Lab. Seminar
- Fairness Aware Counterfactuals for Subgroups (NeurIPS 2023)
박세현
- FairFed: Enabling Group Fairness in Federated Learning (AAAI 2023)
정휘창
- A Fair Generative Model Using LeCam Divergence (AAAI 2023)
이경선
- Achieving Counterfactual Fairness for Anomaly Detection PAKDD 2023
이지후
- Fair Representation Learning for Recommendation: A Mutual Information Perspective (AAAI 2023)
- SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification (AAAI 2023)
- Loss Balancing for Fair Supervised Learning (ICML 2023)
박세현
- Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks (ICML 2023)
정휘창
- Fair Neighbor Embedding (ICML 2023)
정한교
- On the Within-Group Fairness of Screening Classifiers (ICML 2023)
박진원
- Balanced Fair K-Means Clustering (IEEE 2023)
- ICLR 2023 FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
정휘창
- ICML 2023 Differential Privacy has Bounded Impact on Fairness in Classification
김초은
- Adapting fairness interventions to missing values
박진원
- ICML 2023 Approximation Algorithms for Fair Range Clustering
- TabMT: Generating tabular data with masked transformers
이지후
- Flat Seeking Bayesian Neural Networks
김초은
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
박찬무
- Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
신윤섭
- Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
박유하
- Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
박진원
- Students Parrot Their Teachers: Membership Inference on Model Distillation
박석훈
- Feature learning for interpretable , performant decisition treess
정휘창
- Causal normalizing flows: from theory to practice
최성식
- A Measure-Theoretic Axiomatisation of Causality
정한교
- Are Emergent Abilities of Large Language Models a Mirage?
박세현
- Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
공인성
- A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
김건웅
- FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-trained Models in Few-Shot Learning
- Dividing and Conquering a BlackBox to a Mixture of Interpretable Models
- Label free explainability for unsupervised model
박찬무
- Explainability as statistical inference
- Learning_Perturbation
김건웅
- Identifying Interpretable Subspaces in Image
- Visual Classification via Description from
박석훈
- Interpretable GAN
- ProtoVAE
최성식
- Inducing Causal Structure for Interpretable Neural Networks
- Neural Basis Models for Interpretability
신윤섭
- Natural Posterior Network Deep Bayesian Uncertainty for Exponential Family Distribution
- Towards Trustworthy Explanation On Causal Rationalization
- Fair Bayes-Optimal classifiers under predictive parity
정연호
- Counterfactual Fairness with Partially Known Causal Graph
양동윤
- On the Tradeoff Between Robustness and Fairness
정휘창
- Fair and Efficient Allocations Without Obvious Manipulations
최성식
- Fairness without Demographics through Knowledge Distillation
김건웅
- Domain Adaptation meets Individual Fairness. And they get along
- Optimal Transport of Classifiers to Fairness
박진원
- Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
이지후
- Fairness Reprogramming
박유하
- Fair Ranking with Noisy Protected Attributes
박세현
- Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
박찬무
- Conformalized Fairness via Quantile Regression
이종진
- Self-Supervised Fair Representation Learning without Demographics
김성현
- Fairness Transferability Subject to Bounded Distribution Shift
이경선
- Pushing the limits of fairness impossibility: Who's the fairest of them all?
