wanyu/IteraTeR_full_sent huggingface dataset:
Original:  With diminishing sensitivity over the magnitude of news , one-shot resolution of uncertainty is strictly suboptimal under commonly used functional forms. Information structures that deliver bad news gradually are never optimal. We identify additional conditions that imply the sender optimally releases good news in small pieces but bad newsin one clump. When the sender lacks commitment power, diminishing sensitivity leads to a credibility problem for good-news messages. Without loss aversion, the babbling equilibrium is essentially unique  .
Revised:  With diminishing sensitivity over the magnitude of news . We show the agent's preference between an information structure that delivers news gradually and another that resolves all uncertainty at once depends on his consumption ranking of different states. One-shot resolution is better than gradual bad news, but it is not optimal among all information structures (under common functional forms). In a dynamic cheap-talk framework where a benevolent sender communicates the state over multiple periods, the babbling equilibrium is essentially unique .
Edit intent: clarity
Original:  We aim to explore the impact of contrastive explanations about large errors on users' attitudes towards a ' black-box' model. In this work, we make two contributions. The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target,  based on Monte Carlo simulations.
Revised:  We aim to explore the impact of contrastive explanations about large errors on users' attitudes towards a ' black-box' " model. We propose an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, based on Monte Carlo simulations.
Edit intent: clarity
Original:  The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target,  based on Monte Carlo simulations.
Revised:  The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, based on Monte Carlo simulations.
Edit intent: clarity
Original:  The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target,  based on Monte Carlo simulations.
Revised:  The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, both based on Monte Carlo simulations.
Edit intent: clarity
Original:  We evaluate on a real dataset with real users from Ahold Delhaize by conducting a user study  to determine if explanations generated by MC-BRP help users understand why a prediction results in a large error, and if this promotes trust in an automatically-learned model.
Revised:  We evaluate on a real dataset with real users by conducting a user study to determine if explanations generated by MC-BRP help users understand why a prediction results in a large error, and if this promotes trust in an automatically-learned model.
Edit intent: clarity
Original:  We evaluate on a real dataset with real users from Ahold Delhaize by conducting a user study  to determine if explanations generated by MC-BRP help users understand why a prediction results in a large error, and if this promotes trust in an automatically-learned model.
Revised:  We evaluate on a real dataset with real users from Ahold Delhaize by conducting a user study with 75 participants to determine if explanations generated by MC-BRP help users understand why a prediction results in a large error, and if this promotes trust in an automatically-learned model.
Edit intent: meaning-changed
Original:  We also show that users who saw MC-BRP explanations understand why the model makes large errors in predictions significantly more than users in the control group  .
Revised:  We also show that users who saw MC-BRP explanations understand why the model makes large errors in predictions significantly more than users in the control group . We also conduct an in-depth analysis on the difference in attitudes between Practitioners and Researchers, and confirm that our results hold when conditioning on the users' background .
Edit intent: meaning-changed
Original: The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a real d \times n matrix A, a vector yin^d, a point y\in } R^d, and an integer 2 \leq k \leq d, find an affine combination of at most k columns of A that is closest to y.
Revised: The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a set S of n points in \mathbb{R^d, a point y\in } R^d, and an integer 2 \leq k \leq d, find an affine combination of at most k columns of A that is closest to y.
Edit intent: meaning-changed
Original: The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a real d \times n matrix A, a vector yin^d, a point y\in } R^d, and an integer 2 \leq k \leq d, find an affine combination of at most k columns of A that is closest to y.
Revised: The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a real d \times n matrix A, a vector yin^d, a point y\in } R^d, and an integer 2 \leq k \leq d, find an affine combination of at most k points of S that is nearest to y.
Edit intent: clarity
Original:  We describe a O(n^{k-1} \log^{d-k+2} n)-time randomized (1+\varepsilon)-approximation algorithm for this problem with d and \varepsilon constant.
Revised:  We describe a O(n^{k-1} \log^{d-k+2} n)-time randomized (1+\varepsilon)-approximation algorithm for this problem with \(d\) and \(\varepsilon\) constant.
Edit intent: fluency
Original:  In the special case where k = d = O(1), we also provide a simple O (n ^{d-1+ \epsilon })-time deterministic exact algorithm  .
Revised:  In the special case where k = d = O(1), we also provide a simple O (n ^{d-1+ \epsilon })-time deterministic exact algorithm , for any \(\delta > 0\) .
Edit intent: meaning-changed
Original:  The method  is tested on  synthetic correlated time-series and noisy synthetic data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results.
Revised:  The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in Econophysics and Community Detection. The method is tested on synthetic correlated time-series and noisy synthetic data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results.
Edit intent: meaning-changed
Original:  We argue that ASPC can reduce compute time costs and resource usage cost for large scale clustering  while being serialized  and hence has no obvious parallelization requirement.
Revised:  We apply it to time-series data-sets as large as 20,000 assets and we argue that ALC can reduce compute time costs and resource usage cost for large scale clustering while being serialized and hence has no obvious parallelization requirement.
Edit intent: meaning-changed
Original:  This is confirmed by our data driven result that states that random forest can accurately predict the choice probability of any discrete choice model . Our framework has unique advantages: it can capture behavioral patterns such as irrationality or sequential searches;
Revised:  This is confirmed by our data driven result that states that random forest can accurately predict the choice probability of any discrete choice model consistently, with its splitting criterion capable of recovering preference rank lists. The framework has unique advantages: it can capture behavioral patterns such as irrationality or sequential searches;
Edit intent: meaning-changed
Original:  This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output . This is confirmed by our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently , with its splitting criterion capable of recovering preference rank lists .
Revised:  This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output : the underlying trees can be viewed as the internal choice process of customers. Our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently , with its splitting criterion capable of recovering preference rank lists .
Edit intent: meaning-changed
Original:  This is confirmed by our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently , with its splitting criterion capable of recovering preference rank lists .
Revised:  This is confirmed by our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently . Moreover, our algorithm predicts unseen assortments with mechanisms and errors that can be theoretically analyzed. We also prove that the splitting criterion in random forests, the Gini index, is capable of recovering preference rank lists .
Edit intent: meaning-changed
Original:  Our numerical results show that using random forests to estimate customer choices represented by binary choice forests can outperform the best parametric models in synthetic and real datasets  .
Revised:  Our numerical results show that using random forests to estimate customer choices represented by binary choice forests can outperform the best parametric models in synthetic and real datasets when presented with enough data or when the underlying discrete choice model cannot be correctly specified by existing parametric models .
Edit intent: meaning-changed
Original:  In the discounted setting , the Gittins index reduces the one-player problem to the comparison between a risky arm, with a prior \mu, and a predictable arm  with success probability p.
Revised:  With discount factor \beta , the Gittins index reduces the one-player problem to the comparison between a risky arm, with a prior \mu, and a predictable arm with success probability p.
Edit intent: meaning-changed
Original:  In the discounted setting , the Gittins index reduces the one-player problem to the comparison between a risky arm, with a prior \mu, and a predictable arm  with success probability p.
Revised:  In the discounted setting , the Gittins index reduces the one-player problem to the comparison between a risky arm, with a prior \mu, and a predictable arm , with success probability p.
Edit intent: fluency
Original:  The value of p where the player is indifferent between the arms is the Gittins index g  (\mu,\beta) > m, where m is the mean of the risky arm and \beta the discount factor .
Revised:  The value of p where the player is indifferent between the arms is the Gittins index g = g (\mu,\beta) > m, where m is the mean of the risky arm and \beta the discount factor .
Edit intent: meaning-changed
Original:  The value of p where the player is indifferent between the arms is the Gittins index g  (\mu,\beta) > m, where m is the mean of the risky arm and \beta the discount factor .
Revised:  The value of p where the player is indifferent between the arms is the Gittins index g (\mu,\beta) > m, where m is the mean of the risky arm .
Edit intent: clarity
Original:  We show that competing players explore less than a single player: there is p^* \in (m, g (\mu, \beta) ) so that for all p > p^*, the players stay at the predictable arm.
Revised:  We show that competing players explore less than a single player: there is p^* \in (m, g ) so that for all p > p^*, the players stay at the predictable arm.
Edit intent: clarity
Original:  Finally, we show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g (\mu,\beta) ), where p^* is the threshold above which competing players do not explore.
Revised:  We also show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g (\mu,\beta) ), where p^* is the threshold above which competing players do not explore.
Edit intent: coherence
Original:  Finally, we show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g (\mu,\beta) ), where p^* is the threshold above which competing players do not explore.
Revised:  Finally, we show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g ), where p^* is the threshold above which competing players do not explore.
