
Stein Variational Gradient Descent With MatrixValued Kernels
Stein variational gradient descent (SVGD) is a particlebased inference ...
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Implicit Regularization of Normalization Methods
Normalization methods such as batch normalization are commonly used in o...
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Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
Wide field small aperture telescopes are widely used in optical transien...
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Stein Variational Inference for Discrete Distributions
Gradientbased approximate inference methods, such as Stein variational ...
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Deep Graph Contrastive Representation Learning
Graph representation learning nowadays becomes fundamental in analyzing ...
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Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Recent advances in deep reinforcement learning algorithms have shown gre...
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Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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Blackbox Offpolicy Estimation for InfiniteHorizon Reinforcement Learning
Offpolicy estimation for longhorizon problems is important in many rea...
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Exploration via Hindsight Goal Generation
Goaloriented reinforcement learning has recently been a practical frame...
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Learning Belief Representations for Imitation Learning in POMDPs
We consider the problem of imitation learning from expert demonstrations...
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Dimension Independent Generalization Error with Regularized Online Optimization
One classical canon of statistics is that large models are prone to over...
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Structured Stein Variational Inference for Continuous Graphical Models
We propose a novel distributed inference algorithm for continuous graphi...
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On the DiscriminationGeneralization Tradeoff in GANs
Generative adversarial training can be generally understood as minimizin...
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Sampleefficient Policy Optimization with Stein Control Variate
Policy gradient methods have achieved remarkable successes in solving ch...
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Efficient Localized Inference for Large Graphical Models
We propose a new localized inference algorithm for answering marginaliza...
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Stochastic Variance Reduction for Policy Gradient Estimation
Recent advances in policy gradient methods and deep learning have demons...
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Learning Infinite RBMs with FrankWolfe
In this work, we propose an infinite restricted Boltzmann machine (RBM),...
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Learning to Draw Samples with Amortized Stein Variational Gradient Descent
We propose a simple algorithm to train stochastic neural networks to dra...
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Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE
We propose a number of new algorithms for learning deep energy models an...
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Stein Variational Gradient Descent as Gradient Flow
Stein variational gradient descent (SVGD) is a deterministic sampling al...
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Stein Variational Adaptive Importance Sampling
We propose a novel adaptive importance sampling algorithm which incorpor...
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ICE: Information Credibility Evaluation on Social Media via Representation Learning
With the rapid growth of social media, rumors are also spreading widely ...
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Contextaware Sequential Recommendation
Since sequential information plays an important role in modeling user be...
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Approximate Inference with Amortised MCMC
We propose a novel approximate inference algorithm that approximates a t...
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Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
We propose a simple algorithm to train stochastic neural networks to dra...
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Blackbox Importance Sampling
Importance sampling is widely used in machine learning and statistics, b...
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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
We propose a general purpose variational inference algorithm that forms ...
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Bootstrap Model Aggregation for Distributed Statistical Learning
In distributed, or privacypreserving learning, we are often given a set...
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A Kernelized Stein Discrepancy for Goodnessoffit Tests and Model Evaluation
We derive a new discrepancy statistic for measuring differences between ...
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Decomposition Bounds for Marginal MAP
Marginal MAP inference involves making MAP predictions in systems define...
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Local PerturbandMAP for Structured Prediction
Conditional random fields (CRFs) provide a powerful tool for structured ...
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Regularized Minimax Conditional Entropy for Crowdsourcing
There is a rapidly increasing interest in crowdsourcing for data labelin...
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Communicationefficient sparse regression: a oneshot approach
We devise a oneshot approach to distributed sparse regression in the hi...
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Cheaper and Better: Selecting Good Workers for Crowdsourcing
Crowdsourcing provides a popular paradigm for data collection at scale. ...
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Belief Propagation for Structured Decision Making
Variational inference algorithms such as belief propagation have had tre...
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Distributed Estimation, Information Loss and Exponential Families
Distributed learning of probabilistic models from multiple data reposito...
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Distributed Parameter Estimation via Pseudolikelihood
Estimating statistical models within sensor networks requires distribute...
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Negative Tree Reweighted Belief Propagation
We introduce a new class of lower bounds on the log partition function o...
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Variational Algorithms for Marginal MAP
Marginal MAP problems are notoriously difficult tasks for graphical mode...
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Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
For large scale online inference problems the update strategy is critic...
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Learning to Explore with MetaPolicy Gradient
The performance of offpolicy learning, including deep Qlearning and de...
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BEBP: An Poisoning Method Against Machine Learning Based IDSs
In big data era, machine learning is one of fundamental techniques in in...
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Learning SelfImitating Diverse Policies
Deep reinforcement learning algorithms, including policy gradient method...
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Stein Variational Gradient Descent Without Gradient
Stein variational gradient decent (SVGD) has been shown to be a powerful...
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OffPolicy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
When learning from a batch of logged bandit feedback, the discrepancy be...
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On the Margin Theory of Feedforward Neural Networks
Past works have shown that, somewhat surprisingly, overparametrization ...
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Stein Variational Gradient Descent as Moment Matching
Stein variational gradient descent (SVGD) is a nonparametric inference ...
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Variational Inference with Tailadaptive fDivergence
Variational inference with αdivergences has been widely used in modern ...
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Beetle Swarm Optimization Algorithm:Theory and Application
In this paper, a new metaheuristic algorithm, called beetle swarm optim...
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Breaking the Curse of Horizon: InfiniteHorizon OffPolicy Estimation
We consider the offpolicy estimation problem of estimating the expected...
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Qiang Liu
verfied profile
Assistant Professor at Department of Computer Science, The University of Texas at Austin