- Benign overfitting in linear regression
정휘창
- Benign overfitting in ridge regression
김건웅
- Just interpolate: Kernel “ridgeless” regression can generalize
박진원
- On uniform convergence and low-norm interpolation
learning
최성식
- Exact gap between generalization error and uniform convergence in random feature models
이종진
- Uniform convergence of interpolators: Gaussian width, norm bounds, and benign overfitting
공인성
- In defense of uniform convergence: Generalization via derandomization with an application to interpolating predictors
박석훈
- Minimum ℓ1-norm interpolators: Precise asymptotics and multiple descent
이지후
- A random matrix analysis of random fourier features: beyond the gaussian kernel, a precise phase transition, and the corresponding double descent
양동윤
- Generalization of two-layer neural networks: An asymptotic viewpoint
-Some Methods for Estimating CATE
-Approximate function with DNN
-Causal paper review
-Review of Sparsity
-Review of Fair Clustering Papers
-Implicit regularization in deep matrix factorization
박진원
-Kernel and rich regimes in overparametrized models
이지후
-Implicit bias of gradient descent for wide two-layer neural networks Trained with the Logistic Loss
박유하
-The implicit bias of depth, How incremental learning drives generalization
이종진
-A unifying view on implicit bias in training linear neural networks
-Implicit regularization in matrix factorization
김건웅
-Characterizing Implicit Bias in Terms of Optimization Geometry
최성식
-Implicit Bias of Gradient Descent on Linear Convolutional Networks
양동윤
-Gradient descent aligns the layers of deep linear networks
공인성
-The Implicit Bias of Gradient Descent on Separable Data
-Ranking problem
-Class activation map
이종진
-Neural network pruning
-Unconstrained convex optimization through first-order approximation methods
-Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 4, 5)
-Individual Fairness AI Reviews
-Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 3.2, 3.4)
김건웅
-Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 3.3, 3.5, and 3.6)
-Practical Secure Aggregation for Privacy-Preserving Machine
Learning
정휘창
-Abstract Algebra
-Constrained Fairness AI Reviews
최용찬
-Counterfactual Fairness
-Introduction to Hamiltonian Monte Carlo methods
-Review of DeLiGAN:Generative Adversarial Networks for Diversed and Limited Data
최용찬
-VEEGAN : Reducing Mode Collapse in GANs using Implicit Variational Learning
이종진
-Bayesian GAN
김건웅
-Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images
공인성
-Mixture of GANs for Clustering
김성현
-On GANs and GMMs
정휘창
-Mixture Density Generative Adversarial Networks
백규승
-PROBGAN: Towards probabilistic GAN with theoretical guarantees
예성주
-Variational Bayesian GAN
최용찬
-Label-removed generative adversarial networks incorporationg with K-Means
최성식
-A Style-Based Generator Architecture for Generative Adversarial Networks
-Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
백규승
-Learning Sparse DNN Architecture
-Adam, AMSGrad, AdamX
-Sparse VAE
백규승
-Generalization error bound - via Compressing Deep Neural Network
최용찬
-Semi-supervised learning with MixUp method - ICT
김사라
-Data Augmentation Reviews
-Approximation and Estimation for High-Dimensional Deep Learning Networks
-Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks
-Spectrally-normalized Margin Bounds for Neural Networks
백규승
-On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
-Size-Independent Sample Complexity of Neural Networks
-A Survey of Algorithms and Analysis for Adaptive Online Learning
-A generalized online mirror descent with applications to classification and regression
김사라
-Adaptive Bound Optimization for Online Convex Optimization
백규승
-Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
-Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
서지인
-AdaGrad stepsizes : sharp convergence over nonconvex landscapes, from any initialization
-Bayesian clinical trials
김주은
-Bayesian clinical trials
-Bayesian predictive power for interim adaptation in seamless phase II/III trials where the endpoint is survival up to some
specied timepoint
공인성
-Use of Bayesian statistics in drug development: Advantages and challenges
-Bayesian design and analysis for clinical trials - a case study and a learner's perspective
이상엽
-BAYESIAN CLINICAL TRIALS: WHY BOTHER?
