wanyu/IteraTeR_v2 huggingface dataset:
Original:  We propose metrics to quantify the nominal performance of a classifier with abstaining option and its robustness against adversarial perturbations.
Revised:  We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations.
Edit intent: fluency
Original:  We show that there exist a tradeoff between the two metrics regardless of what method is used to choose the abstaining region.
Revised:  We show that there exist a tradeoff between the two metrics regardless of what method is used to choose the abstain region.
Edit intent: fluency
Original:  Our results imply that the robustness of a classifier with abstaining can only be improved at the expense of its nominal performance.
Revised:  Our results imply that the robustness of a classifier with an abstain option can only be improved at the expense of its nominal performance.
Edit intent: clarity
Original:  Further, we provide necessary conditions to design the abstaining region for a 1-dimensional binary classification problem.
Revised:  Further, we provide necessary conditions to design the abstain region for a 1-dimensional binary classification problem.
Edit intent: fluency
Original:  Further, we provide necessary conditions to design the abstaining region for a 1-dimensional binary classification problem.
Revised:  Further, we provide necessary conditions to design the abstaining region for a 1- dimensional binary classification problem.
Edit intent: fluency
Original:  The research focuses on  estimation of the noise under various conditions, since  noise estimation is considered equivalent to errors minimization.
Revised:  The research focuses on the task of estimation of the noise under various conditions, since noise estimation is considered equivalent to errors minimization.
Edit intent: clarity
Original:  The research focuses on  estimation of the noise under various conditions, since  noise estimation is considered equivalent to errors minimization.
Revised:  The research focuses on estimation of the noise under various conditions, since precise noise estimation is considered equivalent to errors minimization.
Edit intent: clarity
Original:  However, we show that even a seemingly small violation of KF assumptions can significantly modify the effective noise, breaking the equivalence between the tasks and making noise estimation a highly sub-optimal strategy.
Revised:  However, we show that even a small violation of KF assumptions can significantly modify the effective noise, breaking the equivalence between the tasks and making noise estimation a highly sub-optimal strategy.
Edit intent: clarity
Original:  We suggest a method (based on Cholesky decomposition) to apply gradient-based optimization efficiently to the symmetric and positive-definite (SPD) parameters of KF , so that KF can be optimized similarly to common neural networks.
Revised:  We suggest a method (based on Cholesky decomposition) to apply gradient-based optimization efficiently to the symmetric and positive-definite parameters of KF , so that KF can be optimized similarly to common neural networks.
Edit intent: clarity
Original:  For Radar tracking we also show how a non-linear neural-network-based model can seem to reduce the tracking errors significantly compared to a KF - and how this reduction entirely vanishes once the KF is optimized.
Revised:  For Radar tracking we also show how a non-linear neural-network-based model can seem to reduce the errors significantly compared to a KF - and how this reduction entirely vanishes once the KF is optimized.
Edit intent: clarity
Original:  For Radar tracking we also show how a non-linear neural-network-based model can seem to reduce the tracking errors significantly compared to a KF - and how this reduction entirely vanishes once the KF is optimized.
Revised:  For Radar tracking we also show how a non-linear neural-network-based model can seem to reduce the tracking errors significantly compared to a KF - and how this reduction vanishes once the KF is optimized.
Edit intent: clarity
Original:  Through a detailed case-study, we also demonstrate that KF requires non-trivial design-decisions to be made, and that parameters optimization makes KF more robust to these decisions .
Revised:  This indicates how complicated models can be wrongly identified as superior to KF, while in fact they were merely over-optimized .
Edit intent: coherence
Original: Determining the noise parameters of a Kalman Filter (KF) has been researched for decades.
Revised: Determining the noise parameters of a Kalman Filter (KF) has been studied for decades.
Edit intent: clarity
Original:  The research focuses on the task of estimation of the noise under various conditions, since precise noise estimation is considered equivalent to errors minimization .
Revised:  A huge body of research focuses on the task of estimation of the noise under various conditions, since precise noise estimation is considered equivalent to errors minimization .
Edit intent: clarity
Original:  The research focuses on the task of estimation of the noise under various conditions, since precise noise estimation is considered equivalent to errors minimization .
Revised:  The research focuses on the task of estimation of the noise under various conditions, since precise noise estimation is considered equivalent to minimization of the filtering errors .
Edit intent: clarity
Original:  However, we show that even a small violation of  KF assumptions can significantly modify the effective noise, breaking the equivalence between the tasks and making noise estimation an inferior strategy.
