
Neural Networks are Surprisingly Modular
The learned weights of a neural network are often considered devoid of s...
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Bayesian Relational Memory for Semantic Visual Navigation
We introduce a new memory architecture, Bayesian Relational Memory (BRM)...
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Learning Plannable Representations with Causal InfoGAN
In recent years, deep generative models have been shown to 'imagine' con...
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Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adap...
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Inverse Reward Design
Autonomous agents optimize the reward function we give them. What they d...
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Servant of Many Masters: Shifting priorities in Paretooptimal sequential decisionmaking
It is often argued that an agent making decisions on behalf of two or mo...
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Should Robots be Obedient?
Intuitively, obedience  following the order that a human gives  seem...
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The OffSwitch Game
It is clear that one of the primary tools we can use to mitigate the pot...
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Swift: Compiled Inference for Probabilistic Programming Languages
A probabilistic program defines a probability measure over its semantic ...
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Cooperative Inverse Reinforcement Learning
For an autonomous system to be helpful to humans and to pose no unwarran...
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Probabilistic ModelBased Approach for Heart Beat Detection
Nowadays, hospitals are ubiquitous and integral to modern society. Patie...
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Selecting Computations: Theory and Applications
Sequential decision problems are often approximately solvable by simulat...
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Towards Practical Bayesian Parameter and State Estimation
Joint state and parameter estimation is a core problem for dynamic Bayes...
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Research Priorities for Robust and Beneficial Artificial Intelligence
Success in the quest for artificial intelligence has the potential to br...
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Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
An algorithm for automated construction of a sparse Bayesian network giv...
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FineGrained DecisionTheoretic Search Control
Decisiontheoretic control of search has previously used as its basic un...
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Image Segmentation in Video Sequences: A Probabilistic Approach
"Background subtraction" is an old technique for finding moving objects ...
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Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex s...
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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
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Decayed MCMC Filtering
Filteringestimating the state of a partially observable Markov proces...
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Improving Gradient Estimation by Incorporating Sensor Data
An efficient policy search algorithm should estimate the local gradient ...
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RAPID: A Reachable Anytime Planner for Impreciselysensed Domains
Despite the intractability of generic optimal partially observable Marko...
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Gibbs Sampling in OpenUniverse Stochastic Languages
Languages for openuniverse probabilistic models (OUPMs) can represent s...
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A temporally abstracted Viterbi algorithm
Hierarchical problem abstraction, when applicable, may offer exponential...
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Variational MCMC
We propose a new class of learning algorithms that combines variational ...
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A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
The mean field methods, which entail approximating intractable probabili...
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Graph partition strategies for generalized mean field inference
An autonomous variational inference algorithm for arbitrary graphical mo...
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A compact, hierarchical Qfunction decomposition
Previous work in hierarchical reinforcement learning has faced a dilemma...
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On DiscreteContinuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite of the recent successes of probabilistic programming languages (...
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DiscreteContinuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite the recent successes of probabilistic programming languages (PPL...
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An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Our goal is for AI systems to correctly identify and act according to th...
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Inverse reinforcement learning for video games
Deep reinforcement learning achieves superhuman performance in a range o...
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Adversarial Policies: Attacking Deep Reinforcement Learning
Deep reinforcement learning (RL) policies are known to be vulnerable to ...
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