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.
Continuous semi-implicit models enable faster training and better performance in generative modeling.
Kernel semi-implicit variational inference improves variational inference without additional optimization.
Semi-Implicit Variational Inference (SIVI) is improved with SIVI-SM using score matching.
In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic mar…
Kernel SIVI improves variational inference by avoiding lower-level optimization.
IDAC improves reinforcement learning efficiency by modeling implicit distributions.
Enhances graph modeling with hyperbolic geometry and variational inference.
Neural dynamical systems are dynamical systems that are described at least in part by neural networks. The class of continuous-time neural dynamical systems must, however, be numerically integrated for simulation and learning. Here, we present a compact neural circuit for two common numerical integrators: the explicit …
We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference …
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for better generative modeling of graph dependency structure, together with …
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of existing methods have limited expressive power due to the Gaussian assumption of latent variables. In t…
Unified theory for semi-implicit variational inference, bridging approximation and optimization.
Improved phylogenetic tree reconstruction using flexible branch length distributions.
To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framework. Both theoretically and experimentally, we demonstrate that SIG can generate high quality samples especially when dealing with multi-mod…
We present a dynamical system framework for understanding Nesterov's accelerated gradient method. In contrast to earlier work, our derivation does not rely on a vanishing step size argument. We show that Nesterov acceleration arises from discretizing an ordinary differential equation with a semi-implicit Euler integrat…
Neural network has attracted great attention for a long time and many researchers are devoted to improve the effectiveness of neural network training algorithms. Though stochastic gradient descent (SGD) and other explicit gradient-based methods are widely adopted, there are still many challenges such as gradient vanish…
PVI improves SIVI by directly optimizing ELBO without parametric assumptions.
Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generat…
Improved UIVI method shows better performance than state-of-the-art SIVI methods.
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. By probabilistic modeling of Bernoulli dropout, our method enables more robust prediction and uncertainty quantification in deep m…
Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is largely limited by …
SGRNN models evolving graph data for better property prediction.
SIFG uses noisy particles to efficiently sample from complex distributions.
We consider a numerical approach for the incompressible surface Navier-Stokes equation. The approach is based on the covariant form and uses discrete exterior calculus (DEC) in space and a semi-implicit discretization in time. The discretization is described in detail and related to finite difference schemes on stagger…
Thompson sampling is an efficient algorithm for sequential decision making, which exploits the posterior uncertainty to address the exploration-exploitation dilemma. There has been significant recent interest in integrating Bayesian neural networks into Thompson sampling. Most of these methods rely on global variable u…
Develops a new trading strategy for renewable producers to manage price volatility.
In this paper we investigate a dynamic stochastic portfolio optimization problem involving both the expected terminal utility and intertemporal utility maximization. We solve the problem by means of a solution to a fully nonlinear evolutionary Hamilton-Jacobi-Bellman (HJB) equation. We propose the so-called Riccati met…
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph…
The classical linear Black--Scholes model for pricing derivative securities is a popular model in financial industry. It relies on several restrictive assumptions such as completeness, and frictionless of the market as well as the assumption on the underlying asset price dynamics following a geometric Brownian motion. …
We demonstrate the effectiveness of an adaptive explicit Euler method for the approximate solution of the Cox-Ingersoll-Ross model. This relies on a class of path-bounded timestepping strategies which work by reducing the stepsize as solutions approach a neighbourhood of zero. The method is hybrid in the sense that a c…
SiD distills pretrained diffusion models into a fast one-step generator.
Bayesian approach improves ODE solution accuracy.
The paper studies continuous submodular functions and their optimization.
Bilevel Continual Learning improves continual learning by transferring knowledge effectively.
Continuized Nesterov acceleration accelerates stochastic gradient descent and gossip algorithms.
CANDI solves the gap between continuous and discrete diffusion models for text generation.
Faster policy learning via continuous-time gradients.
Study on Hölder continuity of complex Monge-Ampère solutions on Stein spaces.
Classifies when homeomorphism groups of stable surfaces have automatic continuity.
EBMs improve continual learning without external memory or regularization.
Root's barrier is continuous and finite under certain conditions.
We solve the Dirichlet problem for the complex Monge-Ampère equation on a strictly pseudoconvex with the right hand side being a positive Borel measure which is dominated by the Monge-Ampère measure of a Hölder continuous plurisubharmonic function. If the boundary data is continuous, then the solution is continuous. If…
Study on existence and properties of continuous solutions to complex Hessian equations.
Continuous time framework for discrete data denoising models.
Continuity of Kähler-Einstein potentials at singularities proven.
Study on continual learning with Twitter data, developing ConGraD algorithm.
Continuity of delta invariant leads to uniform Kähler-Einstein metrics.
Proposes a framework for semi-supervised continual learning from sequentially arriving data.