wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MS MARCO benchmark show that, (1) all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: coherence
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models are less fair in comparison with ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: clarity
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, and (2) the fairness of Bert rankers significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: meaning-changed
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when using the proposed AdvBert models . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: clarity
Original:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
Revised:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that we can maintain the significant improvements in fairness without any significant loss in utility.
Edit intent: coherence
Original: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Revised: Mean field games (MFG) facilitate the application of reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Edit intent: clarity
Original: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Revised: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Edit intent: clarity
Original: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Revised: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between an individual agent and the mass of the population.
Edit intent: clarity
Original: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the mass of the population.
Revised: Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in large-scale multi-agent systems (MAS) , through reducing interplays among agents to those between a representative individual agent and the average effect from the population.
Edit intent: clarity
Original:  Inverse reinforcement learning (IRL) provides a framework to automatically acquire proper reward functions from expert demonstrations. Extending IRL to MFG , however, is challenging due to the complex notion of mean-field-type equilibria and the coupling between agent-level and population-level dynamics.
Revised:  Inverse reinforcement learning (IRL) holds promise for automatically acquiring suitable rewards from demonstrations, its extension to MFG is challenging due to the complex notion of mean-field-type equilibria and the coupling between agent-level and population-level dynamics.
Edit intent: clarity
Original:  To this end, we propose mean field inverse reinforcement learning (MFIRL), a novel model-free IRL framework for MFG . We derive the algorithm based on a new equilibrium concept that incorporates entropy regularization, and the maximum entropy IRL framework.
Revised:  To this end, we propose a novel IRL framework for MFG . We derive the algorithm based on a new equilibrium concept that incorporates entropy regularization, and the maximum entropy IRL framework.
Edit intent: clarity
Original:  To this end, we propose mean field inverse reinforcement learning (MFIRL), a novel model-free IRL framework for MFG . We derive the algorithm based on a new equilibrium concept that incorporates entropy regularization, and the maximum entropy IRL framework.
Revised:  To this end, we propose mean field inverse reinforcement learning (MFIRL), a novel model-free IRL framework for MFG , called Mean Field IRL (MFIRL), where we build upon a new equilibrium concept that incorporates entropy regularization, and the maximum entropy IRL framework.
Edit intent: meaning-changed
Original:  To this end, we propose mean field inverse reinforcement learning (MFIRL), a novel model-free IRL framework for MFG . We derive the algorithm based on a new equilibrium concept that incorporates entropy regularization, and the maximum entropy IRL framework.
Revised:  To this end, we propose mean field inverse reinforcement learning (MFIRL), a novel model-free IRL framework for MFG . We derive the algorithm based on a new equilibrium concept and the maximum entropy IRL framework.
Edit intent: coherence
Original:  Experimental results on simulated environments demonstrate that MFIRL is sample efficient and can accurately recover the ground-truth reward functions , compared to the state-of-the-art method.
Revised:  Experimental results on simulated environments demonstrate that MFIRL is sample efficient and can accurately recover the ground-truth ) reward functions for MFG. Experiments show the superior performance of MFIRL on sample efficiency, reward recovery and robustness against varying environment dynamics , compared to the state-of-the-art method.
Edit intent: meaning-changed
Original:  The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action.
Revised:  The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one external signal from multiple signal sources as additional payoff-relevant information and then takes an action.
Edit intent: clarity
Original:  An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Revised:  We propose a direct information design when the information design incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Edit intent: clarity
Original:  An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Revised:  An obedient principle is established which states that it is without loss of generality to focus on the direct information design approach that incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Edit intent: clarity
Original:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Revised:  We then introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Edit intent: coherence
Original:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Revised:  Based on the obedient principle, we introduce the design protocol given a goal of the designer which we refer to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Edit intent: clarity
Original:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Revised:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Edit intent: coherence
Original:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE ).
Revised:  Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect equilibria (O-SMPE ).
Edit intent: meaning-changed
Original:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved  .
Revised:  A design regime is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved .
Edit intent: clarity
Original:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved  .
Revised:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, guarantees that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved .
Edit intent: clarity
Original:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved  .
Revised:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPE and the principal's goal is achieved .
Edit intent: meaning-changed
Original:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved  .
Revised:  A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved . We then formulate the principal's optimal goal selection problem in terms of information design and characterize the optimization problem by minimizing the fixed-point misalignments .
Edit intent: meaning-changed
Original:  With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy Hold (B H) . There is a certain gap between these algorithms and the real trading decision making scenarios.
Revised:  With the development of computer science and deep reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy Hold (B H) . There is a certain gap between these algorithms and the real trading decision making scenarios.
Edit intent: meaning-changed
Original:  With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy Hold (B H) . There is a certain gap between these algorithms and the real trading decision making scenarios. On the one hand, they only consider trading signals while ignoring the number of transactions. On the other hand, the information level considered by these algorithms is not rich enough, which limits the performance of these algorithms.
Revised:  With the development combination of quantitative trading and reinforcement learning, more trading algorithms have achieved significant gains beyond the benchmark model Buy Hold (B \&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limits the performance of these algorithms.
Edit intent: meaning-changed
Original:  Thus, we propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and deep reinforcement learning . The former is composed of three parallel network layers , respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks.
Revised:  Thus, we propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above, which consists of parallel network layers and deep reinforcement learning model, where the deep learning parallel network layers , respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks.
Edit intent: clarity
Original:  The former is composed of three parallel network layers , respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Revised:  The former is composed of three parallel network layers deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Edit intent: meaning-changed
Original:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Revised:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Edit intent: meaning-changed
Original:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Revised:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench model .
Edit intent: clarity
Original:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
Revised:  The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench models .
Edit intent: fluency
Original:  It has been widely shown to improve accuracy. At the same time, ensemble learning has also been proposed to mitigate privacy leakage in terms of membership inference (MI), where the goal of an attacker is to infer whether a particular data sample has been used to train a target model .
Revised:  Ensemble learning has also been proposed to mitigate privacy leakage in terms of membership inference (MI), where the goal of an attacker is to infer whether a particular data sample has been used to train a target model .
Edit intent: coherence
Original:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Revised:  In this paper, we empirically demonstrate a trade-off between these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Edit intent: coherence
Original:  In this paper, we show that these two goals of ensemble learning, namely improving accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Revised:  In this paper, we show that these two goals , namely accuracy and privacy , directly conflict with each other . Using a wide range of datasets and model architectures, we empirically demonstrate the trade-off between privacy and accuracy in deep ensemble learning.
Edit intent: clarity