1 code implementation • 30 Mar 2024 • Bohan Zhang, Yixin Wang, Paramveer S. Dhillon
A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality.
no code implementations • 28 Feb 2024 • Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van Den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
The field of deep generative modeling has grown rapidly and consistently over the years.
no code implementations • 4 Oct 2023 • Kartik Ahuja, Amin Mansouri, Yixin Wang
Causal representation learning has emerged as the center of action in causal machine learning research.
1 code implementation • 19 Aug 2023 • Yixin Wang, Wei Peng, Susan F. Tapert, Qingyu Zhao, Kilian M. Pohl
An alternative is to impute the missing measurements via a deep learning approach.
no code implementations • 10 Aug 2023 • Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, Wei Lou, Haofeng Li, Eric Upschulte, Timo Dickscheid, José Guilherme de Almeida, Yixin Wang, Lin Han, Xin Yang, Marco Labagnara, Vojislav Gligorovski, Maxime Scheder, Sahand Jamal Rahi, Carly Kempster, Alice Pollitt, Leon Espinosa, Tâm Mignot, Jan Moritz Middeke, Jan-Niklas Eckardt, Wangkai Li, Zhaoyang Li, Xiaochen Cai, Bizhe Bai, Noah F. Greenwald, David Van Valen, Erin Weisbart, Beth A. Cimini, Trevor Cheung, Oscar Brück, Gary D. Bader, Bo wang
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
1 code implementation • 24 Jul 2023 • Yixin Wang, Zihao Lin, Haoyu Dong
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process.
1 code implementation • 8 Jul 2023 • Kevin Christian Wibisono, Yixin Wang
The key observation is that fitting a single-layer single-head bidirectional attention, upon reparameterization, is equivalent to fitting a continuous bag of words (CBOW) model with mixture-of-experts (MoE) weights.
no code implementations • 25 May 2023 • Carles Balsells-Rodas, Yixin Wang, Yingzhen Li
In the realm of interpretability and out-of-distribution generalisation, the identifiability of latent variable models has emerged as a captivating field of inquiry.
no code implementations • 17 Mar 2023 • Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei
Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off.
no code implementations • 6 Feb 2023 • Cameron Gruich, Varun Madhavan, Yixin Wang, Bryan Goldsmith
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches.
no code implementations • 29 Jan 2023 • Xuan Lu, Wei Ai, Yixin Wang, Qiaozhu Mei
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown.
no code implementations • NeurIPS 2021 • Yixin Wang, David M. Blei, John P. Cunningham
Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations.
no code implementations • 29 Nov 2022 • Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning.
no code implementations • 21 Nov 2022 • Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak
Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice.
no code implementations • 10 Nov 2022 • Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan
This result shows that exponential-in-$m$ samples are sufficient and necessary to learn a near-optimal contract, resolving an open problem on the hardness of online contract design.
1 code implementation • CVPR 2023 • Yang Liu, Yao Zhang, Yixin Wang, Yang Zhang, Jiang Tian, Zhongchao shi, Jianping Fan, Zhiqiang He
To bridge the gap between the reference points of salient queries and Transformer detectors, we propose SAlient Point-based DETR (SAP-DETR) by treating object detection as a transformation from salient points to instance objects.
no code implementations • 25 Oct 2022 • Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes.
6 code implementations • 22 Oct 2022 • Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen, Wenzhuo Tang, Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher, Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
1 code implementation • 24 Sep 2022 • Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio
Can interventional data facilitate causal representation learning?
no code implementations • 29 Aug 2022 • Michael I. Jordan, Yixin Wang, Angela Zhou
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference.
no code implementations • 15 Aug 2022 • Celestine Mendler-Dünner, Frances Ding, Yixin Wang
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict.
no code implementations • 11 Aug 2022 • Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
Causal discovery and causal effect estimation are two fundamental tasks in causal inference.
no code implementations • 22 Jul 2022 • Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang
Focusing on Gaussian inferential models (or variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error.
no code implementations • 4 Jul 2022 • Karl Krauth, Yixin Wang, Michael I. Jordan
Our main observation is that a recommender system does not suffer from feedback loops if it reasons about causal quantities, namely the intervention distributions of recommendations on user ratings.
no code implementations • 4 Jul 2022 • Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, Michael I. Jordan
Building from a pre-trained ranking model, we show how to return a set of items that is rigorously guaranteed to contain mostly good items.
no code implementations • 22 Feb 2022 • Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael I. Jordan
Directly adjusting for these imperfect measurements of the covariates can lead to biased causal estimates.
no code implementations • 10 Feb 2022 • Hunter Nisonoff, Yixin Wang, Jennifer Listgarten
The need for function estimation in label-limited settings is common in the natural sciences.
1 code implementation • 13 Jan 2022 • Yixin Wang, Anthony Degleris, Alex H. Williams, Scott W. Linderman
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i. e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data.
1 code implementation • 11 Nov 2021 • Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao shi, Jianping Fan, Zhiqiang He
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP).
