
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Federated learning (FL) allows edge devices to collectively learn a mode...
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MetaStyleSpeech : MultiSpeaker Adaptive TexttoSpeech Generation
With rapid progress in neural texttospeech (TTS) models, personalized ...
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Online Coreset Selection for Rehearsalbased Continual Learning
A dataset is a shred of crucial evidence to describe a task. However, ea...
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RetCL: A Selectionbased Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synt...
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MutuallyConstrained Monotonic Multihead Attention for Online ASR
Despite the feature of realtime decoding, Monotonic Multihead Attention...
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ModelAugmented Qlearning
In recent years, Qlearning has become indispensable for modelfree rein...
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Attribution Preservation in Network Compression for Reliable Network Interpretation
Neural networks embedded in safetysensitive applications such as selfd...
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Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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Neural Complexity Measures
While various complexity measures for diverse model classes have been pr...
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Fewshot Visual Reasoning with Metaanalogical Contrastive Learning
While humans can solve a visual puzzle that requires logical reasoning b...
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TimeReversal Symmetric ODE Network
Timereversal symmetry, which requires that the dynamics of a system sho...
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Distribution Aligning Refinery of Pseudolabel for Imbalanced Semisupervised Learning
While semisupervised learning (SSL) has proven to be a promising way fo...
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A General Family of Stochastic Proximal Gradient Methods for Deep Learning
We study the training of regularized neural networks where the regulariz...
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Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experi...
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A Revision of Neural Tangent Kernelbased Approaches for Neural Networks
Recent theoretical works based on the neural tangent kernel (NTK) have s...
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Compressed Sensing via MeasurementConditional Generative Models
A pretrained generator has been frequently adopted in compressed sensin...
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Stochastic Subset Selection
Current machine learning algorithms are designed to work with huge volum...
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Clinical Risk Prediction with Temporal Probabilistic Asymmetric MultiTask Learning
Although recent multitask learning methods have shown to be effective i...
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Rapid Structural Pruning of Neural Networks with Setbased TaskAdaptive MetaPruning
As deep neural networks are growing in size and being increasingly deplo...
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Federated SemiSupervised Learning with InterClient Consistency
While existing federated learning approaches mostly require that clients...
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Costeffective Interactive Attention Learning with Neural Attention Processes
We propose a novel interactive learning framework which we refer to as I...
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Federated Continual Learning with Adaptive Parameter Communication
There has been a surge of interest in continual learning and federated l...
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SemiRelaxed Quantization with DropBits: Training LowBit Neural Networks via Bitwise Regularization
Neural Network quantization, which aims to reduce bitlengths of the net...
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Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck
Estimating individual level treatment effects (ITE) from observational d...
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Sparsity Normalization: Stabilizing the Expected Outputs of Deep Networks
The learning of deep models, in which a numerous of parameters are super...
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Learning to Balance: Bayesian MetaLearning for Imbalanced and Outofdistribution Tasks
While tasks could come with varying number of instances in realistic set...
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Meta Dropout: Learning to Perturb Features for Generalization
A machine learning model that generalizes well should obtain low errors ...
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Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Adaptive gradient approaches that automatically adjust the learning rate...
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Spectral Approximate Inference
Given a graphical model (GM), computing its partition function is the mo...
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ORACLE: Order Robust Adaptive Continual LEarning
The order of the tasks a continual learning model encounters may have la...
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Mixed Effect Composite RNNGP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
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Adaptive Network Sparsification via Dependent Variational BetaBernoulli Dropout
While variational dropout approaches have been shown to be effective for...
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UncertaintyAware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
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Mestimation with the Trimmed l1 Penalty
We study highdimensional Mestimators with the trimmed ℓ_1 penalty. Whi...
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DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
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Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output var...
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Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification
We consider the problem of active feature acquisition, where we sequenti...
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Deep Asymmetric Multitask Feature Learning
We propose Deep Asymmetric Multitask Feature Learning (DeepAMTFL) which...
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Learning task structure via sparsity grouped multitask learning
Sparse mapping has been a key methodology in many highdimensional scien...
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Sequential Local Learning for Latent Graphical Models
Learning parameters of latent graphical models (GM) is inherently much h...
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A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
The Poisson distribution has been widely studied and used for modeling u...
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A General Family of Trimmed Estimators for Robust Highdimensional Data Analysis
We consider the problem of robustifying highdimensional structured esti...
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Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Gaussian Graphical Models (GGMs) are popular tools for studying network ...
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A General Framework for Mixed Graphical Models
"Mixed Data" comprising a large number of heterogeneous variables (e.g. ...
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On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of ...
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Eunho Yang
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Assistant Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST)