wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees.
Revised:  However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving their claimed fairness guarantees.
Edit intent: clarity
Original:  Motivated by this, we study fair classification problems when the protected attributes in the data may be ``noisy''. In particular, we consider a noise model where any protected type may be flipped to another with some fixed probability. We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Revised:  Motivated by this, recent works have studied the fair classification problem in which a binary protected attribute is "noisy" (the protected type is flipped with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Edit intent: clarity
Original:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Revised:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with a known fixed probability) by either suggesting optimization using tighter statistical or equalized odds constraints to counter the noise or by identifying conditions under which prior equalized odds post-processing algorithms can handle noisy attributes. We extend the study of noise-tolerant fair classification to a very general setting. Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with linear and linear-fractional class of fairness constraints, comes with probabilistic guarantees on accuracy and fairness, and can handle multiple, non-binary protected attributes . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Edit intent: meaning-changed
Original:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Revised:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy for significant noise levels .
Edit intent: meaning-changed
Original:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Revised:  We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy , even when the noise corruption is large in two real-world datasets. Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Edit intent: meaning-changed
Original: Due to the deployment of classification algorithms in a multitude of applications directly and indirectly affecting people and society, developing methods that are fair with respect to protected attributes such as gender or race is crucial. However, protected attributes in datasets may be inaccurate due to noise in the data collection or if the protected attributes are imputed either in whole or in part. Such inaccuracies can prevent existing fair classification algorithms from achieving their claimed fairness guarantees. Motivated by this, recent works have studied the fair classification problem in which a binary protected attribute is "noisy" (the protected type is flipped with a known fixed probability) by either suggesting optimization using tighter statistical or equalized odds constraints to counter the noise or by identifying conditions under which prior equalized odds post-processing algorithms can handle noisy attributes. We extend the study of noise-tolerant fair classification to a very general setting. Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with  linear and linear-fractional class of fairness constraints,
Revised: We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with linear and linear-fractional class of fairness constraints,
Edit intent: meaning-changed
Original:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with  linear and linear-fractional class of fairness constraints,
Revised:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes . Compared to prior work, our framework can be employed with linear and linear-fractional class of fairness constraints,
Edit intent: meaning-changed
Original:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with  linear and linear-fractional class of fairness constraints,
Revised:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with a very general class of linear and linear-fractional class of fairness constraints,
Edit intent: meaning-changed
Original:  non-binary protected attributes  . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise corruption is large in two real-world datasets . Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Revised:  non-binary protected attributes , and outputs a classifier that comes with provable guarantees on both accuracy and fairness . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise corruption is large in two real-world datasets . Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Edit intent: meaning-changed
Original:  non-binary protected attributes  . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise corruption is large in two real-world datasets . Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Revised:  non-binary protected attributes . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise is large, in two real-world datasets . Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Edit intent: fluency
Original:  non-binary protected attributes  . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise corruption is large in two real-world datasets . Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
Revised:  non-binary protected attributes . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise corruption is large in two real-world datasets .
Edit intent: coherence
Original:  1) For two-player games, we show that the regret of optimistic Hedge decays at O( 1/T ^{5/6} ), improving the previous bound O(1/T^{3/4}) by \mbox{%DIFAUXCMD syrgkanis2015fast 2) In contrast, we show that the convergence rate of vanilla Hedge is no better than \Omega(1/ T) , addressing an open question posted in \mbox{%DIFAUXCMD syrgkanis2015fast .
Revised:  1) For two-player games, we show that the regret of optimistic Hedge decays at O( 1/T ^{5/6} ), improving the previous bound O(1/T^{3/4}) by Syrgkanis, Agarwal, Luo and Schapire (NIPS'15) 2) In contrast, we show that the convergence rate of vanilla Hedge is no better than \Omega(1/ T) , addressing an open question posted in \mbox{%DIFAUXCMD syrgkanis2015fast .
Edit intent: meaning-changed
Original:  1) For two-player games, we show that the regret of optimistic Hedge decays at O( 1/T ^{5/6} ), improving the previous bound O(1/T^{3/4}) by \mbox{%DIFAUXCMD syrgkanis2015fast 2) In contrast, we show that the convergence rate of vanilla Hedge is no better than \Omega(1/ T) , addressing an open question posted in \mbox{%DIFAUXCMD syrgkanis2015fast .
Revised:  1) For two-player games, we show that the regret of optimistic Hedge decays at O( 1/T ^{5/6} ), improving the previous bound O(1/T^{3/4}) by \mbox{%DIFAUXCMD syrgkanis2015fast 2) In contrast, we show that the convergence rate of vanilla Hedge is no better than \Omega(1/ T) , addressing an open question posted in Syrgkanis, Agarwal, Luo and Schapire (NIPS'15) .
Edit intent: meaning-changed
Original:  For general m-player games, we show that the swap regret of each player decays at rate O(m^{1/2} (n/T)^{3/4}) when they combine optimistic Hedge with the classical external-to-internal reduction of Blum and Mansour \mbox{%DIFAUXCMD blum2007external .
Revised:  For general m-player games, we show that the swap regret of each player decays at rate O(m^{1/2} (n/T)^{3/4}) when they combine optimistic Hedge with the classical external-to-internal reduction of Blum and Mansour (JMLR'07) .
