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Found 18 papers, 12 papers with code
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.
Curriculum Offline Imitating Learning
However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.
Towards Return Parity in Markov Decision Processes
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
Curriculum Offline Imitation Learning
However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts
We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Improving Knowledge Tracing via Pre-training Question Embeddings
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.
Ranked #3 on
Knowledge Tracing
on EdNet
Model-based Policy Optimization with Unsupervised Model Adaptation
However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance.
GIKT: A Graph-based Interaction Model for Knowledge Tracing
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Ranked #7 on
Knowledge Tracing
on EdNet
Bidirectional Model-based Policy Optimization
Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate.
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Medical Named Entity Recognition
named-entity-recognition
+3
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).
