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.
Two Bayesian optimization methods tackle dynamic design spaces with mixed variables.
The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…
New RL approach learns dynamic VCG mechanisms in unknown MDP environments.
New BED method handles online inference for partially observed dynamical systems.
Algorithm learns optimal dynamic mechanisms from data.
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Nonlinear state space models provide an interpretable signal processing framework by combinin…
Gradient-free framework for Bayesian experimental design in complex systems.
Framework simplifies vision-based control and goal discovery.
New model predicts video sequences with latent dynamics.
We propose a new approach to solve optimal stopping problems via simulation. Working within the backward dynamic programming/Snell envelope framework, we augment the methodology of Longstaff-Schwartz that focuses on approximating the stopping strategy. Namely, we introduce adaptive generation of the stochastic grids an…
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
Bayesian approach for learning spatiotemporal systems from noisy data.
The paper introduces Causal Neural Operators to approximate operators in stochastic analysis.
Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evol…
Bézier-GAN optimizes airfoil design by reducing shape complexity.
Novel approach to Bayesian experimental design for non-exchangeable data.
Adapts MBDOE for real-time parameter estimation in complex systems.
Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In …
Persistent homology provides a new, efficient molecular descriptor for protein dynamics.
We introduce a framework for Newton's flows in probability space with information metrics, named information Newton's flows. Here two information metrics are considered, including both the Fisher-Rao metric and the Wasserstein-2 metric. A known fact is that overdamped Langevin dynamics correspond to Wasserstein gradien…
Efficiently selects top-m designs for various contexts using sequential sampling.
Many dynamical systems exhibit similar structure, as often captured by hand-designed simplified models that can be used for analysis and control. We develop a method for learning to correspond pairs of dynamical systems via a learned latent dynamical system. Given trajectory data from two dynamical systems, we learn a …
Meta-Dynamic models learn shared neural dynamics across tasks.
Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of ability to effectively tackle the reality gap. The reliance on absolute or deterministic reward as a metric for optimization process renders rein…
Despite the phenomenal success of deep learning in recent years, there remains a gap in understanding the fundamental mechanics of neural nets. More research is focussed on handcrafting complex and larger networks, and the design decisions are often ad-hoc and based on intuition. Some recent research has aimed to demys…
Framework learns image dynamics between time steps using latent variables.
Unified framework for robust A/B testing under model misspecification.
New method uses neural nets in Hilbert space for option pricing on flow forwards.
We investigate two new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametr…
Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, we must often compute expected downstream values corresponding to current decision…
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. By leveraging recent work connecting deep neural networks to systems of differential equations, we propose \emph{variational integrator networks}, a class of neural network architecture…
Researchers dissect Neural ODEs to understand their dynamics.
Value functions struggle to represent transition dynamics, impacting statistical efficiency.
This paper introduces a new neural ODE model for continuous-time sequence generation.
Constraints improve deep neural network training by stabilizing and enhancing robustness.
Model learns Lagrangian dynamics from images for better prediction and control.
DaringFed incentivizes clients in OFL with dynamic rewards under TII.
We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system's evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithful…
The paper proposes a method to identify causal structure in complex dynamical systems.
We consider solution of stochastic storage problems through regression Monte Carlo (RMC) methods. Taking a statistical learning perspective, we develop the dynamic emulation algorithm (DEA) that unifies the different existing approaches in a single modular template. We then investigate the two central aspects of regres…
This paper introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks (SAWs) in the state space. As a consequence, many bits are flipped in a single MCMC step. We name the alg…
Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…
Bayesian optimization identifies optimal alloy formulations.
VTIRT speeds up IRT inference for dynamic learner proficiency.
Paper proposes a new method to optimize robot body structure and control policy.
Meta-learning improves drone trajectory design for dynamic wireless networks.
New method designs antimicrobial peptides with high potency and low toxicity.
The present paper proposes a unified geometric framework for coordinated motion on Lie groups. It first gives a general problem formulation and analyzes ensuing conditions for coordinated motion. Then, it introduces a precise method to design control laws in fully actuated and underactuated settings with simple integra…