wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Offline meta-RL removes the need to continuously provide reward supervision because rewards must only be provided once when the offline dataset is generated. In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
Revised:  Offline meta-RL a practical tool for real-world use, offline meta-RL presents additional challenges beyond online meta-RL or standard offline RL settings. Meta-RL learns an exploration strategy that collects data for adapting, and also meta-trains a policy that quickly adapts to data from a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
Edit intent: clarity
Original:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
Revised:  In addition to the challenges of offline RL , a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
Edit intent: clarity
Original:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect.
Revised:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task . Since this policy was meta-trained on a fixed, offline dataset, it might behave unpredictably when adapting to data collected by the learned exploration strategies collect.
Edit intent: meaning-changed
Original:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategies collect. Unlike the online setting, the adaptation and exploration strategies cannot effectively adapt to each other, resulting in poor performance. In this paper , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels , to bridge this distribution shift problem.
Revised:  In addition to the challenges of offline RL , a unique distribution shift is present in meta RL: agents learn exploration strategies that can gather the experience needed to learn a new task , and also learn adaptation strategies that work well when presented with the trajectories in the dataset, but the adaptation strategies are not adapted to the data distribution that the learned exploration strategy, which differs systematically from the offline data and thus induces distributional shift. We do not want to remove this distributional shift by simply adopting a conservative exploration strategy, because learning an exploration strategy enables an agent to collect better data for faster adaptation. Instead , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels , to bridge this distribution shift problem.
Edit intent: meaning-changed
Original:  In this paper , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels , to bridge this distribution shift problem.
Revised:  In this paper , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any reward labels to bridge this distribution shift problem.
Edit intent: clarity
Original:  In this paper , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels , to bridge this distribution shift problem. Our method uses the offline data to learn the distribution of reward functions, which is then sampled to self-supervise reward labels for the additional online data. By removing the need to provide reward labels for the online experience, our approach can be more practical to use in settings where reward supervision would otherwise be provided manually .
Revised:  In this paper , we propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy, and then collects additional unsupervised online data, without any ground truth reward labels , to bridge this distribution shift . By not requiring reward labels for the online experience, our approach can be more practical to use in settings where reward supervision would otherwise be provided manually .
Edit intent: coherence
Original:  We compare our method to prior work on offline meta-RL on simulated robot locomotion and manipulation tasks and find that using additional data and self-generated rewards significantly improves an agent's ability to generalize .
Revised:  We compare our method to prior work on offline meta-RL on simulated robot locomotion and manipulation tasks and find that using additional unsupervised online data collection leads to a dramatic improvement in the adaptive capabilities of the meta-trained policies, matching the performance of fully online meta-RL on a range of challenging domains that require generalization to new tasks .
Edit intent: meaning-changed
Original:  It is motivated by the fact that the concepts URLanization of scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of them is centered on this topic and presents  a simple and easily URLanization.
Revised:  It is motivated by the fact that the concepts that are used URLanize scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of them is centered on this topic and presents a simple and easily URLanization.
Edit intent: clarity
Original:  It is motivated by the fact that the concepts URLanization of scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of them is centered on this topic and presents  a simple and easily URLanization.
Revised:  It is motivated by the fact that the concepts URLanization of scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of these ontologies is centered on this topic and presents a simple and easily URLanization.
Edit intent: clarity
Original:  It is motivated by the fact that the concepts URLanization of scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of them is centered on this topic and presents  a simple and easily URLanization.
Revised:  It is motivated by the fact that the concepts URLanization of scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of them is centered on this topic and presents them in a simple and easily URLanization.
Edit intent: clarity
Original:  Finally, we present a pipeline for intent classification and entity extraction which achieves reasonable performance (accuracy: 83.02 %DIFDELCMD < \\%%% %DIF < , precision: 80.82\\%, recall: 83.02\\%, F1-score: 80\\%).abstract 
Revised:  Finally, we present a pipeline for intent classification and entity extraction which achieves reasonable performance (accuracy: 83.02 %DIFDELCMD < \\%%% %DIF < , precision: 80.82\\%, recall: 83.02\\%, F1-score: 80\\%).abstract \%, precision: 80.82\%, recall: 83.02\%, F1-score: 80\%).