-Practical Bayesian Adaptive Randomisation in Clinical Trials
이종진
-A Bayesian adaptive design for clinical trials in rare diseases
-A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test
주여진
-A Bayesian sequential design with binary outcome
-Bayesian apative designs for clinical trials
-SGD 기반 최적화 기법들의 이론적 성질
최용찬
-Explainable AI paper
황재성
-The Solution Of Generalized Lasso For Non Full Rank Case
-Sparse variable clustering models
김성현
-Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes
indices
이종진
-Towards dynamic catchment modelling: a Bayesian Hierarchical mixtures of experts framework
-Exact Solution Path Algorithm Of Generalized Lasso For Non Full Rank Case
이상엽
-SGD 기반 최적화 기법들의 이론적 성질
김사라
-Outcome uncertainty and attendance demand in sport: the case of English soccer
백규승
-Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
-Attention-based LSTM for Aspect-level Sentiment Classification
김성현
-Attention Networks for Aspect-Level Sentiment Classification
이종진
-Hierarchical Attention Networks for Document Classification
-Semisupervised Autoencoder for Sentiment Analysis
-Causal Embeddings for Recommendation
김주은
-Generation Meets Recommendation: Proposing Novel Items for Groups of Users
백규승
-Origin of Native FTRL-Proximal
주여진
-Impact of Item Consumption on Assessment of Recommendations in User Studies
김호철
-Translation-based Factorization Machines for Sequential Recommendation
-Reviews of DeepLDA
-Unbiased Online Recommender Evaluation for Missing-Not-At-Random Implicit Feedback
김사라
-Adaptive Collaborative Topic Modeling for Online Recommendation
서지인
-Spectral Collaborative Filtering
유찬우
-Explore, exploit, and explain_ personalizing explainable recommendations with bandits
-HOP-rec_ high-order proximity for implicit recommendation
-Learning AND-OR Templates for Object Recognition and Detection
김주은
-Visualizing and Understanding Convolutional Networks
주여진
-"Why Should I Trust You?" Explaining the Predictions of Any Classifier
이종진
-Generating Visual Explanations
김성현
-Attentive Explanations: Justifying Decisions and Pointing to the Evidence
최용찬
-Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning models
김보영
-Interpretable classifiers using rules and bayesian Analysis: building a better stroke prediction model
-Human-level concept learning through probabilistic program induction
-Mirror descent, Hedge
이상엽
-Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization
20181006
-Minimax lower bounds I
20181013
-Minimax bounds II
20181201
-Minimax bounds III
20181208
-Minimax bounds IV
-Online Incremental Feature Learning with Denoising Autoencoders
이상엽
-Bayesian Incremental Learning for Deep Neural Networks
김사라
-Error-Driven Incremental Learning in Deep Convolutional Neural Network for Larg-Scale Image Classification
최용찬
-Evolutive deep models for online learning on data streams with no storage
백규승
-Incremental Training of Deep Convolutional Neural Networks
-Online Deep Learning: Learning Deep Neural Networks on the Fly
-Anomaly Detection: A Survey
이종진
-Outlier Detection for Temporal DATA: A Survey
이지수
-A survey of outlier detection
김사라
-Deep autoencoding gaussian mixture model for unsupervised anomaly detection
최용찬
-Semi-Supervised Anomaly Detection
-AD Click Prediction a View from the Trenches
유찬우
-DEEP NEURAL NETWORKS FOR YOUTUBE RECOMMENDATIONS
이지홍
-Related Pins at Pinterest: The Evolution of a Real-World Recommender System
정구환
-Online Learning to Rank in Stochastic Click Models
-Slice Samplers for DPM
-Hamiltonian Monte Carlo and Langevin Monte Carlo
-Factor Graphs, the Sum-Product Algorithm and TrueSkillTM
백규승
-Neural Network Learning: Theoretical Foundation
-Learning by Mirror Averaging
김동하
-Understanding deeply and improving VAT
-Generative Auto Encoder
-Click-through Prediction for Advertising in Twitter Timeline
-Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
최용찬
-CTR prediction models
-DeepDocClassifier: Document Classification
with Deep Convolutional Neural Network
김성현
-Distributed Representations of Sentences and
Documents
최용찬
-Document context language models
-Document embedding with Paragraph vectors
김사라
-Efficient Vector Representation for Documents through
Corruption
-Document Embeddings via Recurrent Language Models
김보영
-Hierarchical Recurrent Neural Network for Document
Modeling
김동하
-Document Embeddings
이종진
-Classify of Select: Neural Architectures for
Extractive Document Summarization
백규승
-From Word Embeddings To Document Distances
-Hierarchical Deep Learning for Text Classication
정구환
-Scheduled Sampling for Sequence Prediction with
