
Unsupervised Selftraining Algorithm Based on Deep Learning for Optical Aerial Images Change Detection
Optical aerial images change detection is an important task in earth obs...
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SYMPAIS: SYMbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Probabilistic software analysis aims at quantifying the probability of a...
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Bayesian Policy Search for Stochastic Domains
AI planning can be cast as inference in probabilistic models, and probab...
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Probabilistic Programs with Stochastic Conditioning
We propose to distinguish between deterministic conditioning, that is, c...
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NearOptimal MNL Bandits Under Risk Criteria
We study MNL bandits, which is a variant of the traditional multiarmed ...
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Efficient Competitive SelfPlay Policy Optimization
Reinforcement learning from selfplay has recently reported many success...
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Generating Adjacency Matrix for VideoQuery based Video Moment Retrieval
In this paper, we continue our work on VideoQuery based Video Moment re...
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Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Understanding how certain brain regions relate to a specific neurologica...
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Graph Neural Network for VideoQuery based Video Moment Retrieval
In this paper, we focus on Video Query based Video Moment Retrieval (VQ...
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Multinomial Logit Bandit with Low Switching Cost
We study multinomial logit bandit with limited adaptivity, where the alg...
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Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Motivated by practical needs such as largescale learning, we study the ...
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ModelFree Reinforcement Learning: from Clipped PseudoRegret to Sample Complexity
In this paper we consider the problem of learning an ϵoptimal policy fo...
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Bidirection Context Propagation Network for Realtime Semantic Segmentation
Spatial details and context correlations are two types of critical infor...
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Adaptive DoubleExploration Tradeoff for Outlier Detection
We study a variant of the thresholding bandit problem (TBP) in the conte...
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MultiIF : An Approach to Anomaly Detection in SelfDriving Systems
Autonomous driving vehicles (ADVs) are implemented with rich software fu...
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Almost Optimal ModelFree Reinforcement Learning via ReferenceAdvantage Decomposition
We study the reinforcement learning problem in the setting of finitehor...
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Collaborative Top Distribution Identifications with Limited Interaction
We consider the following problem in this paper: given a set of n distri...
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Guardauto: A Decentralized Runtime Protection System for Autonomous Driving
Due to the broad attack surface and the lack of runtime protection, pote...
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Stochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete ...
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SoftRootSign Activation Function
The choice of activation function in deep networks has a significant eff...
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Anypath Routing Protocol Design via QLearning for Underwater Sensor Networks
As a promising technology in the Internet of Underwater Things, underwat...
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Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement
Owing to refraction, absorption, and scattering of light by suspended pa...
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Exploiting Operation Importance for Differentiable Neural Architecture Search
Recently, differentiable neural architecture search methods significantl...
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Furnishing Your Room by What You See: An EndtoEnd Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
Understanding interior scenes has attracted enormous interest in compute...
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Subcarrier Assignment Schemes Based on QLearning in Wideband Cognitive Radio Networks
Subcarrier assignment is of crucial importance in wideband cognitive rad...
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CrossScale Residual Network for Multiple Tasks:Image Superresolution, Denoising, and Deblocking
In general, image restoration involves mapping from low quality images t...
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Temporal Action Localization using Long ShortTerm Dependency
Temporal action localization in untrimmed videos is an important but dif...
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Comb Convolution for Efficient Convolutional Architecture
Convolutional neural networks (CNNs) are inherently suffering from massi...
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Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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√(n)Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
In this paper, we consider the problem of online learning of Markov deci...
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Dualreference Age Synthesis
Age synthesis has received much attention in recent years. Stateofthe...
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Graph Neural Network for Interpreting TaskfMRI Biomarkers
Finding the biomarkers associated with ASD is helpful for understanding ...
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Exploration via Hindsight Goal Generation
Goaloriented reinforcement learning has recently been a practical frame...
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HGC: Hierarchical Group Convolution for Highly Efficient Neural Network
Group convolution works well with many deep convolutional neural network...
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Thresholding Bandit with Optimal Aggregate Regret
We consider the thresholding bandit problem, whose goal is to find arms ...
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Tight Regret Bounds for Infinitearmed Linear Contextual Bandits
Linear contextual bandit is a class of sequential decision making proble...
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Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in MultiArmed Bandits
Best arm identification (or, pure exploration) in multiarmed bandits is...
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Nearly MinimaxOptimal Regret for Linearly Parameterized Bandits
We study the linear contextual bandit problem with finite action sets. W...
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LFPPL: A LowLevel First Order Probabilistic Programming Language for NonDifferentiable Models
We develop a new Lowlevel, Firstorder Probabilistic Programming Langua...
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On Asymptotically Tight Tail Bounds for Sums of Geometric and Exponential Random Variables
In this note we prove bounds on the upper and lower probability tails of...
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Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Discovering imaging biomarkers for autism spectrum disorder (ASD) is cri...
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On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
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Dynamic Assortment Optimization with Changing Contextual Information
In this paper, we study the dynamic assortment optimization problem unde...
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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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Dynamic Assortment Selection under the Nested Logit Models
We study a stylized dynamic assortment planning problem during a selling...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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Optimal Design of Process Flexibility for General Production Systems
Process flexibility is widely adopted as an effective strategy for respo...
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Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
We study the collaborative PAC learning problem recently proposed in Blu...
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NearOptimal Policies for Dynamic Multinomial Logit Assortment Selection Models
In this paper we consider the dynamic assortment selection problem under...
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Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
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Yuan Zhou
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