
Repulsive Attention: Rethinking Multihead Attention as Bayesian Inference
The neural attention mechanism plays an important role in many natural l...
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Unsupervised Abstractive Dialogue Summarization for TeteaTetes
Highquality dialoguesummary paired data is expensive to produce and do...
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Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems
We propose a novel framework for structured bandits, which we call an in...
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When does MAML Work the Best? An Empirical Study on ModelAgnostic MetaLearning in NLP Applications
ModelAgnostic MetaLearning (MAML), a modelagnostic metalearning meth...
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Reward Constrained Interactive Recommendation with Natural Language Feedback
Textbased interactive recommendation provides richer user feedback and ...
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Improving Adversarial Text Generation by Modeling the Distant Future
Autoregressive text generation models usually focus on local fluency, a...
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Security Analysis of EOSIO Smart Contracts
The EOSIO blockchain, one of the representative Delegated ProofofStake...
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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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NestedWasserstein SelfImitation Learning for Sequence Generation
Reinforcement learning (RL) has been widely studied for improving sequen...
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Learning Diverse Stochastic HumanAction Generators by Learning Smooth Latent Transitions
Humanmotion generation is a longstanding challenging task due to the r...
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Collaborative Filtering with A Synthetic Feedback Loop
We propose a novel learning framework for recommendation systems, assist...
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Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges o...
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Figure Captioning with Reasoning and SequenceLevel Training
Figures, such as bar charts, pie charts, and line plots, are widely used...
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TopicGuided Variational Autoencoders for Text Generation
We propose a topicguided variational autoencoder (TGVAE) model for text...
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Scalable Thompson Sampling via Optimal Transport
Thompson sampling (TS) is a class of algorithms for sequential decision...
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Improving SequencetoSequence Learning via Optimal Transport
Sequencetosequence models are commonly trained via maximum likelihood ...
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Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
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Stochastic ParticleOptimization Sampling and the NonAsymptotic Convergence Theory
Particleoptimization sampling (POS) is a recently developed technique t...
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Policy Optimization as Wasserstein Gradient Flows
Policy optimization is a core component of reinforcement learning (RL), ...
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Accelerated Firstorder Methods on the Wasserstein Space for Bayesian Inference
We consider doing Bayesian inference by minimizing the KL divergence on ...
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A Unified ParticleOptimization Framework for Scalable Bayesian Sampling
There has been recent interest in developing scalable Bayesian sampling ...
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Learning Structural Weight Uncertainty for Sequential DecisionMaking
Learning probability distributions on the weights of neural networks (NN...
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Particle Optimization in Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has been increasi...
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Ruiyi Zhang
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