1 code implementation • 17 Dec 2023 • Amirreza Kazemi, Martin Ester
Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates.
1 code implementation • 5 Oct 2023 • Tony Shen, Seonghwan Seo, Grayson Lee, Mohit Pandey, Jason R Smith, Artem Cherkasov, Woo Youn Kim, Martin Ester
Searching the vast chemical space for drug-like and synthesizable molecules with high binding affinity to a protein pocket is a challenging task in drug discovery.
1 code implementation • 10 Aug 2022 • Atia Hamidizadeh, Tony Shen, Martin Ester
We propose SeMole, a method that augments the Junction Tree Variational Autoencoders, a state-of-the-art generative model for molecular graphs, with semi-supervised learning.
1 code implementation • 15 Jul 2022 • Ali Arab, Dev Arora, Jialin Lu, Martin Ester
Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest.
1 code implementation • 15 Jun 2022 • Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
no code implementations • 19 May 2022 • Raquel Aoki, Martin Ester
Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
1 code implementation • 14 Dec 2021 • Raquel Aoki, Yizhou Chen, Martin Ester
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.
1 code implementation • 9 Nov 2021 • Zihan Wang, Jialin Lu, Oliver Snow, Martin Ester
Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency.
no code implementations • 1 Jan 2021 • Jialin Lu, Martin Ester
We present Neural Disjunctive Normal Form (Neural DNF), a hybrid neuro- symbolic classifier that vertically integrates propositional logic with a deep neural network.
no code implementations • 16 Nov 2020 • Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
no code implementations • 31 Oct 2020 • Shuman Peng, Weilian Song, Martin Ester
The goal of few-shot classification is to learn a model that can classify novel classes using only a few training examples.
1 code implementation • 26 Sep 2020 • Hossein Sharifi-Noghabi, Hossein Asghari, Nazanin Mehrasa, Martin Ester
To learn a domain-invariant representation, it also utilizes a novel alignment loss to ensure that the distance between pairs of class centroids, computed after adding the unlabeled samples, is preserved across different domains.
1 code implementation • 8 Jun 2020 • Xiang Li, Ben Kao, Caihua Shan, Dawei Yin, Martin Ester
We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.
1 code implementation • 17 Mar 2020 • Raquel Aoki, Martin Ester
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible.
3 code implementations • 14 Nov 2019 • Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on Graph Classification on PROTEINS
1 code implementation • 27 Oct 2019 • Jialin Lu, Martin Ester
However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels.
no code implementations • 25 Sep 2019 • Hossein Sharifi-Noghabi, Shuman Peng, Olga Zolotareva, Colin C. Collins, Martin Ester
To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Jialin Lu, Martin Ester
While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation.
2 code implementations • 25 Jul 2018 • Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, Martin Ester
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients.
no code implementations • 9 Sep 2016 • Vlad Sandulescu, Martin Ester
The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases.