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Found 23 papers, 8 papers with code
Efficient Incremental Belief Updates Using Weighted Virtual Observations
We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference in Bayesian statistical models represented by probabilistic programs.
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach
Systems are commonly monitored for health and security through collection and streaming of multivariate time series.
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs
Reinforcement learning via inference with stochastic preferences naturally describes agent behaviors, does not require introducing rewards and exponential weighing of trajectories, and allows to reason about agents using the solid foundation of Bayesian statistics.
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data
The posterior distribution of model parameters is then used to \textit{stochastically condition} a complementary model, such that inference on new data yields the same posterior distribution of latent parameters corresponding to the new data as inference on a hierachical model on the combination of both previously available and new data, at a lower computation cost.
Bayesian Policy Search for Stochastic Domains
In this work, we cast policy search in stochastic domains as a Bayesian inference problem and provide a scheme for encoding such problems as nested probabilistic programs.
Probabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distributions of observable variables.
Stochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs).
Stochastic Probabilistic Programs
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them.
Warped Input Gaussian Processes for Time Series Forecasting
We introduce a Gaussian process-based model for handling of non-stationarity.
Deployable probabilistic programming
As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development.
Population Anomaly Detection through Deep Gaussianization
A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated.
A Planning Approach to Monitoring Behavior of Computer Programs
Given a system call trace, we simulate the corresponding operators on our model and by observing the properties of the state reached, we learn about the nature of the original program and its behavior.
Process Monitoring on Sequences of System Call Count Vectors
We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network.
Session Analysis using Plan Recognition
This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a costumer with the company's online interface.
Black-Box Policy Search with Probabilistic Programs
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems.
Maximum a Posteriori Estimation by Search in Probabilistic Programs
We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC).
Path Finding under Uncertainty through Probabilistic Inference
We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models.
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH).
Rational Deployment of Multiple Heuristics in IDA*
Recent advances in metareasoning for search has shown its usefulness in improving numerous search algorithms.
Justifying and Improving Meta-Agent Conflict-Based Search
We suggest a justification for the use of a fixed threshold on the number of conflicts based on the analysis of a model problem.
Selecting Computations: Theory and Applications
Sequential decision problems are often approximately solvable by simulating possible future action sequences.
Towards Rational Deployment of Multiple Heuristics in A*
The obvious way to use several admissible heuristics in A* is to take their maximum.
