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On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing 学习索引的分布相关次对数查询时间
Proceedings of machine learning research Pub Date : 2023-06-19 DOI: 10.48550/arXiv.2306.10651
Sepanta Zeighami, C. Shahabi
{"title":"On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing","authors":"Sepanta Zeighami, C. Shahabi","doi":"10.48550/arXiv.2306.10651","DOIUrl":"https://doi.org/10.48550/arXiv.2306.10651","url":null,"abstract":"A fundamental problem in data management is to find the elements in an array that match a query. Recently, learned indexes are being extensively used to solve this problem, where they learn a model to predict the location of the items in the array. They are empirically shown to outperform non-learned methods (e.g., B-trees or binary search that answer queries in O(logn) time) by orders of magnitude. However, success of learned indexes has not been theoretically justified. Only existing attempt shows the same query time of O(logn), but with a constant factor improvement in space complexity over non-learned methods, under some assumptions on data distribution. In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in O(loglogn) expected query time. We also show that allowing for slightly larger but still near-linear space overhead, a learned index can achieve O(1) expected query time. Our results theoretically prove learned indexes are orders of magnitude faster than non-learned methods, theoretically grounding their empirical success.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"261 1","pages":"40669-40680"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79545250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions 评估上下文推断误差和部分可观察性对实时自适应干预RL方法的影响
Proceedings of machine learning research Pub Date : 2023-05-17 DOI: 10.48550/arXiv.2305.09913
Karine Karine, P. Klasnja, Susan A. Murphy, Benjamin M Marlin
{"title":"Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions","authors":"Karine Karine, P. Klasnja, Susan A. Murphy, Benjamin M Marlin","doi":"10.48550/arXiv.2305.09913","DOIUrl":"https://doi.org/10.48550/arXiv.2305.09913","url":null,"abstract":"Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"216 1","pages":"1047-1057"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46832340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DIET: Conditional independence testing with marginal dependence measures of residual information. DIET:利用残差信息的边际依赖性测量进行条件独立性检验。
Mukund Sudarshan, Aahlad Puli, Wesley Tansey, Rajesh Ranganath
{"title":"DIET: Conditional independence testing with marginal dependence measures of residual information.","authors":"Mukund Sudarshan, Aahlad Puli, Wesley Tansey, Rajesh Ranganath","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Conditional randomization tests (CRTs) assess whether a variable <math><mi>x</mi></math> is predictive of another variable <math><mi>y</mi></math>, having observed covariates <math><mrow><mi>z</mi></mrow></math>. CRTs require fitting a large number of predictive models, which is often computationally intractable. Existing solutions to reduce the cost of CRTs typically split the dataset into a train and test portion, or rely on heuristics for interactions, both of which lead to a loss in power. We propose the decoupled independence test (DIET), an algorithm that avoids both of these issues by leveraging marginal independence statistics to test conditional independence relationships. DIET tests the marginal independence of two random variables: <math><mrow><msub><mi>F</mi><mrow><mi>x</mi><mo>∣</mo><mi>z</mi></mrow></msub><mo>(</mo><mi>x</mi><mo>∣</mo><mi>z</mi><mo>)</mo></mrow></math> and <math><mrow><msub><mi>F</mi><mrow><mi>y</mi><mo>∣</mo><mi>z</mi></mrow></msub><mo>(</mo><mi>y</mi><mo>∣</mo><mi>z</mi><mo>)</mo></mrow></math> where <math><mrow><msub><mi>F</mi><mrow><mo>⋅</mo><mo>∣</mo><mi>z</mi></mrow></msub><mo>(</mo><mo>⋅</mo><mo>∣</mo><mi>z</mi><mo>)</mo></mrow></math> is a conditional cumulative distribution function (CDF) for the distribution <math><mrow><mi>p</mi><mo>(</mo><mo>⋅</mo><mo>∣</mo><mi>z</mi><mo>)</mo></mrow></math>. These variables are termed \"information residuals.\" We give sufficient conditions for DIET to achieve finite sample type-1 error control and power greater than the type-1 error rate. We then prove that when using the mutual information between the information residuals as a test statistic, DIET yields the most powerful conditionally valid test. Finally, we show DIET achieves higher power than other tractable CRTs on several synthetic and real benchmarks.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 ","pages":"10343-10367"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484293/pdf/nihms-1899844.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10577745","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
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE. 协变量知情表示学习预防iVAE后塌陷。
Young-Geun Kim, Ying Liu, Xue-Xin Wei
{"title":"Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE.","authors":"Young-Geun Kim,&nbsp;Ying Liu,&nbsp;Xue-Xin Wei","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The recently proposed identifiable variational autoencoder (iVAE) framework provides a promising approach for learning latent independent components (ICs). iVAEs use auxiliary covariates to build an identifiable generation structure from covariates to ICs to observations, and the posterior network approximates ICs given observations and covariates. Though the identifiability is appealing, we show that iVAEs could have local minimum solution where observations and the approximated ICs are independent given covariates.-a phenomenon we referred to as the posterior collapse problem of iVAEs. To overcome this problem, we develop a new approach, covariate-informed iVAE (CI-iVAE) by considering a mixture of encoder and posterior distributions in the objective function. In doing so, the objective function prevents the posterior collapse, resulting latent representations that contain more information of the observations. Furthermore, CI-iVAE extends the original iVAE objective function to a larger class and finds the optimal one among them, thus having tighter evidence lower bounds than the original iVAE. Experiments on simulation datasets, EMNIST, Fashion-MNIST, and a large-scale brain imaging dataset demonstrate the effectiveness of our new method.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 ","pages":"2641-2660"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226378/pdf/nihms-1902106.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9908011","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
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation. 不要上当:标签泄漏在解释方法及其定量评价的重要性。
Neil Jethani, Adriel Saporta, Rajesh Ranganath
