wanyu/IteraTeR_full_sent huggingface dataset:
Original: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Revised: Online learning in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Edit intent: clarity
Original: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Revised: We study regret minimization problems in a two-sided matching market , with demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Edit intent: clarity
Original: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Revised: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents continuously competing to be matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations.
Edit intent: clarity
Original: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations. Such markets abstract online matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al.
Revised: We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al.
Edit intent: clarity
Original:  Such markets abstract online matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al. \mbox{%DIFAUXCMD matching_bandits the demand side admits a unique stable matching equilibrium in the system .
Revised:  Such markets abstract online matching platforms ( for e.g. UpWork, TaskRabbit) . We study the decentralized serial dictatorship setting, a two-sided matching market where the demand side admits a unique stable matching equilibrium in the system .
Edit intent: meaning-changed
Original:  We design the first decentralized algorithm -%DIFDELCMD < \fullname%%% \; (%DIFDELCMD < \name%%%  ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Revised:  We design the first decentralized algorithm -%DIFDELCMD < \fullname%%% %DIFDELCMD < \name%%% ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Edit intent: fluency
Original:  (%DIFDELCMD < \name%%%  ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Revised:  (%DIFDELCMD < \name%%% -- UCB with Decentralized Dominant-arm Deletion (UCB-D3 ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Edit intent: meaning-changed
Original:  (%DIFDELCMD < \name%%%  ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Revised:  (%DIFDELCMD < \name%%% ), for the agents, that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Edit intent: clarity
Original:  (%DIFDELCMD < \name%%%  ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Revised:  (%DIFDELCMD < \name%%% ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon }\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Edit intent: meaning-changed
Original:  (%DIFDELCMD < \name%%%  ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% \;
Revised:  (%DIFDELCMD < \name%%% ), for matching bandits under uniform valuation that does not require any knowledge of reward gaps or time horizon , and thus partially resolves an open question in \mbox{%DIFAUXCMD \cite{matching_bandits}\hspace{0pt}%DIFAUXCMD . }%DIFDELCMD < \name%%% . UCB-D3
Edit intent: meaning-changed
Original:  Finally, the preferred arm is broadcast in a decentralized fashion to other agents through {\em pure exploitation} in (N-1)K rounds with N agents and K arms.
Revised:  higher ranked agents, and play only from the non-dominated arms according to the UCB. At the end of the phase, agents broadcast in a decentralized fashion to other agents through {\em pure exploitation} in (N-1)K rounds with N agents and K arms.
Edit intent: meaning-changed
Original:  Comparing the obtained reward with respect to the unique stable matching, we show that%DIFDELCMD < \name%%% \; achieves O(\log(T)/\Delta^2) regret in T rounds, where \Delta is the minimum gap across all agents and arms. We provide a (orderwise) matching regret lower-bound .
Revised:  Comparing the obtained reward with respect to the unique stable matching, we show that%DIFDELCMD < \name%%% . We prove both, a new regret lower bound for the decentralized serial dictatorship model, and that UCB-D3 is order optimal .
Edit intent: meaning-changed
Original:  Simulation results demonstrate that the balanced non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based and the signed non-backtracking matrix-based algorithm. It shows great potential to detect communities with or without overlap.
Revised:  Simulation results demonstrate that the balanced non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based and the signed non-backtracking matrix. The proposed (improved) matrix shows great potential to detect communities with or without overlap.
Edit intent: meaning-changed
Original:  Theoretically, pay transparency represents an information shock that alters the bargaining power of male and female employees vis-\`a-vis the firm in an asymmetric way. As women are currently underpaid , this shock may improve women's relative  outcomes.
Revised:  Theoretically, pay transparency represents an information shock that alters the bargaining power of male and female employees vis-\`a-vis the firm in opposite ways. Coupled with the potential negative effects of unequal pay on firms' reputation , this shock may improve women's relative outcomes.
Edit intent: meaning-changed
Original:  Additionally, it leads to a 2.8 percent decrease in male real hourly pay in treated firms compared to control ones. Combining the difference-in-difference strategy with a text analysis of job listings, we also find suggestive evidence that treated firms in industries with a high gender pay gap become more likely to post wage information than firms in the control group .
Revised:  Additionally, it leads to a 2.8 percent decrease in male real hourly pay , reducing the pre-policy gender pay gap by 15 percent. Third, combining the difference-in-differences strategy with a text analysis of job listings, we also find suggestive evidence that treated firms in industries with a high gender pay gap become more likely to post wage information than firms in the control group .
Edit intent: meaning-changed
Original:  Combining the difference-in-difference strategy with a text analysis of job listings, we also find suggestive evidence that treated firms in industries with a high gender pay gap become more likely to post wage information than firms in the control group .
Revised:  Combining the difference-in-difference strategy with a text analysis of job listings, we find suggestive evidence that treated firms in industries with a high gender pay gap become more likely to post wage information than firms in the control group .
Edit intent: clarity
Original:  Combining the difference-in-difference strategy with a text analysis of job listings, we also find suggestive evidence that treated firms in industries with a high gender pay gap become more likely to post wage information than firms in the control group .
Revised:  Combining the difference-in-difference strategy with a text analysis of job listings, we also find suggestive evidence that treated firms adopt female-friendly hiring practices in ads for high-gender-pay-gap occupations. Fourth, a reputation motive seems to drive employers' reactions, as firms publishing worse gender equality indicators score lower in YouGov Women's Rankings. Moreover, publicly listed firms experience a 35-basis-point average fall in cumulative abnormal returns in the days following their publication of gender equality data .
