deterministic method for certifying neural network robustness.
CGAN fails to improve deterministic sequence predictions, revealing a theoretical limitation.
Develops new bounds for deterministic samplers in diffusion models.
We consider an individual or household endowed with an initial capital and an income, modeled as a deterministic process with a continuous drift rate. At first, we model the discounting rate as the price of a zero-coupon bond at zero under the assumption of a short rate evolving as an Ornstein-Uhlenbeck process. Then, …
Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of deterministic nature. This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy…
Deterministic training improves generative autoencoder performance.
sFML learns stochastic dynamical systems from data.
New bounds for model generalization under deterministic gradient descent.
We obtain a deterministic characterisation of the \emph{no free lunch with vanishing risk}, the \emph{no generalised arbitrage} and the \emph{no relative arbitrage} conditions in the one-dimensional diffusion setting and examine how these notions of no-arbitrage relate to each other.
Develops a new reinforcement learning framework for complex control problems.
State-space systems generate probabilistic dependencies between inputs and outputs.
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of t…
In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering met…
Generative models using PDMPs with explicit jump rates and kernels.
Bayesian attention improves model performance and robustness.
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroi…
New algorithms reduce regret in both stochastic and deterministic environments.
New methods estimate policy value and gradients for deterministic policies from off-policy data.
Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.
PAC-Bayesian framework for fairness in stochastic and deterministic classifiers.
In this paper, we analyse piecewise deterministic Markov processes, as introduced in Davis (1984). Many models in insurance mathematics can be formulated in terms of the general concept of piecewise deterministic Markov processes. In this context, one is interested in computing certain quantities of interest such as th…
The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in . This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of -divergences, and it is related to …
New model predicts dynamic tax evasion with audits and imitation.
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
New method ensures consistent inference across different tensor parallel sizes for large language models.
Identifies most probable flows for Kunita SDEs in fluid dynamics.