Edit intent: meaning-changed
Original: A significant economic cost for many companies that operate with fleets of diesel and petrol vehicles is related to fuel consumption. Consumption can be reduced by acting over some factors, like driving behaviour style.
Revised: A significant economic cost for many companies that operate with fleets of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style.
Edit intent: meaning-changed
Original:  Improving these factors can reduce the fuel usage of a vehicle without changing other aspects, such as planned routes or stops. This mitigates economic costs while reducing emissions associated to fuel consumption. In this paper we show how Explainable Artificial Intelligence (XAI) is useful for quantifying the impact that fuel factors have on the consumption of a vehicle fleet. We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge .
Revised:  In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge .
Edit intent: clarity
Original:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge .
Revised:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between the input factors and fuel consumption, and then assess quality of the explanations using prior domain knowledge .
Edit intent: clarity
Original:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, and then assess quality of the explanations using prior domain knowledge . We work with real-world industry datasets that represent different types of vehicles, from passenger cars to heavy-duty trucks .
Revised:  We use Explainable Boosting Machines (EBM), trained over different features in order to both model and explain the relationship between them and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70\% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12\% and 15\% in a large fleet (more than 1000 vehicles) .
Edit intent: meaning-changed
Original: We investigate and extend the results of Golts and Jones (2009) that an alpha-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix.
Revised: We investigate and extend the results of Golts and Jones (2009) that an \alpha-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix.
Edit intent: fluency
Original:  In large-scale optimization, when either forming or storing Hessian matrices are prohibitively expensive, quasi-Newton methods are often  used in lieu of Newton's method because they make use of first-order information to approximate the true Hessian.
Revised:  In large-scale optimization, when either forming or storing Hessian matrices are prohibitively expensive, quasi-Newton methods are often . used in lieu of Newton's method because they make use of first-order information to approximate the true Hessian.
Edit intent: fluency
Original:  In large-scale optimization, when either forming or storing Hessian matrices are prohibitively expensive, quasi-Newton methods are often  used in lieu of Newton's method because they make use of first-order information to approximate the true Hessian.
Revised:  In large-scale optimization, when either forming or storing Hessian matrices are prohibitively expensive, quasi-Newton methods are often used in lieu of Newton's method because they only require first-order information to approximate the true Hessian.
Edit intent: clarity
Original:  Previous algorithms for the uniform sampling task were based on a decomposition of H into a collection of odd cycles and stars, denoted D^*(H)=\{O_{k_1}, \ldots, O_{k_q}, S_{p_1}, \ldots, S_{p_\ell}\}.
Revised:  Previous algorithms for these tasks were based on a decomposition of H into a collection of odd cycles and stars, denoted D^*(H)=\{O_{k_1}, \ldots, O_{k_q}, S_{p_1}, \ldots, S_{p_\ell}\}.
Edit intent: clarity
Original:  We present a new algorithm for sampling  arbitrary motifs which, up to %DIFDELCMD < \poly%%%  (\log n) factors, for any motif H whose decomposition contains at least two components or at least one star, is always preferable .
Revised:  We present a new algorithm for sampling and approximately counting arbitrary motifs which, up to %DIFDELCMD < \poly%%% (\log n) factors, for any motif H whose decomposition contains at least two components or at least one star, is always preferable .
Edit intent: meaning-changed
Original:  We present a new algorithm for sampling  arbitrary motifs which, up to %DIFDELCMD < \poly%%%  (\log n) factors, for any motif H whose decomposition contains at least two components or at least one star, is always preferable .
Revised:  We present a new algorithm for sampling arbitrary motifs which, up to %DIFDELCMD < \poly%%% poly (\log n) factors, for any motif H whose decomposition contains at least two components or at least one star, is always preferable .
