17 citations · 33 across the 10 of their papers we have counts for
10 papers
Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models
Yean Hoon Ong, Paolo Barucca, Wei Pan +1
Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-experts GP models (GP-pro), wh…
Specify Robust Causal Representation from Mixed Observations
Mengyue Yang, Xinyu Cai, Furui Liu +2
Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hyp…
Generalizable Information Theoretic Causal Representation
Mengyue Yang, Xinyu Cai, Furui Liu +4
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommende…
Debiased Recommendation with User Feature Balancing
Mengyue Yang, Guohao Cai, Furui Liu +5
Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS…
Learning State Representations via Retracing in Reinforcement Learning
Changmin Yu, Dong Li, Jianye Hao +2
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In a…
Top-N Recommendation with Counterfactual User Preference Simulation
Mengyue Yang, Quanyu Dai, Zhenhua Dong +3
Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. Traditional models usually motivate th…
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Xidong Feng, Chen Chen, Dong Li +3
Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, ca…
Ordering-Based Causal Discovery with Reinforcement Learning
Xiaoqiang Wang, Yali Du, Shengyu Zhu +4
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising result…
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Minne Li, Mengyue Yang, Furui Liu +3
The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a…
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Mengyue Yang, Furui Liu, Zhitang Chen +3
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of…