Proceedings of machine learning research最新文献_第2页

DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. DISCRET：综合治疗效果估算的忠实解释。

Proceedings of machine learning research Pub Date : 2024-07-01

Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong

{"title":"DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation.","authors":"Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong","doi":"","DOIUrl":"","url":null,"abstract":"Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"53597-53618"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters. 核去偏插件估算：针对众多目标参数的无影响函数同步自动去差分。

Proceedings of machine learning research Pub Date : 2024-07-01

Brian Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica

{"title":"Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters.","authors":"Brian Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica","doi":"","DOIUrl":"","url":null,"abstract":"When estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce \"plug-in bias.\" Traditional methods addressing this suboptimal bias-variance trade-off rely on the influence function (IF) of the target parameter. When estimating multiple target parameters, these methods require debiasing the nuisance parameter multiple times using the corresponding IFs, which poses analytical and computational challenges. In this work, we leverage the targeted maximum likelihood estimation (TMLE) framework to propose a novel method named kernel debiased plug-in estimation (KDPE). KDPE refines an initial estimate through regularized likelihood maximization steps, employing a nonparametric model based on reproducing kernel Hilbert spaces. We show that KDPE: (i) simultaneously debiases all pathwise differentiable target parameters that satisfy our regularity conditions, (ii) does not require the IF for implementation, and (iii) remains computationally tractable. We numerically illustrate the use of KDPE and validate our theoretical results.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"8534-8555"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11359899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR.

Proceedings of machine learning research Pub Date : 2024-06-01

Ran Xu, Yiwen Lu, Chang Liu, Yong Chen, Yan Sun, Xiao Hu, Joyce C Ho, Carl Yang

引用次数: 0

Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges. 利用 LLM 从电子病历中检索证据：可能性与挑战。

Proceedings of machine learning research Pub Date : 2024-06-01

Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher Potter, Geoffrey Young, Silvio Amir, Byron C Wallace

{"title":"Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges.","authors":"Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher Potter, Geoffrey Young, Silvio Amir, Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"Unstructured data in Electronic Health Records (EHRs) often contains critical information-complementary to imaging-that could inform radiologists' diagnoses. But the large volume of notes often associated with patients together with time constraints renders manually identifying relevant evidence practically infeasible. In this work we propose and evaluate a zero-shot strategy for using LLMs as a mechanism to efficiently retrieve and summarize unstructured evidence in patient EHR relevant to a given query. Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence. Under expert evaluation, we find that this LLM-based approach provides outputs consistently preferred to a pre-LLM information retrieval baseline. Manual evaluation is expensive, so we also propose and validate a method using an LLM to evaluate (other) LLM outputs for this task, allowing us to scale up evaluation. Our findings indicate the promise of LLMs as interfaces to EHR, but also highlight the outstanding challenge posed by \"hallucinations\". In this setting, however, we show that model confidence in outputs strongly correlates with faithful summaries, offering a practical means to limit confabulations.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"248 ","pages":"489-505"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation. 用于体育锻炼数据估算的时空多尺度稀疏自我关注。

Proceedings of machine learning research Pub Date : 2024-06-01

Hui Wei, Maxwell A Xu, Colin Samplawski, James M Rehg, Santosh Kumar, Benjamin M Marlin

引用次数: 0

s-SuStaIn: Scaling subtype and stage inference via simultaneous clustering of subjects and biomarkers.

Proceedings of machine learning research Pub Date : 2024-06-01

Raghav Tandon, James J Lah, Cassie S Mitchell

引用次数: 0

Online Calibrated and Conformal Prediction Improves Bayesian Optimization. 在线校准和适形预测改进了贝叶斯优化。

Proceedings of machine learning research Pub Date : 2024-05-01

Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

引用次数: 0

Adaptive Discretization for Event PredicTion (ADEPT). 事件预测自适应离散化（ADEPT）。

Proceedings of machine learning research Pub Date : 2024-05-01

Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard

{"title":"Adaptive Discretization for Event PredicTion (ADEPT).","authors":"Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard","doi":"","DOIUrl":"","url":null,"abstract":"Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions on the event density, this approach tends to improve prediction performance, particularly when data are plentiful. However, in clinical settings with limited available data, it is often preferable to judiciously partition the event time space into a limited number of intervals well suited to the prediction task at hand. In this work, we develop Adaptive Discretization for Event PredicTion (ADEPT) to learn from data a set of cut points defining such a partition. We show that in two simulated datasets, we are able to recover intervals that match the underlying generative model. We then demonstrate improved prediction performance on three real-world observational datasets, including a large, newly harmonized stroke risk prediction dataset. Finally, we argue that our approach facilitates clinical decision-making by suggesting time intervals that are most appropriate for each task, in the sense that they facilitate more accurate risk prediction.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"1351-1359"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11078624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of machine learning research Pub Date : 2024-05-01

Zongyu Dai, Emily Getzen, Qi Long

{"title":"SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data.","authors":"Zongyu Dai, Emily Getzen, Qi Long","doi":"","DOIUrl":"","url":null,"abstract":"Missing values are prevalent in temporal electronic health records (EHR) data and are known to complicate data analysis and lead to biased results. The current state-of-the-art (SOTA) models for imputing missing values in EHR primarily leverage correlations across time points and across features, which perform well when data have strong correlation across time points, such as in ICU data where high-frequency time series data are collected. However, this is often insufficient for temporal EHR data from non-ICU settings (e.g., outpatient visits for primary care or specialty care), where data are collected at substantially sparser time points, resulting in much weaker correlation across time points. To address this methodological gap, we propose the Similarity-Aware Diffusion Model-Based Imputation (SADI), a novel imputation method that leverages the diffusion model and utilizes information across dependent variables. We apply SADI to impute incomplete temporal EHR data and propose a similarity-aware denoising function, which includes a self-attention mechanism to model the correlations between time points, features, and similar patients. To the best of our knowledge, this is the first time that the information of similar patients is directly used to construct imputation for incomplete temporal EHR data. Our extensive experiments on two datasets, the Critical Path For Alzheimer's Disease (CPAD) data and the PhysioNet Challenge 2012 data, show that SADI outperforms the current SOTA under various missing data mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"4195-4203"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11391213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Generalization Ability of Unsupervised Pretraining. 论无监督预训练的泛化能力

Proceedings of machine learning research Pub Date : 2024-05-01

Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi

{"title":"On the Generalization Ability of Unsupervised Pretraining.","authors":"Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi","doi":"","DOIUrl":"","url":null,"abstract":"Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset affects the generalization of the fine-tuned model is lacking. Existing theoretical research does not adequately account for the heterogeneity of the distribution and tasks in pre-training and fine-tuning stage. To bridge this gap, this paper introduces a novel theoretical framework that illuminates the critical factor influencing the transferability of knowledge acquired during unsupervised pre-training to the subsequent fine-tuning phase, ultimately affecting the generalization capabilities of the fine-tuned model on downstream tasks. We apply our theoretical framework to analyze generalization bound of two distinct scenarios: Context Encoder pre-training with deep neural networks and Masked Autoencoder pre-training with deep transformers, followed by fine-tuning on a binary classification task. Finally, inspired by our findings, we propose a novel regularization method during pre-training to further enhances the generalization of fine-tuned model. Overall, our results contribute to a better understanding of unsupervised pre-training and fine-tuning paradigm, and can shed light on the design of more effective pre-training algorithms.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"238 ","pages":"4519-4527"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0