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08/22/2023
Convergence guarantee for consistency models
We provide the first convergence guarantees for the Consistency Models (...
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08/09/2023
Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets
Bayesian optimization (BO) is widely adopted in black-box optimization p...
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01/30/2023
Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization
We proposed a new technique to accelerate sampling methods for solving d...
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12/29/2022
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
The mobile communication enabled by cellular networks is the one of the ...
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06/17/2022
Reframed GES with a Neural Conditional Dependence Measure
In a nonparametric setting, the causal structure is often identifiable o...
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03/22/2022
Out-of-distribution Generalization with Causal Invariant Transformations
In real-world applications, it is important and desirable to learn a mod...
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02/07/2022
Universality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various ...
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12/23/2021
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models fo...
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11/30/2021
gCastle: A Python Toolbox for Causal Discovery
is an end-to-end Python toolbox for causal structure learning. It provi...
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06/07/2021
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method based on deep metric learning to perform Bayesian ...
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06/02/2021
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Domain generalization aims to learn knowledge invariant across different...
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05/14/2021
Ordering-Based Causal Discovery with Reinforcement Learning
It is a long-standing question to discover causal relations among a set ...
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12/28/2020
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
The capability of imagining internally with a mental model of the world ...
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07/02/2020
Decoder-free Robustness Disentanglement without (Additional) Supervision
Adversarial Training (AT) is proposed to alleviate the adversarial vulne...
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06/10/2020
Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several important advances in recent years, learning causal stru...
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06/08/2020
Clustering Causal Additive Noise Models
Additive noise models are commonly used to infer the causal direction fo...
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04/18/2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Learning disentanglement aims at finding a low dimensional representatio...
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11/18/2019
A Graph Autoencoder Approach to Causal Structure Learning
Causal structure learning has been a challenging task in the past decade...
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10/18/2019
Masked Gradient-Based Causal Structure Learning
Learning causal graphical models based on directed acyclic graphs is an ...
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09/02/2019
Causal Discovery by Kernel Intrinsic Invariance Measure
Reasoning based on causality, instead of association has been considered...
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08/27/2019
Asymptotically Optimal One- and Two-Sample Testing with Kernels
We characterize the asymptotic performance of nonparametric one- and two...
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07/25/2019
Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple s...
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06/11/2019
Causal Discovery with Reinforcement Learning
Discovering causal structure among a set of variables is a fundamental p...
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11/27/2018
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
Cellular network configuration plays a critical role in network performa...
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09/23/2018
Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed vari...
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09/23/2018
A Kernel Embedding-based Approach for Nonstationary Causal Model Inference
Although nonstationary data are more common in the real world, most exis...
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06/05/2018
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Exogenous state variables and rewards can slow down reinforcement learni...
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02/23/2018
Exponentially Consistent Kernel Two-Sample Tests
Given two sets of independent samples from unknown distributions P and Q...
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02/21/2018