Edit intent: coherence
Original:  Finally, we show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g (\mu,\beta) ), where p^* is the threshold above which competing players do not explore. We show similar phenomena in the finite horizon setting .
Revised:  Finally, we show that neutral players learn from each other, receiving strictly higher total rewards than they would playing alone, for all p\in (p^*, g (\mu,\beta) ), where p^* is the threshold from the competing case. Finally, we show that competing and neutral players eventually settle on the same arm in every Nash equilibrium, while this can fail for cooperating players .
Edit intent: meaning-changed
Original:  The main result consists in a proof of  existence and uniqueness of a strategy, called relatively growth optimal, such that the logarithm of the share of its wealth in the total wealth of the market is a submartingale for any strategies of the other investors.
Revised:  The main result consists in a proof of the existence and uniqueness of a strategy, called relatively growth optimal, such that the logarithm of the share of its wealth in the total wealth of the market is a submartingale for any strategies of the other investors.
Edit intent: fluency
Original:  The main result consists in a proof of  existence and uniqueness of a strategy, called relatively growth optimal, such that the logarithm of the share of its wealth in the total wealth of the market is a submartingale for any strategies of the other investors.
Revised:  The main result consists in a proof of existence and uniqueness of a strategy, called relative growth optimal, such that the logarithm of the share of its wealth in the total wealth of the market is a submartingale for any strategies of the other investors.
Edit intent: fluency
Original:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes .
Revised:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) based on test statistics that compare the proportion of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes .
Edit intent: clarity
Original:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes .
Revised:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes with either the overall proportion of successes following k consecutive successes and the proportion of successes .
Edit intent: clarity
Original:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes .
Revised:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes or the proportion of successes .
Edit intent: clarity
Original:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes . The asymptotic distributions of these test statistics and their permutation distributions are derived under randomness and under general models of streakiness, which allows us to evaluate their local asymptotic power.
Revised:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) which choose as test statistics (i) the difference between the proportions of successes that directly follow k consecutive successes and k consecutive failures or (ii) the difference between the proportion of successes following k consecutive successes and the proportion of successes that directly follow k consecutive failures. We derive the asymptotic distributions of these test statistics and their permutation distributions are derived under randomness and under general models of streakiness, which allows us to evaluate their local asymptotic power.
Edit intent: clarity
Original:  The results are applied to revisit tests of the " hot hand fallacy " implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Revised:  The results are applied to revisit tests of the hot hand fallacy " implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Edit intent: fluency
Original:  The results are applied to revisit tests of the " hot hand fallacy " implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Revised:  The results are applied to revisit tests of the " hot hand fallacy implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Edit intent: fluency
Original:   While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, supporting the existence of positive dependence in basketball shooting, we find that participants in a survey of basketball players over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Revised:  We establish that the tests are insufficiently powered to distinguish randomness from alternatives consistent with the variation in NBA shooting percentages. While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, supporting the existence of positive dependence in basketball shooting, we find that participants in a survey of basketball players over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Edit intent: meaning-changed
Original:   While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, supporting the existence of positive dependence in basketball shooting, we find that participants in a survey of basketball players over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Revised:  While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, we find that participants in a survey of basketball players over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Edit intent: clarity
Original:   While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, supporting the existence of positive dependence in basketball shooting, we find that participants in a survey of basketball players over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Revised:  While multiple testing procedures reveal that one shooter can be inferred to exhibit shooting significantly inconsistent with randomness, supporting the existence of positive dependence in basketball shooting, we find that participants in a survey of basketball fans over-estimate an average player's streakiness, corroborating the empirical support for the hot hand fallacy.
Edit intent: clarity
Original: We study a class of tests of the randomness of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks  as overly representative of positive dependence-the hot hand fallacy.
Revised: We study a class of tests of the randomness of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Edit intent: meaning-changed
Original:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness and under general models of streakiness, which allows us to evaluate their local asymptotic power.
Revised:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness , under a set of general stationary processes, and under a Markov model of "streakiness", which allow us to evaluate their local asymptotic power.
Edit intent: meaning-changed
Original:  The results are applied to revisit tests of the hot hand fallacy implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Revised:  The results are applied to evaluate tests of randomness implemented on data from a basketball shooting experiment, whose conclusions are disputed by Gilovich, Vallone, and Tversky (1985) and Miller and Sanjurjo (2018a).
Edit intent: meaning-changed