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

DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency. DiracDiffusion：去噪和增量重建，确保数据一致性

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

Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi

{"title":"DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency.","authors":"Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi","doi":"","DOIUrl":"","url":null,"abstract":"Diffusion models have established new state of the art in a multitude of computer vision tasks, including image restoration. Diffusion-based inverse problem solvers generate reconstructions of exceptional visual quality from heavily corrupted measurements. However, in what is widely known as the perception-distortion trade-off, the price of perceptually appealing reconstructions is often paid in declined distortion metrics, such as PSNR. Distortion metrics measure faithfulness to the observation, a crucial requirement in inverse problems. In this work, we propose a novel framework for inverse problem solving, namely we assume that the observation comes from a stochastic degradation process that gradually degrades and noises the original clean image. We learn to reverse the degradation process in order to recover the clean image. Our technique maintains consistency with the original measurement throughout the reverse process, and allows for great flexibility in trading off perceptual quality for improved distortion metrics and sampling speedup via early-stopping. We demonstrate the efficiency of our method on different high-resolution datasets and inverse problems, achieving great improvements over other state-of-the-art diffusion-based methods with respect to both perceptual and distortion metrics.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"12754-12783"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482675","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

Self-supervised pretraining in the wild imparts image acquisition robustness to medical image transformers: an application to lung cancer segmentation. 野外自监督预训练赋予医学图像转换器图像获取鲁棒性：肺癌分割的应用。

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

Jue Jiang, Harini Veeraraghavan

{"title":"Self-supervised pretraining in the wild imparts image acquisition robustness to medical image transformers: an application to lung cancer segmentation.","authors":"Jue Jiang, Harini Veeraraghavan","doi":"","DOIUrl":"","url":null,"abstract":"Self-supervised learning (SSL) is an approach to pretrain models with unlabeled datasets and extract useful feature representations such that these models can be easily fine-tuned for various downstream tasks. Self-pretraining applies SSL on curated task-specific datasets without using task-specific labels. Increasing availability of public data repositories has now made it possible to utilize diverse and large, task unrelated datasets to pretrain models in the \"wild\" using SSL. However, the benefit of such wild-pretraining over self-pretraining has not been studied in the context of medical image analysis. Hence, we analyzed transformers (Swin and ViT) and a convolutional neural network created using wild- and self-pretraining trained to segment lung tumors from 3D-computed tomography (CT) scans in terms of: (a) accuracy, (b) fine-tuning epoch efficiency, and (c) robustness to image acquisition differences (contrast versus non-contrast, slice thickness, and image reconstruction kernels). We also studied feature reuse using centered kernel alignment (CKA) with the Swin networks. Our analysis with two independent testing (public N = 139; internal N = 196) datasets showed that wild-pretrained Swin models significantly outperformed self-pretrained Swin for the various imaging acquisitions. Fine-tuning epoch efficiency was higher for both wild-pretrained Swin and ViT models compared to their self-pretrained counterparts. Feature reuse close to the final encoder layers was lower than in the early layers for wild-pretrained models irrespective of the pretext tasks used in SSL. Models and code will be made available through GitHub upon manuscript acceptance.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"250 ","pages":"708-721"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017993","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

Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability. 通过分布可学习性对分布转移下的可学习数据库操作进行理论分析》（Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability）。

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

Sepanta Zeighami, Cyrus Shahabi

{"title":"Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability.","authors":"Sepanta Zeighami, Cyrus Shahabi","doi":"","DOIUrl":"","url":null,"abstract":"Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical results also show performance degradation for learned models, possibly to worse than non-learned alternatives. This, together with a lack of theoretical understanding of learned methods undermines their practical applicability, since there are no guarantees on how well the models will perform after deployment. In this paper, we present the first known theoretical characterization of the performance of learned models in dynamic datasets, for the aforementioned operations. Our results show novel theoretical characteristics achievable by learned models and provide bounds on the performance of the models that characterize their advantages over non-learned methods, showing why and when learned models can outperform the alternatives. Our analysis develops the distribution learnability framework and novel theoretical tools which build the foundation for the analysis of learned database operations in the future.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"58283-58305"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577095","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

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

Position: Topological Deep Learning is the New Frontier for Relational Learning. 观点：拓扑深度学习是关系学习的新前沿。

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

Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

引用次数: 0

From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR. 从基本到额外的功能：超图变压器预训练-然后微调平衡临床预测电子病历。

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. s-SuStaIn：通过受试者和生物标记物的同时聚类来衡量亚型和阶段推断。

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

Raghav Tandon, James J Lah, Cassie S Mitchell

引用次数: 0