Revised:  However, we show that even a small violation of the KF assumptions can significantly modify the effective noise, breaking the equivalence between the tasks and making noise estimation an inferior strategy.
Edit intent: fluency
Original:  To that end, we use a simple parameterization to apply gradient-based optimization efficiently to the symmetric and positive-definite parameters of KF.
Revised:  To that end, we apply gradient-based optimization efficiently to the symmetric and positive-definite parameters of KF.
Edit intent: clarity
Original:  In addition, we demonstrate how a neural network model can seem to reduce the errors significantly compared to a KF - and how this reduction vanishes once the KF is optimized  .
Revised:  In addition, we demonstrate how an optimized neural network model can seem to reduce the errors significantly compared to a KF - and how this reduction vanishes once the KF is optimized .
Edit intent: clarity
Original:  This indicates how complicated models can be wrongly identified as superior to KF, while in fact they were merely over-optimized .
Revised:  This indicates how complicated models can be wrongly identified as superior to KF, while in fact they were merely more optimized .
Edit intent: clarity
Original: Machine Learning models have been deployed across almost every aspect of society, often in situations that affect the social welfareof many individuals .
Revised: Machine Learning models have been deployed across many different aspects of society, often in situations that affect the social welfareof many individuals .
Edit intent: clarity
Original: Machine Learning models have been deployed across almost every aspect of society, often in situations that affect the social welfareof many individuals .
Revised: Machine Learning models have been deployed across almost every aspect of society, often in situations that affect social welfare .
Edit intent: clarity
Original:  We then propose an end-to-end bias mitigation framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique.
Revised:  We then propose an end-to-end bias mitigation framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique.
Edit intent: fluency
Original:  We demonstrate that all three notions have properties expected of ordinals: their order relations, although defined differently in each case, are all extensional and wellfounded, and the usual ordinal arithmetic operations can be defined in each case.
Revised:  We demonstrate that all three notions have properties expected of ordinals: their order relations, although defined differently in each case, are all extensional and wellfounded, and the usual arithmetic operations can be defined in each case.
Edit intent: fluency
Original: This contribution analyzes the widely used and well-known "intelligent driver model " (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations.
Revised: This contribution analyzes the widely used and well-known "intelligent driver model (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations.
Edit intent: fluency
Original:  Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness of solutions has, to our knowledge, never been performed.
Revised:  Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness has, to our knowledge, never been performed.
Edit intent: clarity
Original:  First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to \(-\infty\) in finite time, both undesirable properties for a car-following model.
Revised:  First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to -\infty in finite time, both undesirable properties for a car-following model.
Edit intent: fluency
Original:  Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result practically impossible for large-scale simulations.
Revised:  Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result in practically impossible scenarios for large-scale simulations.
Edit intent: clarity
Original: Several novel algorithms for learning molecular representations have been proposed recently with the consolidation of deep learning in computer-aided drug design.
Revised: With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed recently with the consolidation of deep learning in computer-aided drug design.
Edit intent: clarity
Original:  Despite their theoretical benefits, it is not clear how molecular embeddings compare with each other and with traditional representations  , which in turn hinders the process of choosing a suitable embedding algorithm for QSAR modeling.
Revised:  Despite their theoretical benefits, comparing molecular embeddings with each other and with traditional representations , which in turn hinders the process of choosing a suitable embedding algorithm for QSAR modeling.
Edit intent: coherence
Original:  Despite their theoretical benefits, it is not clear how molecular embeddings compare with each other and with traditional representations  , which in turn hinders the process of choosing a suitable embedding algorithm for QSAR modeling.
Revised:  Despite their theoretical benefits, it is not clear how molecular embeddings compare with each other and with traditional representations , which in turn hinders the process of choosing a suitable representation for QSAR modeling.
Edit intent: clarity
Original:  A reason for this lack of consensus is that a fair and thorough comparison of different approaches is not straightforward .
Revised:  A reason behind this issue is the difficulty of conducting a fair and thorough comparison of different approaches is not straightforward .
Edit intent: clarity
Original:  A reason for this lack of consensus is that a fair and thorough comparison of different approaches is not straightforward .
Revised:  A reason for this lack of consensus is that a fair and thorough comparison of the different existing embedding approaches, which requires numerous experiments on various datasets and training scenarios .
Edit intent: clarity
Original:  Through a thorough experimental setup, we compared the molecular representations of these five methods concerning their performance in QSAR scenarios using five different datasets with varying class imbalance levels .