Edit intent: meaning-changed
Original:  We present a new algorithm for sampling  arbitrary motifs which, up to %DIFDELCMD < \poly%%%  (\log n) factors, for any motif H whose decomposition contains at least two components or at least one star, is always preferable .
Revised:  We present a new algorithm for sampling arbitrary motifs which, up to %DIFDELCMD < \poly%%% (\log n) factors, is always at least as good as previous results, and for most graphs G is strictly better .
Edit intent: meaning-changed
Original:  Finally, we prove that this algorithm is decomposition-optimal for decompositions that contain at least one odd cycle . That is, we prove that for any decomposition D that contains at least one odd cycle, there exists a motif H_{D . These are the first lower bounds for motifs H with a nontrivial decomposition, i.e., motifs that have more than a single component in their decomposition.
Revised:  Finally, we prove that this algorithm is decomposition-optimal for decompositions that contain at least one odd cycle . These are the first lower bounds for motifs H with a nontrivial decomposition, i.e., motifs that have more than a single component in their decomposition.
Edit intent: coherence
Original:  We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution , also showing how these techniques can be made computationally tractableby leveraging tools from meta-learning.
Revised:  We propose a novel mechanism for obtaining these calibrated, uncertainty-aware classifiers based on an amortized technique for computing the normalized maximum likelihood (NML) distribution . To make this tractable, we propose a novel method for computing the NML distribution by using meta-learning.
Edit intent: coherence
Original:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site  , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Revised:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on in-silico mutagenesis of residues of the antibody binding site , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Edit intent: fluency
Original:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site  , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Revised:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of antibody binding site , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Edit intent: clarity
Original:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site  , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Revised:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site residues , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Edit intent: meaning-changed
Original:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site  , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and its cognate antigen epitope .
Revised:  Focusing on the peculiar case of antibody-antigen interaction, we designed a novel computational strategy based on the in-silico mutagenesis of residues of the antibody binding site , where a Monte Carlo procedure aims at increasing the shape complementarity between the antibody paratope and a given epitope on a target protein surface .
Edit intent: clarity
Original:  Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to iteratively find near-optimal policies of the  optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy.
Revised:  Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to iteratively find near-optimal policies of the adaptive optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy.
Edit intent: meaning-changed
Original:  Although there exists plentiful generalization analysis of ERM and SGD for supervised learning, current theoretical understandings of ERM and SGD are either have stronger assumptions in convex learning, e.g., strong convexity condition , or show slow rates and less studied in nonconvex learning.
Revised:  Although there exists plentiful generalization analysis of ERM and SGD for supervised learning, current theoretical understandings of ERM and SGD either have stronger assumptions in convex learning, e.g., strong convexity condition , or show slow rates and less studied in nonconvex learning.
Edit intent: fluency
Original:  Although there exists plentiful generalization analysis of ERM and SGD for supervised learning, current theoretical understandings of ERM and SGD are either have stronger assumptions in convex learning, e.g., strong convexity condition , or show slow rates and less studied in nonconvex learning.
Revised:  Although there exists plentiful generalization analysis of ERM and SGD for supervised learning, current theoretical understandings of ERM and SGD are either have stronger assumptions in convex learning, e.g., strong convexity , or show slow rates and less studied in nonconvex learning.
Edit intent: clarity
Original:  It is notable that our analysis span two popular theoretical viewpoints: stability and uniform convergence. To be specific  , in stability regime, we present high probability  rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Revised:  It is notable that our analysis span two popular theoretical viewpoints: stability , in stability regime, we present high probability rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Edit intent: coherence
Original:  To be specific  , in stability regime, we present high probability  rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Revised:  To be specific and , in stability regime, we present high probability rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Edit intent: coherence
Original:  To be specific  , in stability regime, we present high probability  rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Revised:  To be specific uniform convergence , in stability regime, we present high probability rates of order O (1/n) w.r.t. the sample size n for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order O (1/n) in nonconvex learning, rather than in expectation.
Edit intent: meaning-changed