Transactions on machine learning research最新文献_第3页

Selective Classification Under Distribution Shifts. 分布移位下的选择性分类。

Transactions on machine learning research Pub Date : 2024-10-01

Hengyue Liang, Le Peng, Ju Sun

{"title":"Selective Classification Under Distribution Shifts.","authors":"Hengyue Liang, Le Peng, Ju Sun","doi":"","DOIUrl":"","url":null,"abstract":"In selective classification (SC), a classifier abstains from making predictions that are likely to be wrong to avoid excessive errors. To deploy imperfect classifiers-either due to intrinsic statistical noise of data or for robustness issue of the classifier or beyond-in high-stakes scenarios, SC appears to be an attractive and necessary path to follow. Despite decades of research in SC, most previous SC methods still focus on the ideal statistical setting only, i.e., the data distribution at deployment is the same as that of training, although practical data can come from the wild. To bridge this gap, in this paper, we propose an SC framework that takes into account distribution shifts, termed generalized selective classification, that covers label-shifted (or out-of-distribution) and covariate-shifted samples, in addition to typical in-distribution samples, the first of its kind in the SC literature. We focus on non-training-based confidence-score functions for generalized SC on deep learning (DL) classifiers, and propose two novel margin-based score functions. Through extensive analysis and experiments, we show that our proposed score functions are more effective and reliable than the existing ones for generalized SC on a variety of classification tasks and DL classifiers. The code is available at https://github.com/sun-umn/sc_with_distshift.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12470254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187750","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

MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning. MoMA：离线强化学习的基于模型的镜像提升。

Transactions on machine learning research Pub Date : 2024-10-01

Mao Hong, Zhiyue Zhang, Yue Wu, Yanxun Xu

{"title":"MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning.","authors":"Mao Hong, Zhiyue Zhang, Yue Wu, Yanxun Xu","doi":"","DOIUrl":"","url":null,"abstract":"Model-based offline reinforcement learning methods (RL) have achieved state-of-the-art performance in many decision-making problems thanks to their sample efficiency and generalizability. Despite these advancements, existing model-based offline RL approaches either focus on theoretical studies without developing practical algorithms or rely on a restricted parametric policy space, thus not fully leveraging the advantages of an unrestricted policy space inherent to model-based methods. To address this limitation, we develop MoMA, a model-based mirror ascent algorithm with general function approximations under partial coverage of offline data. MoMA distinguishes itself from existing literature by employing an unrestricted policy class. In each iteration, MoMA conservatively estimates the value function by a minimization procedure within a confidence set of transition models in the policy evaluation step, then updates the policy with general function approximations instead of commonly-used parametric policy classes in the policy improvement step. Under some mild assumptions, we establish theoretical guarantees for MoMA by proving an upper bound on the suboptimality of the returned policy. We also provide a practically implementable, approximate version of the algorithm. The effectiveness of MoMA is demonstrated via numerical studies.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12742664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851635","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

ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text. 心电语义积分器（ESI）：用llm增强的心电学文本预训练的基础心电模型。

Transactions on machine learning research Pub Date : 2024-10-01

Han Yu, Peikun Guo, Akane Sano

{"title":"ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text.","authors":"Han Yu, Peikun Guo, Akane Sano","doi":"","DOIUrl":"","url":null,"abstract":"The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. In this work, we address a critical challenge in the field of ECG analysis with deep learning: learning robust representation without large-scale labeled datasets. We propose ECG Semantic Integrator (ESI), a novel multimodal contrastive pretraining framework that jointly learns from ECG signals and associated textual descriptions. ESI employs a dual objective function that comprises a contrastive loss and a captioning loss to develop representations of ECG data. To create a sufficiently large and diverse training dataset, we develop a retrieval-augmented generation (RAG)-based Large Language Model (LLM) pipeline, called Cardio Query Assistant (CQA). This pipeline is designed to generate detailed textual descriptions for ECGs from diverse databases. The generated text includes information about demographics and waveform patterns. This approach enables us to compile a large-scale multimodal dataset with over 660,000 ECG-text pairs for pretraining ESI, which then learns robust and generalizable representations of 12-lead ECG. We validate our approach through various downstream tasks, including arrhythmia detection and ECG-based subject identification. Our experimental results demonstrate substantial improvements over strong baselines in these tasks. These baselines encompass supervised and self-supervised learning methods, as well as prior multimodal pretraining approaches. Our work shows the potential of combining multimodal pretraining to improve the analysis of ECG signals. The training code and generated waveform descriptions are available at https://github.com/comp-well-org/ESI.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12974696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438299","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

ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. ModuLoRA：通过集成模块化量化器对消费级gpu上的2位llm进行微调。

Transactions on machine learning research Pub Date : 2024-02-01 Epub Date: 2024-02-27

Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov

{"title":"ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers.","authors":"Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov","doi":"","DOIUrl":"","url":null,"abstract":"We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 2/3/4-bit precision on as little as one 24GB GPU. Our method, modular low-rank adaptation (ModuLoRA), integrates any user-specified weight quantizer with finetuning via low-rank adapters (LoRAs). Our approach relies on a simple quantization-agnostic backward pass that adaptively materializes low-precision LLM weights from a custom black-box quantization module. This approach enables finetuning 2-bit and 3-bit LLMs for the first time-leveraging state-of-the-art 2-bit QuIP# quantization and 3-bit OPTQ quantization-outperforming finetuning that relies on less sophisticated 4-bit and 8-bit methods. In our experiments, ModuLoRA attains competitive performance on text classification, natural language inference, and instruction following tasks using significantly less memory than existing approaches, and we also surpass the state-of-the-art ROUGE score on a popular summarization task. We release ModuLoRA together with a series of low-precision models as part of LLMTools, a user-friendly library for quantizing, running, and finetuning LLMs on consumer GPUs.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981971","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

Federated Learning with Convex Global and Local Constraints. 全局和局部约束条件下的联合学习

Transactions on machine learning research Pub Date : 2024-01-01 Epub Date: 2024-05-03

Chuan He, Le Peng, Ju Sun

{"title":"Federated Learning with Convex Global and Local Constraints.","authors":"Chuan He, Le Peng, Ju Sun","doi":"","DOIUrl":"","url":null,"abstract":"In practice, many machine learning (ML) problems come with constraints, and their applied domains involve distributed sensitive data that cannot be shared with others, e.g., in healthcare. Collaborative learning in such practical scenarios entails federated learning (FL) for ML problems with constraints, or FL with constraints for short. Despite the extensive developments of FL techniques in recent years, these techniques only deal with unconstrained FL problems or FL problems with simple constraints that are amenable to easy projections. There is little work dealing with FL problems with general constraints. To fill this gap, we take the first step toward building an algorithmic framework for solving FL problems with general constraints. In particular, we propose a new FL algorithm for constrained ML problems based on the proximal augmented Lagrangian (AL) method. Assuming convex objective and convex constraints plus other mild conditions, we establish the worst-case complexity of the proposed algorithm. Our numerical experiments show the effectiveness of our algorithm in performing Neyman-Pearson classification and fairness-aware learning with nonconvex constraints, in an FL setting.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11295925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891198","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

Online model selection by learning how compositional kernels evolve. 通过学习组成核的演变过程进行在线模型选择。

Transactions on machine learning research Pub Date : 2023-11-01

Eura Shin, Predrag Klasnja, Susan A Murphy, Finale Doshi-Velez

引用次数: 0

Reliable Active Learning via Influence Functions. 通过影响函数进行可靠的主动学习。

Transactions on machine learning research Pub Date : 2023-11-01

Meng Xia, Ricardo Henao

{"title":"Reliable Active Learning via Influence Functions.","authors":"Meng Xia, Ricardo Henao","doi":"","DOIUrl":"","url":null,"abstract":"Due to the high cost and time-consuming nature of collecting labeled data, having insufficient labeled data is a common challenge that can negatively impact the performance of deep learning models when applied to real-world applications. Active learning (AL) aims to reduce the cost and time required for obtaining labeled data by selecting valuable samples during model training. However, recent works have pointed out the performance unreliability of existing AL algorithms for deep learning (DL) architectures under different scenarios, which manifests as their performance being comparable (or worse) to that of basic random selection. This behavior compromises the applicability of these approaches. We address this problem by proposing a theoretically motivated AL framework for DL architectures. We demonstrate that the most valuable samples for the model are those that, unsurprisingly, improve its performance on the entire dataset, most of which is unlabeled, and present a framework to efficiently estimate such performance (or loss) via influence functions, pseudo labels and diversity selection. Experimental results show that the proposed reliable active learning via influence functions (RALIF) can consistently outperform the random selection baseline as well as other existing and state-of-the art active learning approaches.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208297","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

Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics. 超越分布偏移：从训练动态的角度看虚假特征。

Transactions on machine learning research Pub Date : 2023-10-01

Nihal Murali, Aahlad Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich

{"title":"Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics.","authors":"Nihal Murali, Aahlad Puli, Ke Yu, Rajesh Ranganath, Kayhan Batmanghelich","doi":"","DOIUrl":"","url":null,"abstract":"Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when deploying them in safety-critical applications. This paper aims to better understand the effects of spurious features through the lens of the learning dynamics of the internal neurons during the training process. We make the following observations: (1) While previous works highlight the harmful effects of spurious features on the generalization ability of DNNs, we emphasize that not all spurious features are harmful. Spurious features can be \"benign\" or \"harmful\" depending on whether they are \"harder\" or \"easier\" to learn than the core features for a given model. This definition is model and dataset dependent. (2) We build upon this premise and use instance difficulty methods (like Prediction Depth (Baldock et al., 2021)) to quantify \"easiness\" for a given model and to identify this behavior during the training phase. (3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN's early layers. In other words, easy features learned by the initial layers of a DNN early during the training can (potentially) hurt model generalization. We verify our claims on medical and vision datasets, both simulated and real, and justify the empirical success of our hypothesis by showing the theoretical connections between Prediction Depth and information-theoretic concepts like <math><mi>𝒱</mi></math>-usable information (Ethayarajh et al., 2021). Lastly, our experiments show that monitoring only accuracy during training (as is common in machine learning pipelines) is insufficient to detect spurious features. We, therefore, highlight the need for monitoring early training dynamics using suitable instance difficulty metrics.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11029547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863872","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

RIFLE: Imputation and Robust Inference from Low Order Marginals. RIFLE：根据低阶边际值进行归因和稳健推断。

Transactions on machine learning research Pub Date : 2023-09-01

Sina Baharlouei, Kelechi Ogudu, Sze-Chuan Suen, Meisam Razaviyayn

{"title":"RIFLE: Imputation and Robust Inference from Low Order Marginals.","authors":"Sina Baharlouei, Kelechi Ogudu, Sze-Chuan Suen, Meisam Razaviyayn","doi":"","DOIUrl":"","url":null,"abstract":"The ubiquity of missing values in real-world datasets poses a challenge for statistical inference and can prevent similar datasets from being analyzed in the same study, precluding many existing datasets from being used for new analyses. While an extensive collection of packages and algorithms have been developed for data imputation, the overwhelming majority perform poorly if there are many missing values and low sample sizes, which are unfortunately common characteristics in empirical data. Such low-accuracy estimations adversely affect the performance of downstream statistical models. We develop a statistical inference framework for regression and classification in the presence of missing data without imputation. Our framework, RIFLE (Robust InFerence via Low-order moment Estimations), estimates low-order moments of the underlying data distribution with corresponding confidence intervals to learn a distributionally robust model. We specialize our framework to linear regression and normal discriminant analysis, and we provide convergence and performance guarantees. This framework can also be adapted to impute missing data. In numerical experiments, we compare RIFLE to several state-of-the-art approaches (including MICE, Amelia, MissForest, KNN-imputer, MIDA, and Mean Imputer) for imputation and inference in the presence of missing values. Our experiments demonstrate that RIFLE outperforms other benchmark algorithms when the percentage of missing values is high and/or when the number of data points is relatively small. RIFLE is publicly available at https://github.com/optimization-for-data-driven-science/RIFLE.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10977932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320107","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

mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization. mL-BFGS：用于分布式大规模神经网络优化的基于动量的L-BFGS。

Transactions on machine learning research Pub Date : 2023-08-01

Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr

{"title":"mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization.","authors":"Yue Niu, Zalan Fabian, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr","doi":"","DOIUrl":"","url":null,"abstract":"Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability issues in stochastic training. A well-known method, L-BFGS that efficiently approximates the Hessian using history parameter and gradient changes, suffers convergence instability in stochastic training. So far, attempts that adapt L-BFGS to large-scale stochastic training incur considerable extra overhead, which offsets its convergence benefits in wall-clock time. In this paper, we propose mL-BFGS, a lightweight momentum-based L-BFGS algorithm that paves the way for quasi-Newton (QN) methods in large-scale distributed deep neural network (DNN) optimization. mL-BFGS introduces a nearly cost-free momentum scheme into L-BFGS update and greatly reduces stochastic noise in the Hessian, therefore stabilizing convergence during stochastic optimization. For model training at a large scale, mL-BFGS approximates a block-wise Hessian, thus enabling distributing compute and memory costs across all computing nodes. We provide a supporting convergence analysis for mL-BFGS in stochastic settings. To investigate mL-BFGS's potential in large-scale DNN training, we train benchmark neural models using mL-BFGS and compare performance with baselines (SGD, Adam, and other quasi-Newton methods). Results show that mL-BFGS achieves both noticeable iteration-wise and wall-clock speedup.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982031","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