
OffPolicy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family o...
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Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Structural equation models (SEMs) are widely used in sciences, ranging f...
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Unsupervised Landmark Learning from Unpaired Data
Recent attempts for unsupervised landmark learning leverage synthesized ...
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Scalable Deep Generative Modeling for Sparse Graphs
Learning graph generative models is a challenging task for deep learning...
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Video Representation Learning with Visual Tempo Consistency
Visual tempo, which describes how fast an action goes, has shown its pot...
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Novel Policy Seeking with Constrained Optimization
In this work, we address the problem of learning to seek novel policies ...
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Evolutionary Stochastic Policy Distillation
Solving the GoalConditioned Reward Sparse (GCRS) task is a challenging ...
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Temporal Pyramid Network for Action Recognition
Visual tempo characterizes the dynamics and the temporal scale of an act...
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SelfSupervised Scene Deocclusion
Natural scene understanding is a challenging task, particularly when enc...
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Learning Sparse Rewarded Tasks from SubOptimal Demonstrations
Modelfree deep reinforcement learning (RL) has demonstrated its superio...
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Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Learning a good image prior is a longterm goal for image restoration an...
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EnergyBased Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangea...
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Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of ...
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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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Differentiable Topk Operator with Optimal Transport
The topk operation, i.e., finding the k largest or smallest elements fr...
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Real or Not Real, that is the Question
While generative adversarial networks (GAN) have been widely adopted in ...
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Reinforcement Learning via FenchelRockafellar Duality
We review basic concepts of convex duality, focusing on the very general...
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Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
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AlgaeDICE: Policy Gradient from Arbitrary Experience
In many realworld applications of reinforcement learning (RL), interact...
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Overcoming Catastrophic Forgetting by Generative Regularization
In this paper, we propose a new method to overcome catastrophic forgetti...
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EnergyInspired Models: Learning with SamplerInduced Distributions
Energybased models (EBMs) are powerful probabilistic models, but suffer...
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Recursive Visual Sound Separation Using MinusPlus Net
Sounds provide rich semantics, complementary to visual data, for many ta...
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DualDICE: BehaviorAgnostic Estimation of Discounted Stationary Distribution Corrections
In many realworld reinforcement learning applications, access to the en...
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Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), wh...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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Feature Intertwiner for Object Detection
A welltrained model should classify objects with a unanimous score for ...
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Learning to Plan via Neural ExplorationExploitation Trees
Samplingbased algorithms such as RRT and its variants are powerful tool...
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Bayesian Metanetwork Architecture Learning
For deep neural networks, the particular structure often plays a vital r...
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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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Learning to Defense by Learning to Attack
Adversarial training provides a principled approach for training robust ...
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A Neural Compositional Paradigm for Image Captioning
Mainstream captioning models often follow a sequential structure to gene...
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Neural Network Encapsulation
A capsule is a collection of neurons which represents different variants...
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Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph...
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Rethinking the Form of Latent States in Image Captioning
RNNs and their variants have been widely adopted for image captioning. I...
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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
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Learning towards Minimum Hyperspherical Energy
Neural networks are a powerful class of nonlinear functions that can be ...
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Decoupled Networks
Inner productbased convolution has been a central component of convolut...
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SyntaxDirected Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
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Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
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Boosting the Actor with Dual Critic
This paper proposes a new actorcriticstyle algorithm called Dual Actor...
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Deep Hyperspherical Learning
Convolution as inner product has been the founding basis of convolutiona...
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Towards Blackbox Iterative Machine Teaching
In this paper, we make an important step towards the blackbox machine t...
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Contrastive Learning for Image Captioning
Image captioning, a popular topic in computer vision, has achieved subst...
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Iterative Machine Teaching
In this paper, we consider the problem of machine teaching, the inverse ...
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Detecting Visual Relationships with Deep Relational Networks
Relationships among objects play a crucial role in image understanding. ...
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Towards Diverse and Natural Image Descriptions via a Conditional GAN
Despite the substantial progress in recent years, the image captioning t...
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Stochastic Generative Hashing
Learningbased binary hashing has become a powerful paradigm for fast se...
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Learning from Conditional Distributions via Dual Embeddings
Many machine learning tasks, such as learning with invariance and policy...
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Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
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Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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Bo Dai
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Research Assistant, Ph.D. Candidate at Georgia Institute of Technology