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Found 16 papers, 3 papers with code
Variational Inference on the Final-Layer Output of Neural Networks
In contrast, Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming due to the large parameter space.
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning
DiSProD builds a symbolic graph that captures the distribution of future trajectories, conditioned on a given policy, using independence assumptions and approximate propagation of distributions.
On the Performance of Direct Loss Minimization for Bayesian Neural Networks
Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method motivated as regularized loss minimization.
Explainable Models via Compression of Tree Ensembles
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs).
Approximate Inference for Stochastic Planning in Factored Spaces
Stochastic planning can be reduced to probabilistic inference in large discrete graphical models, but hardness of inference requires approximation schemes to be used.
Direct loss minimization algorithms for sparse Gaussian processes
The application of DLM in non-conjugate cases is more complex because the logarithm of expectation in the log-loss DLM objective is often intractable and simple sampling leads to biased estimates of gradients.
Sampling Networks and Aggregate Simulation for Online POMDP Planning
Our approach enables scaling to large factored action spaces in addition to large state spaces and observation spaces.
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models
Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.
From Stochastic Planning to Marginal MAP
This yields a novel algebraic gradient-based solver (AGS) for MMAP.
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
The paper furthers such analysis by providing bounds on the excess risk of variational inference algorithms and related regularized loss minimization algorithms for a large class of latent variable models with Gaussian latent variables.
Stochastic Planning and Lifted Inference
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems.
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models
The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data analysis in conjugate Bayesian models and demonstrated to be effective in several problems.
The Complexity of Reasoning with FODD and GFODD
In particular, we study the evaluation problem, the satisfiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability.
Probabilistic Relational Planning with First Order Decision Diagrams
Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation.
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs
We address the scalability of symbolic planning under uncertainty with factored states and actions.
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
The paper applies this framework for data where each task is a phase-shifted periodic time series.
