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06/08/2022
What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective
Algorithmic fairness has attracted increasing attention in the machine l...
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06/03/2022
Offline Reinforcement Learning with Causal Structured World Models
Model-based methods have recently shown promising for offline reinforcem...
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05/27/2022
Counterfactual Fairness with Partially Known Causal Graph
Fair machine learning aims to avoid treating individuals or sub-populati...
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05/27/2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
State-of-the-art causal discovery methods usually assume that the observ...
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05/19/2022
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Calibration is defined as the ratio of the average predicted click rate ...
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05/18/2022
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Cross-domain sentiment classification (CDSC) aims to use the transferabl...
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04/26/2022
A Review-aware Graph Contrastive Learning Framework for Recommendation
Most modern recommender systems predict users preferences with two compo...
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03/30/2022
Factored Adaptation for Non-Stationary Reinforcement Learning
Dealing with non-stationarity in environments (i.e., transition dynamics...
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03/30/2022
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towar...
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03/29/2022
Sample Recycling for Nested Simulation with Application in Portfolio Risk Measurement
Nested simulation is a natural approach to tackle nested estimation prob...
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02/24/2022
Attainability and Optimality: The Equalized Odds Fairness Revisited
Fairness of machine learning algorithms has been of increasing interest....
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02/11/2022
Conditional Contrastive Learning with Kernel
Conditional contrastive learning frameworks consider the conditional sam...
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02/10/2022
Learning Latent Causal Dynamics
One critical challenge of time-series modeling is how to learn and quick...
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01/14/2022
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
Many of the causal discovery methods rely on the faithfulness assumption...
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12/26/2021
Perlin Noise Improve Adversarial Robustness
Adversarial examples are some special input that can perturb the output ...
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12/13/2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has drive...
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11/05/2021
Transferable Time-Series Forecasting under Causal Conditional Shift
This paper focuses on the problem of semi-supervised domain adaptation f...
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10/18/2021
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Traditionally, Bayesian network structure learning is often carried out ...
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10/12/2021
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional a...
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10/11/2021
Learning Temporally Causal Latent Processes from General Temporal Data
Our goal is to recover time-delayed latent causal variables and identify...
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09/24/2021
Unaligned Image-to-Image Translation by Learning to Reweight
Unsupervised image-to-image translation aims at learning the mapping fro...
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09/10/2021
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy
The PC algorithm is the state-of-the-art algorithm for causal structure ...
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09/07/2021
Instance-dependent Label-noise Learning under a Structural Causal Model
Label noise will degenerate the performance of deep learning algorithms ...
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08/27/2021
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Text-to-image synthesis refers to generating an image from a given text ...
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08/24/2021
Outdoor Position Recovery from HeterogeneousTelco Cellular Data
Recent years have witnessed unprecedented amounts of data generated by t...
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08/06/2021
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
Sentence semantic matching requires an agent to determine the semantic r...
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07/15/2021
Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images
Domain shift is a problem commonly encountered when developing automated...
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07/06/2021
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Most approaches in reinforcement learning (RL) are data-hungry and speci...
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06/23/2021
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting
Background noise and scale variation are common problems that have been ...
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06/14/2021
Graph Domain Adaptation: A Generative View
Recent years have witnessed tremendous interest in deep learning on grap...
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06/11/2021
Adversarial Robustness through the Lens of Causality
The adversarial vulnerability of deep neural networks has attracted sign...
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06/09/2021
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching
Sentence semantic matching requires an agent to determine the semantic r...
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06/05/2021
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
Self-supervised learning is a form of unsupervised learning that leverag...
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06/01/2021
Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction
Predicting student performance is a fundamental task in Intelligent Tuto...
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05/31/2021
Privileged Graph Distillation for Cold Start Recommendation
The cold start problem in recommender systems is a long-standing challen...
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05/19/2021
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
Click-through rate (CTR) prediction is one of the most central tasks in ...
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05/16/2021
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
As users often express their preferences with binary behavior data (impl...
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04/27/2021
A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
Influenced by the stunning success of deep learning in computer vision a...
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03/26/2021
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
We consider the problem of estimating a particular type of linear non-Ga...
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02/28/2021
RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials
The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid...
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01/20/2021
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Objective: Multi-modal functional magnetic resonance imaging (fMRI) can ...
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12/22/2020
Learning Disentangled Semantic Representation for Domain Adaptation
Domain adaptation is an important but challenging task. Most of the exis...
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12/16/2020
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Reinforcement learning (RL) algorithms usually require a substantial amo...
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12/16/2020
R^2-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural la...
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12/13/2020
Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation
The target of 2D human pose estimation is to locate the keypoints of bod...
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10/21/2020
How Do Fair Decisions Fare in Long-term Qualification?
Although many fairness criteria have been proposed for decision making, ...
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10/10/2020
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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08/16/2020
Attack on Multi-Node Attention for Object Detection
This paper focuses on high-transferable adversarial attacks on detection...
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07/17/2020
Adaptive Task Sampling for Meta-Learning
Meta-learning methods have been extensively studied and applied in compu...
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06/17/2020