Revised:  We compared these five methods concerning their performance in QSAR scenarios using five different datasets with varying class imbalance levels .
Edit intent: clarity
Original:  Through a thorough experimental setup, we compared the molecular representations of these five methods concerning their performance in QSAR scenarios using five different datasets with varying class imbalance levels .
Revised:  Through a thorough experimental setup, we compared the molecular representations of these five methods concerning their performance in QSAR scenarios using different classification and regression datasets .
Edit intent: clarity
Original:  Our results show that molecular embeddings did not significantly surpass baseline results obtained using traditional molecular representations.
Revised:  As opposed to the expected outcome, our experimental setup consisting of over 25,000 trained models and statistical tests revealed that the predictive performance using molecular embeddings did not significantly surpass baseline results obtained using traditional molecular representations.
Edit intent: coherence
Original:  Our results show that molecular embeddings did not significantly surpass baseline results obtained using traditional molecular representations.
Revised:  Our results show that molecular embeddings did not significantly surpass that of traditional representations.
Edit intent: clarity
Original:  While supervised techniques yielded competitive results compared to those obtained by traditional molecular representations, unsupervised techniques did not match the baseline results .
Revised:  While supervised embeddings yielded competitive results compared to those obtained by traditional molecular representations, unsupervised techniques did not match the baseline results .
Edit intent: clarity
Original:  While supervised techniques yielded competitive results compared to those obtained by traditional molecular representations, unsupervised techniques did not match the baseline results .
Revised:  While supervised techniques yielded competitive results compared to those using traditional molecular representations, unsupervised techniques did not match the baseline results .
Edit intent: clarity
Original:  While supervised techniques yielded competitive results compared to those obtained by traditional molecular representations, unsupervised techniques did not match the baseline results .
Revised:  While supervised techniques yielded competitive results compared to those obtained by traditional molecular representations, unsupervised embeddings tended to perform worse than traditional representations .
Edit intent: clarity
Original:  Our results  motivate a discussion about the usefulness of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.
Revised:  Our results highlight the need for conducting a careful comparison and analysis of the different embedding techniques prior to using them in drug design tasks, and motivate a discussion about the usefulness of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.
Edit intent: clarity
Original:  Our results  motivate a discussion about the usefulness of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.
Revised:  Our results motivate a discussion about the potential of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.
Edit intent: clarity
Original:  Our results  motivate a discussion about the usefulness of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.
Revised:  Our results motivate a discussion about the usefulness of molecular embeddings in computer-aided drug design.
Edit intent: clarity
Original:  We then propose a Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees and a complexity analysis of the encoding.
Revised:  We then propose a Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees along with a complexity analysis of the encoding.
Edit intent: clarity
Original:  Experiments on real-world language confirm that the protocol's error is small .
Revised:  Experiments on real-world data .
Edit intent: clarity
Original:  We also bound the impact corrupt users have on the estimatesand show that it compares favorably to prior state-of-the-art .
Revised:  We also prove that corrupt users have on the estimatesand show that it compares favorably to prior state-of-the-art .
Edit intent: clarity
Original: This paper works towards merging two popular approaches in the control community for safety control : Hamilton-Jacobi (HJ) reachability analysis and Control Barrier Functions (CBFs).
Revised: This paper works towards unifying two popular approaches in the control community for safety control : Hamilton-Jacobi (HJ) reachability analysis and Control Barrier Functions (CBFs).
Edit intent: clarity
Original: This paper works towards merging two popular approaches in the control community for safety control : Hamilton-Jacobi (HJ) reachability analysis and Control Barrier Functions (CBFs).
Revised: This paper works towards merging two popular approaches in the safety control community : Hamilton-Jacobi (HJ) reachability analysis and Control Barrier Functions (CBFs).
Edit intent: clarity
Original: This paper works towards merging two popular approaches in the control community for safety control : Hamilton-Jacobi (HJ) reachability analysis and Control Barrier Functions (CBFs).
Revised: This paper works towards merging two popular approaches in the control community for safety control : Hamilton-Jacobi (HJ) reachability and Control Barrier Functions (CBFs).
Edit intent: clarity
Original:  HJ Reachability has methods for direct construction of value functions that provide safety guarantees and safe controllers, however the online implementation can be overly conservative and/or rely on jumpy bang-bang control.
Revised:  HJ Reachability has methods for direct construction of value functions that provide safety guarantees and safe controllers, however the online implementation can be overly conservative and/or rely on chattering bang-bang control.