Recurrent Neural Networks
-SeqGAN: Sequence Generative Adversarial Nets with
Policy Gradient
최세민
-Multi-Task Learning for Document Ranking and
Query Suggestion
-Hierarchical Multiscale Recurrent Neural Networks
-Factorization Machines, Field-aware Factorization Machines
유찬우
-Simple and Scalable Response Prediction for Display Advertising
김보영
-Web-Scale Bayesian Click-Through Rate Prediction
for Sponsored Search Advertising
in Microsoft's Bing Search Engine
-adPredictor and Message Passing
이종진
-Coupled Group Lasso for Web-Scale CTR
Prediction in Display Advertising
김사라
-Personalized click prediction in sponsored search
-Wide and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
김사라
-PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
서지인
-Hierarchical Attentive Recurrent Tracking(HART)
최세민
-Dilated Recurrent Neural Networks
온일상
-MaskRNN: Instance level video object segmentation
이종진
-Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
백규승
-Z-Forcing: Training Stochastic Recurrent Networks
김보영
-Gated Recurrent Convolution Neural Network for OCR
최용찬
-Learning Hierarchical Information Flow with Recurrent Neural Modules
김동하
-Cortical microcircuits as gated-recurrent neural networks
이종진(20171122)
-Multilayer Perceptron based Recommender System
김보영(20171122)
-Autoencoder based Recommendation System
서지인(20171128)
-Convolutional Neural Network based Recommender System
최용찬(20171226)
-Recurrent Neural Network based Recommender System
이지수(20171226)
-Learning deep structured semantic models for web search using clickthrough data
정구환(20180124)
-Restricted Boltzmann Machine based Recommender System
유찬우(20180124)
-NADE, GAN Recommender
-Introduction to GAN variants
-Capsule Networks
-Mixture models with a prior on the number of components
백규승
-On the Expressive Power of Deep Neural Networks
김사라
-Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
김보영
-Instacart 장바구니 자료 분석
-Recommendation with Non-Random Missing Data
-Deep Decision Network for Multi-Class Image Classification
최세민
-Generative Adversarial Nets
-Supervised learning exploiting clustering information
온일상
-Adaptive Face Verication
최세민
-Introduction to face verification
-Learnable pooling with context Gating for video classification
-Neural Machine Translation, Answer Sentence Selection
최세민
-Deep Learning Models for Information Retrieval
김보영
-Next Basket Recommendation
황참이
-Generalized Additive Model
전종준
-Primal path algorithm for compositional data analysis
-Google Cloud & YouTube-8M Video Understanding Challenge
*회의록
-20170519 회의록
-Google Cloud & YouTube-8M Video Understanding Challenge
백규승
-Exploratory data analysis of labels
-YouTube-8M:A Large-Scale Video Classification Benchmark
-YouTube-8M:A Large-Scale Video Classification
김성현
-Google Cloud & YouTube-8M Video Understanding Challenge
김보영
-Learning without forgetting
백규승
-Saliency Detection by Multi-Context Deep Learning
-Exploratory data analysis of labels
최세민
-Hierarchical Multi-Label Classification Using Local Neural Networks
최용찬
-ImageNet Classication with Deep Convolutional Neural Networks
-Glossary of terms
김성현
-Network Cohesion & Graph Partitioning
최용찬
-자료와 코드
-딥러닝에서의 준 지도 학습법에 관한 연구
최세민
-Personalized Regression for Recommender System
김보영
-Recommender System and Data Analysis
정구환
-토픽모형을 이용한 마이크로 세그멘테이션
온일상
-Sparse Bayesian factor analysis
-Autoencoder for Collaborative Filtering
-A non-IID Framework for Collaborative Filtering
with Restricted Boltzmann Machines
김동하
-Some discovery of deep learning
김성현
-DNN & visualization in R
온일상
-Quantifying lagged effects with a fused lasso penalty
-Sparse factor models with IBP prior
정구환
-Hamiltonian Monte Carlo
-Trueskill
김사라
-Self Consistency
최세민
-Sparse CCA via Precision Adjusted Iterative
Thresholding
조성현
-Comparison of Ridge Penalized Logistic Model and Ridge
Penalized Simple Linear model in Dichotomous response
백규승
-Wavelets and high order numerical differentiation
-Active Learning
김보영
-Content-Aware Point of Interest Recommendation on
Location-Based Social Networks
-Restricted Boltzmann Machines
for Collaborative Filtering
김사라
-Explaining Nonlinear Classification Decisions with
Deep Taylor Decomposition
정구환
-Expectation Propagation
-Undirected Topic Models
온일상
-Measuring Invariances in Deep Networks
권용찬
-3D Multiscale Residual U-Net Architecture for Brain
Lesion Segmentation
최세민
-Semi-supervised learning and clustering using deep
structure literature review
-Variants of Canonical Correlation Analysis
백규승
-Visualizing and Understanding Convolutional
Networks
김성현
-Visualizing Higher-Layer Features of a Deep
Network