Search Results for author: Yixin Wang

Found 41 papers, 17 papers with code

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection

no code implementations22 Jul 2022 Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang

From the Bayesian posterior inference perspective, we characterize the error of the variational posterior relative to the exact posterior.

Bayesian Inference Model Selection +2

Recommendation Systems with Distribution-Free Reliability Guarantees

no code implementations4 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.

Learning-To-Rank Recommendation Systems

Breaking Feedback Loops in Recommender Systems with Causal Inference

no code implementations4 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.

Causal Inference Recommendation Systems

Partial Identification with Noisy Covariates: A Robust Optimization Approach

no code implementations22 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.

Causal Inference

Augmenting Neural Networks with Priors on Function Values

no code implementations10 Feb 2022 Hunter Nisonoff, Yixin Wang, Jennifer Listgarten

The need for function estimation in label-limited settings is common in the natural sciences.

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

no code implementations13 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.

Bayesian Inference Event Detection

Posterior Collapse and Latent Variable Non-identifiability

no code implementations NeurIPS 2021 Yixin Wang, David Blei, John P. Cunningham

Existing approaches to posterior collapse oftenattribute it to the use of neural networks or optimization issues dueto variational approximation.

A Survey of Visual Transformers

1 code implementation11 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).

Natural Language Processing

Identifiable Deep Generative Models via Sparse Decoding

1 code implementation20 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.

Representation Learning

Desiderata for Representation Learning: A Causal Perspective

1 code implementation8 Sep 2021 Yixin Wang, Michael I. Jordan

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data.

Disentanglement

Learning Equilibria in Matching Markets from Bandit Feedback

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.

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

1 code implementation28 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.

Brain Tumor Segmentation Transfer Learning +1

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

no code implementations21 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.

Decision Making Image Captioning

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

1 code implementation3 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.

Multi-Source Causal Inference Using Control Variates

no code implementations30 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.

Causal Inference Epidemiology +2

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

no code implementations19 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.

Offline RL reinforcement-learning

Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer

1 code implementation29 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.

Semantic Segmentation Style Transfer

Double-Uncertainty Weighted Method for Semi-supervised Learning

no code implementations19 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.

Towards Clarifying the Theory of the Deconfounder

no code implementations10 Mar 2020 Yixin Wang, David M. Blei

Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm.

Causal Inference

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

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.

The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)

no code implementations15 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).

Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes

no code implementations5 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.

Tumor Segmentation

Multiple Causes: A Causal Graphical View

no code implementations30 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.

Causal Inference

Variational Bayes under Model Misspecification

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.

Equal Opportunity and Affirmative Action via Counterfactual Predictions

no code implementations26 May 2019 Yixin Wang, Dhanya Sridhar, David M. Blei

Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.

Decision Making Fairness

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

no code implementations3 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.

The Deconfounded Recommender: A Causal Inference Approach to Recommendation

no code implementations20 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."

Causal Inference Recommendation Systems

Black Box FDR

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.

The Blessings of Multiple Causes

2 code implementations17 May 2018 Yixin Wang, David M. Blei

Causal inference from observational data often assumes "ignorability," that all confounders are observed.

Causal Inference

Frequentist Consistency of Variational Bayes

no code implementations9 May 2017 Yixin Wang, David M. Blei

The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB.

Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations

1 code implementation2 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

Robust Probabilistic Modeling with Bayesian Data Reweighting

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.

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