Edit intent: meaning-changed
Original:  Recently, Gagie et al. presented r-index,  an efficient compressed index on RLBWT whose space usage does not depend on text length . In this paper , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Revised:  Recently, Gagie et al. presented r-index, an efficient compressed index on RLBWT whose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega(r) words for the number r of runs in the RLBWT of an input string. Following this line of research , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Edit intent: meaning-changed
Original:  In this paper , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Revised:  In this paper , we present the first compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Edit intent: meaning-changed
Original:  In this paper , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Revised:  In this paper , we present a new compressed index on RLBWT, which we call r-index-f in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Edit intent: coherence
Original:  In this paper , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries. in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Revised:  In this paper , we present a new compressed index on RLBWT, which we call r-index-f, in which r-index is improved for faster locatequeries., that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper. We present efficient data structures for computing two important functions of LF and \phi^{-1 in constant time with O(r) words of space, which is a bit step forward in computation time from the previous best result of O(\log \log n) time for string length n and O(r) words of space.
Edit intent: meaning-changed
Original:  Finally, } We introduce a novel division of RLBWT into blocks, which we call balanced BWT-sequence as follows: the RLBWT of a string is divided into several blocks, and a parent-child relationship between each pair of blocks is defined. In addition, we present a novel backward search algorithm on the balanced BWT-sequences, resulting in faster locate queries of r-index-f. We also present new algorithms for solving the queries of count query, extract query, decompression and prefix search on r-index-f .
Revised:  Finally, } We present algorithms for computing queries on RLBWT by leveraging those two data structures in optimal time with O(r) words of space .
Edit intent: clarity
Original:  We are the first to exploit neuroscientific data, namely electroencephalography (EEG), to inform a neural attention model about language processing of the human brain with direct cognitive measures. Part of the challenge in working with EEG  is that features are exceptionally rich and need extensive pre-processing to isolate signals specific to text processing.
Revised:  We are the first to exploit neuroscientific data, namely electroencephalography (EEG), to inform a neural attention model about language processing of the human brain . The challenge in working with EEG is that features are exceptionally rich and need extensive pre-processing to isolate signals specific to text processing.
Edit intent: clarity
Original:  We find that the proposed approach, which only uses hand-crafted and learnable attentions, is comparable with the full self-attention  .
Revised:  With the assistance of t-SNE, PCA and some observation, the attention weights in self-supervised audio transformers can be categorized into four general cases. Based on these cases and some analyses, we are able to use a specific set of attention weights to initialize the model. Our approach shows comparable performance to the typical self-attention .
Edit intent: meaning-changed
Original:  Using a constructive approach based on duality, we prove that a mechanism that maximizes the worst-case expected revenue among all deterministic dominant-strategy incentive compatible, ex post individually rational mechanisms takes the following form: (1) the bidders submit bids b_i; (2) for each bidder, a linear score s_i=\beta_ib_i-\alpha_i is calculated where \alpha_i, \beta_i are fixed parameters; (3) the object is  awarded to the agent with the highest score, provided it 's nonnegative;
Revised:  Using a constructive approach based on duality, we prove that a mechanism that maximizes the worst-case expected revenue among all deterministic dominant-strategy incentive compatible, ex post individually rational mechanisms is awarded to the agent with the highest score, provided it 's nonnegative;
Edit intent: coherence
Original: Multiple studies have been conducted to predict the impact and duration of the current COVID-19 epidemics. Most of those studies rely on parameter calibration using the published number of confirmed cases. Unfortunately, this number is usually incomplete and biased due to the lack of testing capacities, and varying testing protocols. An essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals.
Revised: In times of outbreaks, an essential requirement for better monitoring is the evaluation of the number of undiagnosed infected individuals.
Edit intent: clarity
Original:  This number is crucial for the determination of transmission prevention strategies and it provides statistics on the epidemic dynamics. To estimate the number of undiagnosed infected individuals, we studied the relation between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected.
Revised:  This number is crucial for the assessment of the situation and the establishment of protective measures. In most current studies using epidemics models, the total number of infected is either approximated by the number of diagnosed individuals or is dependent on the model parameters and assumptions, which are often debated. We here study the relationship between the fraction of diagnosed infected out of all infected, and the fraction of infected with known contaminator out of all diagnosed infected.
Edit intent: meaning-changed
Original:  We simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models . Across most models currently used and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent.
Revised:  We show that those two are approximately the same in exponential models and across most models currently used and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent.
Edit intent: meaning-changed
Original:  We simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models . Across most models currently used and for most realistic model parameters, the relation between the two fractions is consistently linear and model independent. This relation can be used to estimate the number of undiagnosed infected, with no explicit epidemiological model. We apply this method to measure the number of undiagnosed infected in Israel. Since the fraction of confirmed cases with a known source can be obtained from epidemiological investigations in any country, one can estimate the total number of infected individuals in the same country .
Revised:  We simulated multiple models currently used to study the COVID-19 pandemic and computed the relation between these two fractions in all those models . Across most models currently used in the study of epidemics, independently of the total number of infected individuals in the same country .
Edit intent: clarity
Original:  We highlight implications for causal accounts .
Revised:  We compare these new insights the kinds of causal processes that affect the evolution of phonemic inventories over time, and identify a potential account for why, despite there being an important role for phonetic substance in phonemic change, we could still expect inventories with highly diverse phonetic content to share similar distributions of phoneme frequencies. We conclude with priorities for future work in this promising program of research .
Edit intent: meaning-changed
Original: The recent advent of single cell RNA-sequencing and other high-throughput technologies has led to an explosion of cell type definitions across URLan systems. Consortia like the BRAIN Initiative Cell Census Network (BICCN) and the Human Cell Atlas (HCA) have begun to standardize and centralize the intake of data and associated metadata from these projects; however, the naming URLanization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies derived from overlapping datasets .
Revised: The recent advent of single cell RNA-sequencing and other high-throughput technologies has led to an explosion of cell type definitions across URLans URLanisms. While standards for data and metadata intake are URLanization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies derived from overlapping datasets .
Edit intent: clarity