1 code implementation • 20 Oct 2021 • Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
The underlying model is sparse in that each observed feature (i. e. each dimension of the data) depends on a small subset of the latent factors.
1 code implementation • 28 Sep 2021 • Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
1 code implementation • 24 Sep 2021 • Mingzhang Yin, Yixin Wang, David M. Blei
This paper presents a new optimization approach to causal estimation.
1 code implementation • 8 Sep 2021 • Yixin Wang, Michael I. Jordan
Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data.
no code implementations • NeurIPS 2021 • Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data.
2 code implementations • 28 Jun 2021 • Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao shi, Jianping Fan, Zhiqiang He
Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.
no code implementations • 21 Jun 2021 • Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao shi, Yang Zhang, Jianping Fan, Zhiqiang He
Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.
1 code implementation • 3 Jun 2021 • Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jayender Jagadeesan, Kai Ma, Yefeng Zheng, Xiu Li
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.
no code implementations • 30 Mar 2021 • Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan
Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.
no code implementations • 19 Feb 2021 • Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Mladen Kolar, Zhaoran Wang
Instrumental variables (IVs), in the context of RL, are the variables whose influence on the state variables are all mediated through the action.
1 code implementation • 29 Dec 2020 • Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice.
no code implementations • 19 Oct 2020 • Yixin Wang, Yao Zhang, Feng Hou, Yang Liu, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He
In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation.
no code implementations • 19 Oct 2020 • Yixin Wang, Yao Zhang, Jiang Tian, Cheng Zhong, Zhongchao shi, Yang Zhang, Zhiqiang He
We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i. e. segmentation uncertainty and feature uncertainty.
1 code implementation • NeurIPS 2020 • Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning.
no code implementations • 23 Jun 2020 • Yixin Wang, Yao Zhang, Yang Liu, Jiang Tian, Cheng Zhong, Zhongchao Shi, Yang Zhang, Zhiqiang He
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world.
2 code implementations • 27 Apr 2020 • Jun Ma, Yixin Wang, Xingle An, Cheng Ge, Ziqi Yu, Jianan Chen, Qiongjie Zhu, Guoqiang Dong, Jian He, Zhiqiang He, Yuntao Zhu, Ziwei Nie, Xiaoping Yang
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients.
no code implementations • 10 Mar 2020 • Yixin Wang, David M. Blei
Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm.
2 code implementations • MIDL 2019 • Jun Ma, Zhan Wei, Yiwen Zhang, Yixin Wang, Rongfei Lv, Cheng Zhu, Gaoxiang Chen, Jianan Liu, Chao Peng, Lei Wang, Yunpeng Wang, Jianan Chen
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
4 code implementations • 2 Dec 2019 • Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein, Xiaoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guangrui Mu, Zhiyong Lin, Miofei Han, Guang Yao, Yaozong Gao, Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong, Jun Ma, Jack Rickman, Joshua Dean, Bethany Stai, Resha Tejpaul, Makinna Oestreich, Paul Blake, Heather Kaluzniak, Shaneabbas Raza, Joel Rosenberg, Keenan Moore, Edward Walczak, Zachary Rengel, Zach Edgerton, Ranveer Vasdev, Matthew Peterson, Sean McSweeney, Sarah Peterson, Arveen Kalapara, Niranjan Sathianathen, Nikolaos Papanikolopoulos, Christopher Weight
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
no code implementations • 15 Oct 2019 • Yixin Wang, David M. Blei
Ogburn et al. (2019, arXiv:1910. 05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805. 06826).
no code implementations • 5 Oct 2019 • Yao Zhang, Yixin Wang, Feng Hou, Jiawei Yang, Guangwei Xiong, Jiang Tian, Cheng Zhong
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease.
no code implementations • 30 May 2019 • Yixin Wang, David M. Blei
Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.
1 code implementation • NeurIPS 2019 • Yixin Wang, David M. Blei
As a consequence of these results, we find that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions.
no code implementations • 26 May 2019 • Yixin Wang, Dhanya Sridhar, David M. Blei
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.
no code implementations • 3 Apr 2019 • Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak
To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs.
3 code implementations • NeurIPS 2019 • Victor Veitch, Yixin Wang, David M. Blei
We validate the method with experiments on a semi-synthetic social network dataset.
no code implementations • 20 Aug 2018 • Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome."
no code implementations • ICML 2018 • Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan
BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis.
2 code implementations • 17 May 2018 • Yixin Wang, David M. Blei
Causal inference from observational data often assumes "ignorability," that all confounders are observed.
no code implementations • ICML 2017 • Alp Kucukelbir, Yixin Wang, David M. Blei
We propose to evaluate a model through posterior dispersion.
no code implementations • 9 May 2017 • Yixin Wang, David M. Blei
The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB.
1 code implementation • 2 May 2017 • Yixin Wang, José R. Zubizarreta
The key observation is the connection between approximate covariate balance and shrinkage estimation of the propensity score.
Methodology Statistics Theory Applications Statistics Theory
1 code implementation • ICML 2017 • Yixin Wang, Alp Kucukelbir, David M. Blei
We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models.