wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Also, for k-means clustering , we give a faster alternative for retaining worst-case guarantees. 2) We show empirically that learned sketches are reliable in improving approximation accuracy, with comparison against "non-learned" sketching baselines. 3) We introduce a greedy algorithm for optimizing the location of the nonzero entries of a sparse sketch and prove guarantees for certain distributions on the LRA task. Previous work only looked at optimizing the values rather than the locations. Also, we show empirically that it further improves learned sketch performance . \\%DIF > and 20\\%, respectively.
Revised:  Also, for k-means clustering . \\%DIF > and 20\\%, respectively.
Edit intent: meaning-changed
Original:  We consider classical sketching algorithms which first  compress data by multiplication with a random "sketch matrix". Our work improves and extends the "learned sketch " paradigm , in which sketch matrices are optimized to yield better expected performance.This technique has only been used for a suboptimal variant of sketched low-rank decomposition (LRD). Our work extends the problem coverage to optimal sketched LRD, least-squares regression (LS), and ] k-means clustering .
Revised:  We consider classical sketching algorithms which first compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem, e.g., low rank approximation. In the learning-based sketching paradigm proposed by Indyk et al. ] k-means clustering .
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Original:  Our work extends the problem coverage to optimal sketched LRD, least-squares regression (LS), and ] k-means clustering .
Revised:  Our work extends the problem coverage to optimal sketched LRD, least-squares regression (LS), and ], the sketch matrix is found by choosing a random sparse matrix, e.g., the CountSketch, and then updating the values of the non-zero entries by running gradient descent on a training data set. Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned. In this work we propose the first learning algorithm that also optimizes the locations of the non-zero entries. We show this algorithm gives better accuracy for low rank approximation than previous work, and apply it to other problems such as k-means clustering .
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Original:  Our work extends the problem coverage to optimal sketched LRD, least-squares regression (LS), and ] k-means clustering . We improve sketch learning for all three problems and very significantly for LS and LRD: experimental performance increases by 12%DIFDELCMD < \\%%% %DIF < and 20\\%, respectively.
Revised:  Our work extends the problem coverage to optimal sketched LRD, least-squares regression (LS), and ] k-means clustering %DIFDELCMD < \\%%% %DIF < and 20\\%, respectively.
Edit intent: coherence
Original:  We prove the worst-case property for each of the problems and their modified algorithms.abstract 
Revised:  We prove the worst-case property for each of the problems and their modified algorithms.abstract for the first time. We show that our algorithm is provably better in the spiked covariance model and for Zipfian matrices. We also show the importance of the sketch monotonicity property for combining learned sketches. Our empirical results show the importance of optimizing not only the values of the non-zero entries but also their positions.
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Original:  We initiate a study of the interplay between exploration and competition: how such platforms balance the exploration for learning and the competition for consumers. Here consumers play three distinct roles: they are customers that generate revenue, they are sources of data for learning, and they are self-interested agents which choose among the competing platforms.
Revised:  We initiate a study of the interplay between exploration and competition: how such platforms balance the exploration for learning and the competition for users. Here users play three distinct roles: they are customers that generate revenue, they are sources of data for learning, and they are self-interested agents which choose among the competing platforms.
Edit intent: clarity
Original:  We consider a stylized duopoly model in which two firms face the same problem instance of multi-armed bandits . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit instance only if it is chosen.
Revised:  We consider a stylized duopoly model in which two firms face the same multi-armed bandits . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit instance only if it is chosen.
Edit intent: clarity
Original:  We consider a stylized duopoly model in which two firms face the same problem instance of multi-armed bandits . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit instance only if it is chosen.
Revised:  We consider a stylized duopoly model in which two firms face the same problem instance of multi-armed bandit problem . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit instance only if it is chosen.
Edit intent: clarity
Original:  We consider a stylized duopoly model in which two firms face the same problem instance of multi-armed bandits . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit instance only if it is chosen.
Revised:  We consider a stylized duopoly model in which two firms face the same problem instance of multi-armed bandits . Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen.
Edit intent: fluency
Original:  Our findings are closely related to the "competition vs. innovation" relationship, a well-studied theme in economics. They also elucidate the first-mover advantage in the digital economy by exploring the role that data can play as a barrier to entry in online markets .
Revised:  Our findings are closely related to the "competition vs. innovation" relationship, and elucidate the first-mover advantage in the digital economy by exploring the role that data can play as a barrier to entry in online markets .
Edit intent: coherence
Original: Learned Data Structures, a recent research area overlapping Machine Learning and Data Structure, uses learning from data in order to improve the time/space performance of classic data structures, e.g ., Search Trees, Hash Tables and Bloom Filters.
Revised: Sorted Table Search Procedures are the quintessential query-answering tool, with widespread usage that now includes also Web Applications, e.g ., Search Trees, Hash Tables and Bloom Filters.
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Original: Learned Data Structures, a recent research area overlapping Machine Learning and Data Structure, uses learning from data in order to improve the time/space performance of classic data structures, e.g ., Search Trees, Hash Tables and Bloom Filters. It is also claimed that the use of GPUs for the learning phase contributes to make those approaches widespread. Yet, the role of GPU computing with respect to Learned Data Structures is unclear. Moreover, none of such studies has been devoted to algorithms for Sorted Table Search, that are both fundamental and still widely used in several application domains. We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures  and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter.
Revised: Learned Data Structures, a recent research area overlapping Machine Learning and Data Structure, uses learning from data in order to improve the time/space performance of classic data structures, e.g , Search Engines (Google Chrome) and ad Bidding Systems (AppNexus). Speeding them up, at very little cost in space, is still a quite significant achievement. Here we study to what extend Machine Learning Techniques can contribute to obtain such a speed-up via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter.
