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.
Proposes DGCN with trajectory sampling for data-efficient policy search in MBRL.
The paper studies the concepts of hedging and arbitrage in a non probabilistic framework. It provides conditions for non probabilistic arbitrage based on the topological structure of the trajectory space and makes connections with the usual notion of arbitrage. Several examples illustrate the non probabilistic arbitrag…
CoverNet predicts urban driving trajectories using diverse sets of possible actions.
MultiPath predicts multi-modal future trajectories for better motion planning.
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources. Applied in an auto-regressive way it provides an explicit probability distribution over continuations of a given initial trajectory segment. We discuss it in relation to (more complicated) existing work and…
The paper develops general, discrete, non-probabilistic market models and minmax price bounds leading to price intervals for European options. The approach provides the trajectory based analogue of martingale-like properties as well as a generalization that allows a limited notion of arbitrage in the market while still…
Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion i…
Adaptive vehicle trajectory prediction for safer autonomous driving.
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be in…
The paper studies sub and super-replication price bounds for contingent claims defined on general trajectory based market models. No prior probabilistic or topological assumptions are placed on the trajectory space, trading is assumed to take place at a finite number of occasions but not bounded in number nor necessari…
New model accounts for continuous human trajectories in robotics.
Predicts multiple vehicle trajectories efficiently.
Paper presents a probabilistic framework for diffusion synchronization.
Pattern ensembling fills in missing or inaccurate trajectory data.
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control. Due t…
Develops a neural framework for probabilistic forecasting of dynamical systems.
Using movement primitive libraries is an effective means to enable robots to solve more complex tasks. In order to build these movement libraries, current algorithms require a prior segmentation of the demonstration trajectories. A promising approach is to model the trajectory as being generated by a set of Switching L…
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge model free methods, especially in simulated robotics tasks. Probabilistic ensembles with trajectory sampl…
Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a prog…
New analysis shows FM learns underlying dynamical structure, not just trajectory replay.
Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.
Physics-informed diffusion model detects anomalous trajectories in GPS data.
ProbETA models travel time correlations between trips for better navigation.
LLMs learn probability density functions in-context, showing distinct learning trajectories.
We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2ve…
UK's rapid vaccine rollout linked to reduced COVID-19 mortality.
In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We the…
This work frames active inference through control as inference, offering robust control algorithms.
We provide a model-free pricing-hedging duality in continuous time. For a frictionless market consisting of risky assets with continuous price trajectories, we show that the purely analytic problem of finding the minimal superhedging price of a path dependent European option has the same value as the purely probabi…
Medical researchers are coming to appreciate that many diseases are in fact complex, heterogeneous syndromes composed of subpopulations that express different variants of a related complication. Time series data extracted from individual electronic health records (EHR) offer an exciting new way to study subtle differen…
Trajectory or behavior prediction of traffic agents is an important component of autonomous driving and robot planning in general. It can be framed as a probabilistic future sequence generation problem and recent literature has studied the applicability of generative models in this context. The variety or Minimum over …
Bayesian inference models failure distributions in autonomous systems.
Coordination recognition and subtle pattern prediction of future trajectories play a significant role when modeling interactive behaviors of multiple agents. Due to the essential property of uncertainty in the future evolution, deterministic predictors are not sufficiently safe and robust. In order to tackle the task o…
CDLF predicts product life-cycles in cold-start phases with high accuracy.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
Unified framework for multi-view diffusion geometries using intertwined diffusion trajectories.
Adaptive stopping in MCMC using classifier-based dynamics
Improves RL from historical data by stitching trajectories.
SPECTRA improves probabilistic energy forecasting by separating trends and uncertainties.
MA-COPP predicts multi-agent system outcomes using data from a different policy, with probabilistic guarantees.
The paper defines symmetries in no-arbitrage markets.
Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.
Stable GFlowNets prevent loss spikes and mode collapse in training.
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In real…
EFiGP uses Fourier and eigen-decomposition for efficient ODE parameter estimation.
Unified view of generative models using GFlowNet framework.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic tr…