
Stochastic Online Learning with Probabilistic Graph Feedback
We consider a problem of stochastic online learning with general probabi...
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Combinatorial SemiBandit in the NonStationary Environment
In this paper, we investigate the nonstationary combinatorial semiband...
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Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders
The success of enhanced sampling molecular simulations that accelerate a...
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Improved Algorithm on Online Clustering of Bandits
We generalize the setting of online clustering of bandits by allowing no...
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PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs
Deep generative models are proven to be a useful tool for automatic desi...
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DataCentric MixedVariable Bayesian Optimization For Materials Design
Materials design can be cast as an optimization problem with the goal of...
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Learning Deep Correspondence through Prior and Posterior Feature Constancy
Stereo matching algorithms usually consist of four steps, including matc...
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G2LNet: Global to Local Network for Realtime 6D Pose Estimation with Embedding Vector Features
In this paper, we propose a novel realtime 6D object pose estimation fr...
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Delay Optimal Scheduling for Energy Harvesting Based Communications
Green communication attracts increasing research interest recently. Equi...
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A Gametheoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
Sponsored search is an important monetization channel for search engines...
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Generalization Analysis for GameTheoretic Machine Learning
For Internet applications like sponsored search, cautions need to be tak...
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Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engine...
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Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
When using stochastic gradient descent to solve largescale machine lear...
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EnsembleCompression: A New Method for Parallel Training of Deep Neural Networks
Parallelization framework has become a necessity to speed up the trainin...
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Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
Many engineering problems require identifying feasible domains under imp...
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Dual Supervised Learning
Many supervised learning tasks are emerged in dual forms, e.g., English...
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DIMMSC: A Dirichlet mixture model for clustering dropletbased single cell transcriptomic data
Motivation: Single cell transcriptome sequencing (scRNASeq) has become ...
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Improving Regret Bounds for Combinatorial SemiBandits with Probabilistically Triggered Arms and Its Applications
We study combinatorial multiarmed bandit with probabilistically trigger...
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Combinatorial MultiArmed Bandit with General Reward Functions
In this paper, we study the stochastic combinatorial multiarmed bandit ...
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Generalization Error Bounds for Optimization Algorithms via Stability
Many machine learning tasks can be formulated as Regularized Empirical R...
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A Theoretical Analysis of NDCG Type Ranking Measures
A central problem in ranking is to design a ranking measure for evaluati...
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Compositional Structure Learning for Action Understanding
The focus of the action understanding literature has predominately been ...
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Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
This paper proposes an approach for applying GANs to NMT. We build a con...
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Improving Brain Storm Optimization Algorithm via Simplex Search
Through modeling human's brainstorming process, the brain storm optimiza...
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Molecular enhanced sampling with autoencoders: Onthefly collective variable discovery and accelerated free energy landscape exploration
Macromolecular and biomolecular folding landscapes typically contain hig...
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Optimizing Neural Networks in the Equivalent Class Space
It has been widely observed that many activation functions and pooling m...
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Crowd Simulation Model Integrating "PhysiologyPsychologyPhysics" Factors
Crowd simulation is a daunting task due to the lack of a comprehensive a...
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Data offloading in mobile edge computing: A coalitional game based pricing approach
Mobile edge computing (MEC), affords service to the vicinity of mobile d...
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Train Feedfoward Neural Network with Layerwise Adaptive Rate via Approximating Backmatching Propagation
Stochastic gradient descent (SGD) has achieved great success in training...
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Thompson Sampling for Combinatorial SemiBandits
We study the application of the Thompson Sampling (TS) methodology to th...
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Scalable Influence Maximization with General Marketing Strategies
In this paper, we study scalable algorithms for influence maximization w...
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Caching with Time Domain Buffer Sharing
In this paper, storage efficient caching based on time domain buffer sha...
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Unsupervised Neural Machine Translation with Weight Sharing
Unsupervised neural machine translation (NMT) is a recently proposed app...
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Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (Extended Version)
We study the Combinatorial Pure Exploration problem with Continuous and ...
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Differential Equations for Modeling Asynchronous Algorithms
Asynchronous stochastic gradient descent (ASGD) is a popular parallel op...
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Machine LearningAssisted Least Loaded Routing to Improve Performance of CircuitSwitched Networks
The Least Loaded (LL) routing algorithm has been in recent decades the r...
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A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
Computer simulations often involve both qualitative and numerical inputs...
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Towards BinaryValued Gates for Robust LSTM Training
Long ShortTerm Memory (LSTM) is one of the most widely used recurrent s...
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Higher order monotonicity and submodularity of influence in social networks: from local to global
Kempe, Kleinberg and Tardos (KKT) proposed the following conjecture abou...
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Improving Aviation Safety using Synthetic Vision System integrated with Eyetracking Devices
By collecting the data of eyeball movement of pilots, it is possible to ...
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CubeP Crowds: crowd simulation integrated into "PhysiologyPsychologyPhysics" factors
In this paper, we present a novel CubeP model for crowd simulation that ...
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SupportNet: solving catastrophic forgetting in class incremental learning with support data
A plain welltrained deep learning model often does not have the ability...
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Exploring the Design Space of Immersive Urban Analytics
Recent years have witnessed the rapid development and wide adoption of i...
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Joint QueueAware and ChannelAware Delay Optimal Scheduling of Arbitrarily Bursty Traffic over MultiState TimeVarying Channels
This paper is motivated by the observation that the average queueing del...
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A unified crowd simulation model revealing relationships among "PhysiologyPsychologyPhysics" factors
We present a unified model for crowd simulation, CubeP, which comprehens...
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An Issue in the Martingale Analysis of the Influence Maximization Algorithm IMM
This paper explains a subtle issue in the martingale analysis of the IMM...
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Capacity Control of ReLU Neural Networks by Basispath Norm
Recently, path norm was proposed as a new capacity measure for neural ne...
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Capturing Complementarity in Set Functions by Going Beyond Submodularity/Subadditivity
We introduce two new "degree of complementarity" measures, which we refe...
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Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
In reinforcement learning (RL) , one of the key components is policy eva...
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Extending Recurrent Neural Aligner for Streaming EndtoEnd Speech Recognition in Mandarin
Endtoend models have been showing superiority in Automatic Speech Reco...
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Wei Chen
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Senior Researcher at Microsoft Research Asia, Adjunct Professor in the Institute of Interdisciplinary Information Sciences, Tsinghua University, Adjunct Researcher in the Institute of Computing Technology, Chinese Academy of Sciences, Ph.D degree from the Department of Computer Science, Cornell University, Scientist at Oracle Corporation 2004.