wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We  show that the hard instances from this earlier work have the propertythat in every partition of the hypergraph formed by the constraints into small blocks, most of the hyperedges are incident on vertices from distinct blocks .
Revised:  We design a family of appropriate CSPs (one for every q) from any given OCSP, and apply their work to this family of CSPs. We show that the hard instances from this earlier work have the propertythat in every partition of the hypergraph formed by the constraints into small blocks, most of the hyperedges are incident on vertices from distinct blocks .
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Original: In some puzzles, the strategy we need to use near the goal can be quite different from the strategy that is effective earlier on , e.g. due to a smaller branching factor near the exit state in a maze. A common approach in these casesis to apply both a forward and a backward search, and to try and align the two .
Revised: In some puzzles, the strategy we need to use near the goal can be quite different from the strategy that is effective earlier on , e.g. given only at task completion, remain quite challenging. In such cases, it can be beneficial to tackle the task both from its beginning and end, and make the two ends meet. Existing approaches that do so, however, are not effective in the common scenario where the strategy needed near the end goal is very different from the one that is effective earlier on.
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Original:  In this work we propose an approach that takes this idea a step forward, within a reinforcement learning (RL ) framework. Training a traditional forward-looking agent using RL can be difficult because rewards are often sparse, e.g. given only at the goal. Instead, we first train a backward-looking agent with a simple relaxed goal .
Revised:  In this work we propose a novel RL approach for such settings. In short, we first train a backward-looking agent with a simple relaxed goal .
Edit intent: clarity
Original:  We then augment the state representation of the puzzle with straightforward hint features that are extracted from the behavior of that agent. Finally, we train a forward looking agent with this informed augmented state. We demonstrate that this simple "access" to partial backward plansleads to a substantial performance boost. On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
Revised:  We then augment the state representation of the puzzle with straightforward hint features . This allows the learned forward agent to leverage information from backward plans, without mimicking their policy. We demonstrate the efficacy of our approach on the challenging game of Sokoban, where we substantially surpass learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
Edit intent: clarity
Original:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
Revised:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize across levels, and is competitive with SOTA performance of the best highly-crafted solution .
Edit intent: fluency
Original:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
Revised:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and are competitive with SOTA performance of the best highly-crafted solution .
Edit intent: fluency
Original:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
Revised:  On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted systems .
Edit intent: clarity
Original:  These monotonicity methods build the basis for the improvement of the ] standard one-step linearization method.
Revised:  These monotonicity methods build the basis for the improvement of the ], but have no rigorously proven convergence theory. Therefore we show how the monotonicity methods can be converted into a regularization method for a data-fitting functional without losing the convergence properties of the monotonicity methods. This is a great advantage and a significant improvement over standard one-step linearization method.
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Original:  These monotonicity methods build the basis for the improvement of the ] standard one-step linearization method. The one-step linearization method consists of solving a minimization problem with standard regularization techniques, which is commonly used in practice but builds only a heuristical approach since there is no proven theory of its convergence.
Revised:  These monotonicity methods build the basis for the improvement of the ] standard regularization techniques, which is commonly used in practice but builds only a heuristical approach since there is no proven theory of its convergence.
Edit intent: coherence
Original:  In contrary, the monotonicity-based regularization, where we introduce constraints for the minimization problem via the monotonicity methods, has a rigorously proven theory, i.e., we prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
Revised:  In more detail, we introduce constraints for the minimization problem via the monotonicity methods, has a rigorously proven theory, i.e., we prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
Edit intent: clarity
Original:  In contrary, the monotonicity-based regularization, where we introduce constraints for the minimization problem via the monotonicity methods, has a rigorously proven theory, i.e., we prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
Revised:  In contrary, the monotonicity-based regularization, where we introduce constraints on the minimization problem via the monotonicity methods, has a rigorously proven theory, i.e., we prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
Edit intent: fluency
Original:  In contrary, the monotonicity-based regularization, where we introduce constraints for the minimization problem via the monotonicity methods, has a rigorously proven theory, i.e., we prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
Revised:  In contrary, the monotonicity-based regularization, where we introduce constraints for the minimization problem of the residual based on the monotonicity methods and prove the existence and uniqueness of a minimizer as well as the convergence of the method for noisy data.