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Original: We propose a knowledge-enhanced approach, ERNIE-ViL,  to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Revised: We propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Edit intent: meaning-changed
Original: We propose a knowledge-enhanced approach, ERNIE-ViL,  to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Revised: We propose a knowledge-enhanced approach, ERNIE-ViL, to learn joint representations of vision-language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Edit intent: fluency
Original: We propose a knowledge-enhanced approach, ERNIE-ViL,  to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Revised: We propose a knowledge-enhanced approach, ERNIE-ViL, to learn joint representations of vision and language . ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Edit intent: clarity
Original: We propose a knowledge-enhanced approach, ERNIE-ViL,  to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects in visual scenes ) across vision and language, which are essential to vision-language cross-modal tasks.
Revised: We propose a knowledge-enhanced approach, ERNIE-ViL, to learn joint representations of vision and language . ERNIE-ViL tries to construct the detailed semantic connections (objects, attributes of objects and relationships between objects ) across vision and language, which are essential to vision-language cross-modal tasks.
Edit intent: clarity
Original:  Incorporating knowledge from scene graphs , ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction  in the pre-training phase.
Revised:  Utilizing scene graphs of visual scenes , ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction in the pre-training phase.
Edit intent: meaning-changed
Original:  Incorporating knowledge from scene graphs , ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction  in the pre-training phase.
Revised:  Incorporating knowledge from scene graphs , ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase.
Edit intent: fluency
Original:  Pre-trained on two large image-text alignment datasets(Conceptual Captions and SBU), ERNIE-ViL learns better and more robust joint representations. It achieves state-of-the-art performance on 5 vision-language downstream tasksafter fine-tuning ERNIE-ViL.
Revised:  Pre-trained on two large image-text alignment datasets(Conceptual Captions and SBU), ERNIE-ViL on 5 vision-language downstream tasksafter fine-tuning ERNIE-ViL.
Edit intent: clarity
Original:  It achieves state-of-the-art performance on 5 vision-language downstream tasksafter fine-tuning ERNIE-ViL. Furthermore, it ranked the 1st place on the VCR leader-board with an absolute improvement of 3.7\%.
Revised:  It achieves state-of-the-art performance on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leader-board with an absolute improvement of 3.7\%.
Edit intent: clarity
Original:  In this work, we propose an alternative way to identify individuals with anxiety and further estimate their levels of anxiety using private online activities histories from YouTube and Google search engine, platforms that are used by millions of people daily.
Revised:  In this work, we propose an alternative way to identify individuals with anxiety and further estimate their levels of anxiety using private online activities histories from YouTube and the Google Search engine, platforms that are used by millions of people daily.
Edit intent: fluency
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  Using those, we were able to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Edit intent: coherence
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  We managed to train models that (i) identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Edit intent: clarity
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  We managed to train models that not only identify individuals having anxiety disorder with an average F1 score of 0.83 and (ii) assess the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Edit intent: meaning-changed
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety by predicting the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Edit intent: clarity
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores (ranges from 0 to 21) with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Edit intent: meaning-changed
Original:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
Revised:  We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores with a mean square error of 1.87 based on the ubiquitous individual-level online engagement data. Our proposed anxiety assessment framework is cost-effective, time-saving, scalable, and opens the door for it to be deployed in real-world clinical settings, empowering care providers and therapists to learn about anxiety disorders of patients non-invasively at any moment in time .
Edit intent: coherence
Original:  Eigensolvers involving complex moments for linear square matrix eigenproblems are paid much attention in the eigencomputation due to its coarse-grained parallelizability. Such solvers determine all finite eigenvalues in a prescribed region in the complex plane and the corresponding eigenvectors.
Revised:  Eigensolvers involving complex moments for linear square matrix eigenproblems , eigensolvers involving complex moments can determine all the eigenvalues in a prescribed region in the complex plane and the corresponding eigenvectors.
Edit intent: clarity
Original:  For extracting eigencomponents from random vectors or matrices , a complex moment forms a filter that allows desired eigencomponents to pass through. The challenge is to extend the definition of a complex matrix moment to the non-square case .
Revised:  For extracting eigencomponents from random vectors or matrices . This study extends a projection method for regular matrix pencils to the singular nonsquare case. Extending the definition of a complex matrix moment to the non-square case .
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Original:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is  more robust and efficient , in particular, in parallel. Further , the proposed method does not fail for the existence of many pairs of extremely close eigenvalues and breaks the previous barrier of problem size .
Revised:  Numerical experiments on large matrix pencils illustrate the method , comparing with previous methods, and show that the new method is more robust and efficient and is conjectured by experiment results to be more efficient in parallelized settings. Notably , the proposed method does not fail for the existence of many pairs of extremely close eigenvalues and breaks the previous barrier of problem size .
Edit intent: meaning-changed
Original: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to efficiently find a minimizer of f using at most  O(n (n + \log(R))) calls to SO (n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Revised: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to find an exact minimizer of f using at most O(n (n + \log(R))) calls to SO (n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Edit intent: clarity
Original: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to efficiently find a minimizer of f using at most  O(n (n + \log(R))) calls to SO (n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Revised: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to efficiently find a minimizer of f using at most (a) O(n (n + \log(R))) calls to SO (n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Edit intent: fluency
Original: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to efficiently find a minimizer of f using at most  O(n (n + \log(R))) calls to SO (n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Revised: Given a separation oracle SO for a convex function f that has an integral minimizer inside a box with radius R, we show how to efficiently find a minimizer of f using at most O(n (n + \log(R))) calls to SO and \mathsf{poly(n, \log(R)) arithmetic operations, or (b) O(n \log(nR)) calls to SO and \exp(n) \cdot poly(\log(R)) arithmetic operations} .
Edit intent: meaning-changed