
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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ConQUR: Mitigating Delusional Bias in Deep Qlearning
Delusional bias is a fundamental source of error in approximate Qlearni...
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Domain Aggregation Networks for MultiSource Domain Adaptation
In many realworld applications, we want to exploit multiple source data...
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GenDICE: Generalized Offline Estimation of Stationary Values
An important problem that arises in reinforcement learning and Monte Car...
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The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value ...
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A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of ...
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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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On the Global Convergence Rates of Softmax Policy Gradient Methods
We make three contributions toward better understanding policy gradient ...
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Striving for Simplicity in Offpolicy Deep Reinforcement Learning
Reflecting on the advances of offpolicy deep reinforcement learning (RL...
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TrustPCL: An OffPolicy Trust Region Method for Continuous Control
Trust region methods, such as TRPO, are often used to stabilize policy o...
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Improving Policy Gradient by Exploring Underappreciated Rewards
This paper presents a novel form of policy gradient for modelfree reinf...
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Bridging the Gap Between Value and Policy Based Reinforcement Learning
We establish a new connection between value and policy based reinforceme...
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Adaptive Monte Carlo via Bandit Allocation
We consider the problem of sequentially choosing between a set of unbias...
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Stochastic Neural Networks with Monotonic Activation Functions
We propose a Laplace approximation that creates a stochastic unit from a...
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Learning Bayesian Nets that Perform Well
A Bayesian net (BN) is more than a succinct way to encode a probabilisti...
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Monte Carlo Matrix Inversion Policy Evaluation
In 1950, Forsythe and Leibler (1950) introduced a statistical technique ...
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Generalized Conditional Gradient for Sparse Estimation
Structured sparsity is an important modeling tool that expands the appli...
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Convex Relaxations of Bregman Divergence Clustering
Although many convex relaxations of clustering have been proposed in the...
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Monte Carlo Inference via Greedy Importance Sampling
We present a new method for conducting Monte Carlo inference in graphica...
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Boltzmann Machine Learning with the Latent Maximum Entropy Principle
We present a new statistical learning paradigm for Boltzmann machines ba...
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Maximum Margin Bayesian Networks
We consider the problem of learning Bayesian network classifiers that ma...
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Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
We demonstrate that almost all nonparametric dimensionality reduction m...
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Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
We present a new approach to learning the structure and parameters of a ...
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Rank/Norm Regularization with ClosedForm Solutions: Application to Subspace Clustering
When data is sampled from an unknown subspace, principal component analy...
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Smoothed Action Value Functions for Learning Gaussian Policies
Stateaction value functions (i.e., Qvalues) are ubiquitous in reinforc...
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Planning and Learning with Stochastic Action Sets
In many practical uses of reinforcement learning (RL) the set of actions...
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Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is...
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Understanding the impact of entropy in policy learning
Entropy regularization is commonly used to improve policy optimization i...
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Understanding the impact of entropy on policy optimization
Entropy regularization is commonly used to improve policy optimization i...
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Learning to Generalize from Sparse and Underspecified Rewards
We consider the problem of learning from sparse and underspecified rewar...
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Advantage Amplification in Slowly Evolving LatentState Environments
Latentstate environments with long horizons, such as those faced by rec...
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AlgaeDICE: Policy Gradient from Arbitrary Experience
In many realworld applications of reinforcement learning (RL), interact...
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Learning to Combat CompoundingError in ModelBased Reinforcement Learning
Despite its potential to improve sample complexity versus modelfree app...
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Variational Inference for Deep Probabilistic Canonical Correlation Analysis
In this paper, we propose a deep probabilistic multiview model that is ...
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EnergyBased Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangea...
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Dale Schuurmans
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Professor of Department of Computing Science at University of Alberta, Research Scientist at Google Brain