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Original: In this work we present an approach for building tight model-free confidence intervals for the optimal value function V ^\star in general infinite horizon MDPs via the upper solutions. We suggest a novel upper value iterative procedure (UVIP) to construct upper solutions for a given agent's policy. UVIP leads to a model free method of policy evaluation. We analyze convergence properties of the approximate UVIPunder rather corresponding to a policy \pi does not provide reliable information on how far is the policy \pi from the optimal one.
Revised: In this work we present an approach for building tight model-free confidence intervals for the optimal value function V ^{\pi corresponding to a policy \pi does not provide reliable information on how far is the policy \pi from the optimal one.
Edit intent: coherence
Original:  We present a novel model-free upper value iteration procedure (}{\sf  (x) - V^{\pi}(x) from above and to construct confidence intervals for V^\star.
Revised:  We present a novel model-free upper value iteration procedure (}{\sf ) that allows us to estimate the suboptimality gap V^{\star(x) - V^{\pi}(x) from above and to construct confidence intervals for V^\star.
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Original:  First, we present a stochastic method for non-convex SBO with only one lower problem and establish its sample complexity of O(1/\epsilon^3) for finding an \epsilon-stationary point under appropriate conditions , matching the lower bound for stochastic smooth non-convex optimization.
Revised:  First, we present a stochastic method for non-convex SBO with only one lower problem and establish its sample complexity of O(1/\epsilon^3) for finding an \epsilon-stationary point under Lipschitz continuous conditions of stochastic oracles , matching the lower bound for stochastic smooth non-convex optimization.
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Original:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems by processing only one lower problem at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could have a better complexity than  simply processing all m lower problems at each iteration.
Revised:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems (multi-task SBO) by processing a constant number of lower problems at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could have a better complexity than simply processing all m lower problems at each iteration.
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Original:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems by processing only one lower problem at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could have a better complexity than  simply processing all m lower problems at each iteration.
Revised:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems by processing only one lower problem at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could be a better complexity than simply processing all m lower problems at each iteration.
Edit intent: clarity
Original:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems by processing only one lower problem at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could have a better complexity than  simply processing all m lower problems at each iteration.
Revised:  Second, we present a randomized stochastic method for non-convex SBO with m>1 lower level problems by processing only one lower problem at each iteration, and establish its sample complexity no worse than O(m/\epsilon^3), which could have a better complexity than that of simply processing all m lower problems at each iteration.
Edit intent: clarity
Original:  The higher-order interactions in complex networksare interactions between two or more nodes, i. e., beyond edges.
Revised: Information diffusion on networks is an important concept in network science observed in many situations such as information spreading and rumor controlling in social networks, disease contagion between individuals, cascading failures in power grids. The higher-order interactions in complex networksare interactions between two or more nodes, i. e., beyond edges.
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Original:  Identifying critical higher-order interactions is a significant research problem with a wide range of applications, such as information propagation, market advertising, and rumor controlling.
Revised:  Besides, interactions can occur between not only two nodes as pairwise interactions, i.e., edges, but also three or more nodes, described as higher-order interactions is a significant research problem with a wide range of applications, such as information propagation, market advertising, and rumor controlling.
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Original:  Identifying critical higher-order interactions is a significant research problem with a wide range of applications, such as information propagation, market advertising, and rumor controlling. This paper proposes two new Laplacians that allow redefining classical graph centrality measures for higher-order interactions.
Revised:  Identifying critical higher-order interactions. This report presents a novel method to identify critical higher-order interactions. We propose two new Laplacians that allow redefining classical graph centrality measures for higher-order interactions.
Edit intent: clarity
Original:  We present a data-efficient imitation learning technique called Collocation for Demonstration Encoding (CoDE) that operates on only a fixed set of trajectory demonstrations by modeling learning as empirical risk minimization .
Revised:  In this paper, we present a novel imitation learning technique called Collocation for Demonstration Encoding (CoDE) that operates on only a fixed set of trajectory demonstrations by modeling learning as empirical risk minimization .
Edit intent: clarity
Original:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with experts and/or anti-experts in an ensemble of language models .
Revised:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language model with experts and/or anti-experts in an ensemble of language models .
Edit intent: coherence
Original:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with experts and/or anti-experts in an ensemble of language models .
Revised:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with "expert" LMs and/or anti-experts in an ensemble of language models .
Edit intent: clarity
Original:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with experts and/or anti-experts in an ensemble of language models .