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Original:  We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures  and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter.
Revised:  We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures , with different Data Layouts, and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter.
Edit intent: meaning-changed
Original:  We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures  and their Learned counterparts developed here , obtaining valuable methodological insights into the use of those latter. Specifically, we characterize the scenarios in which those latter can be profitably used with respect to the former, accounting for both CPU and GPU computing.
Revised:  We provide such a study here, via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures and their Learned counterparts developed here . We characterize the scenarios in which those latter can be profitably used with respect to the former, accounting for both CPU and GPU computing.
Edit intent: clarity
Original:  We also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one  and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
Revised:  Our approach contributes also to the study of Learned Data Structures, a recent proposal to improve the time/space performance of fundamental Data Structures, e.g., B-trees, Hash Tables, Bloom Filters. Indeed, we also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
Edit intent: meaning-changed
Original:  We also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one  and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
Revised:  We also formalize an Algorithmic Paradigm of Learned Dichotomic Sorted Table Search procedures that naturally complements the Learned one proposed here and that characterizes most of the known Sorted Table Search Procedures as having a "learning phase" that approximates Simple Linear Regression.
Edit intent: meaning-changed
Original:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Revised:  To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Edit intent: clarity
Original:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Revised:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Edit intent: coherence
Original:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Revised:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Edit intent: fluency
Original:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their eight attributes such as temporal information and negations from clinical text.
Revised:  To this end, the existing clinical NLP tool CLAMP was quickly adapted to COVID-19 information and generated an automated tool called COVID-19 SignSym, which can extract and signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.
Edit intent: meaning-changed
Original:  The extracted information is also mapped to standard clinical concepts in the  common data model  of OHDSI OMOP.
Revised:  The extracted information is also mapped to standard clinical concepts in the common data model . A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of OHDSI OMOP.
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Original:  The extracted information is also mapped to standard clinical concepts in the  common data model  of OHDSI OMOP. Evaluation on clinical notes and medical dialogues demonstrated promising results. It is freely accessible to the  community as a downloadable package of APIs URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.
Revised:  The extracted information is also mapped to standard clinical concepts in the common data model of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 Sign-Sym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the community as a downloadable package of APIs URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.
Edit intent: meaning-changed
Original:  It is freely accessible to the  community as a downloadable package of APIs URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.
Revised:  It is freely accessible to the research community as a downloadable package of APIs URL We believe COVID-19 SignSym will provide fundamental supports to the secondary use of EHRs, thus accelerating the global research of COVID-19.
Edit intent: meaning-changed
Original:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Revised:  We determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Edit intent: coherence
Original:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Revised:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Edit intent: clarity
Original:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Revised:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database.
Edit intent: clarity
Original:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe ) database. We fit a set of probability distribution functions and estimate a symptom-onset-to-death mean of 15.2 days for Brazil, which is lower than earlier estimates of 17.8 days based on early Chinese data.
Revised:  Here we determine epidemiological distributions for patients hospitalised with COVID-19 using a large dataset ( range N=21{,}000-157{,}000) from the Brazilian SIVEP-Gripe ( Sistema de Informac\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe database.
Edit intent: meaning-changed
Original:  A joint Bayesian subnational model  is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2-17.8 days across the different states  .
Revised:  A joint Bayesian subnational model with partial pooling is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2-17.8 days across the different states .
Edit intent: meaning-changed
Original:  A joint Bayesian subnational model  is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2-17.8 days across the different states  .
Revised:  A joint Bayesian subnational model is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2-17.8 days across the different states , and a mean of 15.2 days for Brazil .
Edit intent: meaning-changed
Original:  We find strong evidence in favour of specific probability distribution function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalised log-normal for onset-to-hospital-discharge .
Revised:  We find strong evidence in favour of specific probability density function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalised log-normal for onset-to-hospital-discharge .
Edit intent: clarity
Original:  We find strong evidence in favour of specific probability distribution function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalised log-normal for onset-to-hospital-discharge .
Revised:  We find strong evidence in favour of specific probability distribution function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalised log-normal for onset-to-hospital-admission .
Edit intent: meaning-changed
Original:  These conditions require a combination of assumptions that restrict both the unobserved heterogeneity in treatment assignment and how the instruments target the treatments . We introduce the concept of filtered treatment, which takes into account limitations in the analyst's information  .
Revised:  These conditions require a combination of assumptions that restrict both the unobserved heterogeneity in treatment assignment and how the instruments target the treatments , and optionally (iii) the extent to which counterfactual averages are heterogeneous. We allow for limitations in the analyst's information .
Edit intent: meaning-changed
Original:  An assignment for the constraint is an allocation of subsets of items to the bins , such that the total weight of items assigned to each bin is bounded by the bin capacity. We study modular (linear) and submodular maximization problems subject to a constant number of (i.e., d-resource) multiple knapsack constraints, in which a solution is a subset of items, along with an assignment of the selected items for each of the d multiple knapsack constraints.
Revised:  An assignment for the constraint is an allocation of subsets of items to the bins which adheres to bin capacities. In this paper we present a unified algorithm that yields efficient approximations for a wide class of submodular and modular optimization problems involving multiple knapsack constraints.
Edit intent: clarity
Original:  Our results include a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any d \geq 2.
Revised:  One notable example is a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any d \geq 2.
Edit intent: clarity
Original:  Our results include a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any d \geq 2.
Revised:  Our results include a polynomial time approximation scheme for Multiple-Choice Multiple Knapsack, improving upon the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any d \geq 2.
Edit intent: clarity
Original:  Our results include a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any d \geq 2.
Revised:  Our results include a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known ratio of 2.
Edit intent: meaning-changed