Edit intent: clarity
Original:  The CBF community has methods for safe-guarding controllers in the form of point-wise optimization programs such as CBF-QP  , where the CBF-based safety certificate is used as a constraint.
Revised:  The CBF community has methods for safe-guarding controllers in the form of point-wise optimization programs such as CBF-QP ) , where the CBF-based safety certificate is used as a constraint.
Edit intent: fluency
Original:  This paper merges these two methods by introducing a new reachability formulation inspired by the structure of CBFs that constructs a Control Barrier-Value Function (CBVF).
Revised:  This paper unifies these two methods by introducing a new reachability formulation inspired by the structure of CBFs that constructs a Control Barrier-Value Function (CBVF).
Edit intent: fluency
Original:  This paper merges these two methods by introducing a new reachability formulation inspired by the structure of CBFs that constructs a Control Barrier-Value Function (CBVF).
Revised:  This paper merges these two methods by introducing a new reachability formulation inspired by the structure of CBFs to construct a Control Barrier-Value Function (CBVF).
Edit intent: coherence
Original:  Finally, we propose an online Quadratic Program formulation whose solution is always an optimal control signal , which is inspired by the CBF-QP . We demonstrate the benefit of using the CBVFs for double-integrator and Dubins car systems by comparing it to previous methods.
Revised:  Finally, we propose an online Quadratic Program formulation whose solution is always the CBVF's optimal control signal , which is inspired by the CBF-QP . We demonstrate the benefit of using the CBVFs for double-integrator and Dubins car systems by comparing it to previous methods.
Edit intent: clarity
Original: Reinforcement Learning (RL) is a widely-used technique in many domains, including autonomous driving, robotics, stock trading, and video games.
Revised: Reinforcement learning (RL) is a widely-used technique in many domains, including autonomous driving, robotics, stock trading, and video games.
Edit intent: fluency
Original: Reinforcement Learning (RL) is a widely-used technique in many domains, including autonomous driving, robotics, stock trading, and video games.
Revised: Reinforcement Learning (RL) is used in many domains, including autonomous driving, robotics, stock trading, and video games.
Edit intent: clarity
Original:  Unfortunately, the black box nature of RL agents, combined with increasing legal and ethical considerations, makes it increasingly important that humans  understand the reasoning behind the actions taken by an RL agent, particularly in safety-critical domains.
Revised:  Unfortunately, the black box nature of RL agents, combined with legal and ethical considerations, makes it increasingly important that humans understand the reasoning behind the actions taken by an RL agent, particularly in safety-critical domains.
Edit intent: clarity
Original:  To help address this challenge, we introduce PolicyExplainer, a visual analytics interface which lets the user directly query an RL agent.
Revised:  To help address this challenge, we introduce PolicyExplainer, a visual analytics interface which lets the user directly query an autonomous agent.
Edit intent: clarity
Original:  PolicyExplainer visualizes the states, policy, and expected future rewards for an agent, and supports asking and answering questions such as: " Why take this action?
Revised:  PolicyExplainer visualizes the states, policy, and expected future rewards for an agent, and supports asking and answering questions such as: Why take this action?
Edit intent: fluency
Original:  Why not  this other action?
Revised:  Why not take this other action?
Edit intent: fluency
Original:  ". PolicyExplainer is designed based upon a domain analysis with RL experts , and is evaluated via empirical assessments on a trio of domains: taxi navigation, an inventory application, and the safety-critical domain of drug recommendation for HIV patients  .
Revised:  PolicyExplainer is designed based upon a domain analysis with RL experts , and is evaluated via empirical assessments on a trio of domains: taxi navigation, an inventory application, and the safety-critical domain of drug recommendation for HIV patients .
Edit intent: fluency
Original:  ". PolicyExplainer is designed based upon a domain analysis with RL experts , and is evaluated via empirical assessments on a trio of domains: taxi navigation, an inventory application, and the safety-critical domain of drug recommendation for HIV patients  .
Revised:  ". PolicyExplainer is designed based upon a domain analysis with RL researchers , and is evaluated via empirical assessments on a trio of domains: taxi navigation, an inventory application, and the safety-critical domain of drug recommendation for HIV patients .
Edit intent: clarity
Original: The vision-based reinforcement learning (RL) has achieved tremendous success.
Revised: Vision-based reinforcement learning (RL) has achieved tremendous success.
Edit intent: coherence