{"title":"Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation.","authors":"Neil Jethani, Adriel Saporta, Rajesh Ranganath","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Feature attribution methods identify which features of an input most influence a model's output. Most widely-used feature attribution methods (such as SHAP, LIME, and Grad-CAM) are \"class-dependent\" methods in that they generate a feature attribution vector as a function of class. In this work, we demonstrate that class-dependent methods can \"leak\" information about the selected class, making that class appear more likely than it is. Thus, an end user runs the risk of drawing false conclusions when interpreting an explanation generated by a class-dependent method. In contrast, we introduce \"distribution-aware\" methods, which favor explanations that keep the label's distribution close to its distribution given all features of the input. We introduce SHAP-KL and FastSHAP-KL, two baseline distribution-aware methods that compute Shapley values. Finally, we perform a comprehensive evaluation of seven class-dependent and three distribution-aware methods on three clinical datasets of different high-dimensional data types: images, biosignals, and text.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 ","pages":"8925-8953"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12022845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045795","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
Continuous-Time Decision Transformer for Healthcare Applications. 用于医疗保健应用的连续时间决策变压器。
Zhiyue Zhang, Hongyuan Mei, Yanxun Xu
{"title":"Continuous-Time Decision Transformer for Healthcare Applications.","authors":"Zhiyue Zhang, Hongyuan Mei, Yanxun Xu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Offline reinforcement learning (RL) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. In this paper, we investigate the potential usefulness of Decision Transformer (Chen et al., 2021)-a new offline RL paradigm-in medical domains where decision making in continuous time is desired. As Decision Transformer only handles discrete-time (or turn-based) sequential decision making scenarios, we generalize it to Continuous-Time Decision Transformer that not only considers the past clinical measurements and treatments but also the timings of previous visits, and learns to suggest the timings of future visits as well as the treatment plan at each visit. Extensive experiments on synthetic datasets and simulators motivated by real-world medical applications demonstrate that Continuous-Time Decision Transformer is able to outperform competitors and has clinical utility in terms of improving patients' health and prolonging their survival by learning high-performance policies from logged data generated using policies of different levels of quality.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"206 ","pages":"6245-6262"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10907982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023967","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
Causal Learning through Deliberate Undersampling. 通过故意减少取样进行因果学习
Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis
{"title":"Causal Learning through Deliberate Undersampling.","authors":"Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Domain scientists interested in causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems. A common and plausible assumption is that higher measurement frequencies are the only way to gain more informative data about the underlying dynamical causal structure. This assumption is a strong driver for designing new, faster instruments, but such instruments might not be feasible or even possible. In this paper, we show that this assumption is incorrect: there are situations in which we can gain additional information about the causal structure by measuring more <i>slowly</i> than our current instruments. We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"213 ","pages":"518-530"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10972601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308206","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
Causal isotonic calibration for heterogeneous treatment effects 异质治疗效果的因果等渗校准
Proceedings of machine learning research Pub Date : 2023-02-27 DOI: 10.48550/arXiv.2302.14011
L. Laan, Ernesto Ulloa-P'erez, M. Carone, Alexander Luedtke
{"title":"Causal isotonic calibration for heterogeneous treatment effects","authors":"L. Laan, Ernesto Ulloa-P'erez, M. Carone, Alexander Luedtke","doi":"10.48550/arXiv.2302.14011","DOIUrl":"https://doi.org/10.48550/arXiv.2302.14011","url":null,"abstract":"We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. In addition, we introduce a novel data-efficient variant of calibration that avoids the need for hold-out calibration sets, which we refer to as cross-calibration. Causal isotonic cross-calibration takes cross-fitted predictors and outputs a single calibrated predictor obtained using all available data. We establish under weak conditions that causal isotonic calibration and cross-calibration both achieve fast doubly-robust calibration rates so long as either the propensity score or outcome regression is estimated well in an appropriate sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm to provide strong distribution-free calibration guarantees while preserving predictive performance.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"202 1","pages":"34831-34854"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48024553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fair admission risk prediction with proportional multicalibration. 比例多重校准的公平入场风险预测。
William G La Cava, Elle Lett, Guangya Wan
{"title":"Fair admission risk prediction with proportional multicalibration.","authors":"William G La Cava,&nbsp;Elle Lett,&nbsp;Guangya Wan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose <i>proportional multicalibration</i>, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its <i>differential calibration</i>, a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"209 ","pages":"350-378"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417639/pdf/nihms-1917236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10008223","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
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. 零膨胀数据的有向图模型和因果发现。
Shiqing Yu, Mathias Drton, Ali Shojaie
{"title":"Directed Graphical Models and Causal Discovery for Zero-Inflated Data.","authors":"Shiqing Yu, Mathias Drton, Ali Shojaie","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>With advances in technology, gene expression measurements from single cells can be used to gain refined insights into regulatory relationships among genes. Directed graphical models are well-suited to explore such (cause-effect) relationships. However, statistical analyses of single cell data are complicated by the fact that the data often show zero-inflated expression patterns. To address this challenge, we propose directed graphical models that are based on Hurdle conditional distributions parametrized in terms of polynomials in parent variables and their 0/1 indicators of being zero or nonzero. While directed graphs for Gaussian models are only identifiable up to an equivalence class in general, we show that, under a natural and weak assumption, the exact directed acyclic graph of our zero-inflated models can be identified. We propose methods for graph recovery, apply our model to real single-cell gene expression data on T helper cells, and show simulated experiments that validate the identifiability and graph estimation methods in practice.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"213 ","pages":"27-67"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725222","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
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