wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As a result, there are more datasets collected using bandit algorithms and with that an increased desire to be able to use these datasets to answer scientific questions like: Did one type of ad increase the click-through rate more or lead to more purchases?
Revised:  As a result, there are more datasets collected using bandit algorithms and with that an increased desire to be able to use these datasets to answer scientific questions like: Did one type of ad lead to more purchases?
Edit intent: clarity
Original:  However, it has been shown that classical statistical approaches , like those based on the ordinary least squares estimator, fail to provide reliable confidence intervals when used with bandit data . Recently methods have been developed to conduct statistical inference using simple models fit to data collected with multi-armed bandits.However there is a lack of general methods for conducting statistical inference using more complex models  .
Revised:  However, it has been shown that classical statistical approaches , like those based on the ordinary least squares estimator, fail to provide reliable confidence intervals when used with data collected with bandit algorithms. Alternative methods have recently been developed for simple models (e.g., comparison of means). Yet there is a lack of general methods for conducting statistical inference using more complex models .
Edit intent: meaning-changed
Original:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- but now with data collected with  (contextual) bandit algorithms.
Revised:  In this work, we develop theory justifying the use of M-estimators -- including least squares and maximum likelihood estimators -- but now with data collected with (contextual) bandit algorithms.
Edit intent: coherence
Original:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- but now with data collected with  (contextual) bandit algorithms.
Revised:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- which includes estimators based on empirical risk minimization as well as maximum likelihood -- but now with data collected with (contextual) bandit algorithms.
Edit intent: meaning-changed
Original:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- but now with data collected with  (contextual) bandit algorithms.
Revised:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- on data collected with (contextual) bandit algorithms.
Edit intent: coherence
Original:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- but now with data collected with  (contextual) bandit algorithms.
Revised:  In this work, we develop theory justifying the use of M-estimation (Van der Vaart, 2000), traditionally used with i.i.d data, to provide inferential methods for a large class of estimators -- including least squares and maximum likelihood estimators -- but now with data collected with adaptive algorithms, including (contextual) bandit algorithms.
Edit intent: clarity
Original:  To do this we generalize the use of adaptive weights pioneered by Hadad et al. (2019) and Deshpande et al. (2018). Specifically, in settings in which the data is collected via a (contextual) bandit algorithm, we prove that certain adaptively weighted M-estimators are uniformly asymptotically normal and demonstrate empirically that we can use their asymptotic distribution to construct reliable confidence regions for a variety of inferential targets.
Revised:  Specifically, we show that M-estimators are uniformly asymptotically normal and demonstrate empirically that we can use their asymptotic distribution to construct reliable confidence regions for a variety of inferential targets.
Edit intent: coherence
Original:  Specifically, in settings in which the data is collected via a (contextual) bandit algorithm, we prove that certain adaptively weighted M-estimators are uniformly asymptotically normal and demonstrate empirically that we can use their asymptotic distribution to construct reliable confidence regions for a variety of inferential targets.
Revised:  Specifically, in settings in which the data is collected via a (contextual) bandit algorithm, we prove that certain adaptively weighted M-estimators , modified with particular adaptive weights, can be used to construct asymptotically valid confidence regions for a variety of inferential targets.
Edit intent: meaning-changed
Original: Point Cloud Registration is a fundamental problem in robotics and computer vision . Due to the limited accuracy in the matching process of 3D keypoints, the presence of outliers, probably in very large numbers, is common in many real-world applications  .
Revised: Point Cloud Registration is a fundamental problem in robotics and computer vision , which aim to estimate the rotation and the transformation between the 3D keypoints, the presence of outliers, probably in very large numbers, is common in many real-world applications .
Edit intent: meaning-changed
Original:  In this paper, we present ICOS (Inlier searching using COmpatible Structures), a novel, efficient and highly robust solution for the correspondence-based  point cloud registration problem .
Revised:  In this paper, we present ICOS (Inlier searching using COmpatible Structures), a novel, efficient and highly robust solution for the correspondence-based rotation search and point cloud registration problem .
Edit intent: meaning-changed
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the two problems where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures .
Edit intent: meaning-changed
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design three time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures .
Edit intent: meaning-changed
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, the first for rotation search, the second for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures .
Edit intent: meaning-changed
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the third for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures .
Edit intent: meaning-changed
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling based on the compatible structures .
Edit intent: fluency
Original:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures  .
Revised:  Specifically, we (i) propose and construct a series of compatible structures for the registration problem where various invariants can be established, and (ii) design two time-efficient frameworks, one for known-scale registration and the other for unknown-scale registration, to filter out outliers and seek inliers from the invariant-constrained random sampling built upon the compatible structures proposed .
Edit intent: clarity
Original:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods,  (ii) ICOS is practical for use in  real-world applications.
Revised:  Through plentiful experiments over standard datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods, (ii) ICOS is practical for use in real-world applications.
Edit intent: clarity
Original:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods,  (ii) ICOS is practical for use in  real-world applications.
Revised:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against over 95 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods, (ii) ICOS is practical for use in real-world applications.
Edit intent: meaning-changed
Original:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods,  (ii) ICOS is practical for use in  real-world applications.
Revised:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers for rotation search and both known-scale and unknown-scale registration , outperforming other state-of-the-art methods, (ii) ICOS is practical for use in real-world applications.
Edit intent: meaning-changed
Original:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods,  (ii) ICOS is practical for use in  real-world applications.
Revised:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods, and (ii) ICOS is practical for use in real-world applications.
Edit intent: coherence
Original:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods,  (ii) ICOS is practical for use in  real-world applications.
Revised:  Through plentiful experiments over standard real datasets, we demonstrate that: (i) our solver ICOS is fast, accurate, robust against as many as 99 \% outliers with nearly 100\% recall ratio of inliers whether the scale is known or unknown , outperforming other state-of-the-art methods, (ii) ICOS is practical for use in multiple real-world applications.
Edit intent: clarity
Original:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Revised:  Based on this consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Edit intent: clarity
Original:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Revised:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of stochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Edit intent: fluency
Original:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Revised:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of a (SDE ) driven by either Brownian or L\'evy stable motion.
Edit intent: clarity
Original:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Revised:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation stochastic differential equations (SDEs ) driven by either Brownian or L\'evy stable motion.
Edit intent: meaning-changed
Original:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or L\'evy stable motion.
Revised:  Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of astochastic differential equation (SDE ) driven by either Brownian or Levy stable motion.
Edit intent: fluency
Original:  Instead, inspired by the long-time correlation emerging in  SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Revised:  Instead, inspired by the short-range correlation emerging in SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Edit intent: meaning-changed
Original:  Instead, inspired by the long-time correlation emerging in  SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Revised:  Instead, inspired by the long-time correlation emerging in the SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Edit intent: fluency
Original:  Instead, inspired by the long-time correlation emerging in  SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Revised:  Instead, inspired by the long-time correlation emerging in SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by \textit{fractional Brownian motion (FBM).
Edit intent: fluency
Original:  The results of the experiments show that in most cases, MaC performs much better than several  state-of-the-art methods, including CART, neural network , stacking, and boosting.
Revised:  The results of the experiments show that in most cases, MaC performs much better than several state-of-the-art methods, including classification and regression trees, neural networks , stacking, and boosting.
Edit intent: meaning-changed
Original:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Revised:  Proposed in this paper is a greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Edit intent: clarity
Original:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Revised:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a doubly CL strategy that contrasts positive samples and negative ones within themselves separately. We realize this strategy with contrastive attraction and contrastive repulsion (CACR) makes the query not only exert a greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Edit intent: meaning-changed
Original:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Revised:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a greater force to attract more distant positive samples towards the query and push closer negative samples away from the query .
Edit intent: clarity
Original:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Revised:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a greater force to both pull more distant positive samples but also do so to repel closer negative samples away from the query .
Edit intent: clarity
Original:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
Revised:  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a greater force to both pull more distant positive samples towards the query and push closer negative samples .
Edit intent: clarity