Revised:  We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation which combines a pretrained language model with experts and/or "anti-expert" LMs in a product of experts .
Edit intent: clarity
Original:   Our work highlights the promise of using LMs trained on text with (un) desired attributes for efficient decoding-time controlled language generation .
Revised:  Moreover, because DExperts operates only on the output of the pretrained LM, it is effective with (anti-)experts of smaller size, including when operating on GPT-3. Our work highlights the promise of using LMs trained on text with (un) desired attributes for efficient decoding-time controlled language generation .
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Original:  We further analyze the generalization error, the excess empirical risk, and the excess population risk of our proposed method and give corresponding bounds . By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does   not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Revised:  We further analyze the generalization error, the excess empirical risk, and the excess population risk of our proposed method and give corresponding bounds under both expectation and high probability conditions. We use the not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Edit intent: meaning-changed
Original:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does   not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Revised:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis doeson-average stability not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Edit intent: meaning-changed
Original:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does   not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Revised:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does and the not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Edit intent: coherence
Original:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does   not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Revised:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does pairwise locally elastic stability not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Edit intent: meaning-changed
Original:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does   not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
Revised:  By introducing algorithmic stability theory to pairwise differential privacy, our theoretical analysis does theories to analyze the expectation bound and the high probability bound, respectively. Moreover, our analyzed utility bounds do not require convex pairwise loss functions, which means that our method is general to both convex and non-convex conditions.
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Original:   This paper proposes a universal modularized model, SpeechNet, which contains the five basic modules for speech processing .
Revised:  If a universal model can perform multiple speech processing tasks, some tasks might be improved with the related abilities learned from other tasks. The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied. This paper proposes a universal modularized model, SpeechNet, which contains the five basic modules for speech processing .
Edit intent: coherence
Original:  Specifically, in each trial, we jointly train a subset of all speech tasksunder multi-task setting, with all modules shared. Then we can observe whether one task can benefit another during training . SpeechNet is modularized and flexible for incorporating more modules, tasks, or training approaches in the future.
Revised:  Specifically, in each trial, we jointly train a subset of all speech tasksunder multi-task learning experiments with SpeechNet. We show that SpeechNet learns all of the above tasks, and we further analyze which tasks can be improved by other tasks . SpeechNet is modularized and flexible for incorporating more modules, tasks, or training approaches in the future.
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Original:  By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmark , MuST-C  , to a new state-of-the-art.
Revised:  By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks , MuST-C , to a new state-of-the-art.
Edit intent: fluency
Original:  By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmark , MuST-C  , to a new state-of-the-art.
Revised:  By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmark , MuST-C and Augmented Librispeech , to a new state-of-the-art.
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Original:  By computing the rank correlation between attention weights and the scores produced by a small sample of these methods, previous analyses have sought to either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience.
Revised:  By computing the rank correlation between attention weights and feature-additive explanation methods, previous analyses have sought to either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience.
Edit intent: clarity
Original:  By computing the rank correlation between attention weights and the scores produced by a small sample of these methods, previous analyses have sought to either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience.
Revised:  By computing the rank correlation between attention weights and the scores produced by a small sample of these methods, previous analyses either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience.
Edit intent: clarity
Original:  To investigate what measures of rank correlation can reliably conclude, we comprehensively compare feature-additive methods, including attention-based explanations, across several neural architectures and tasks.
Revised:  To investigate whether this approach is appropriate, we compare LIME, Integrated Gradients, DeepLIFT, Grad-SHAP, Deep-SHAP, and attention-based explanations, across several neural architectures and tasks.
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Original:  In this work, we demonstrate the effectiveness of dynamic flinging actions for cloth unfolding . We propose a self-supervised learning framework, FlingBot , that learns how to unfold a piece of fabric from arbitrary initial configurations using a pick, stretch, and fling primitive for a dual-arm setup from visual observations.
Revised:  In this work, we demonstrate the effectiveness of dynamic flinging actions for cloth unfolding with our proposed self-supervised learning framework, FlingBot , that learns how to unfold a piece of fabric from arbitrary initial configurations using a pick, stretch, and fling primitive for a dual-arm setup from visual observations.
Edit intent: clarity
Original:  We propose a self-supervised learning framework, FlingBot , that learns how to unfold a piece of fabric from arbitrary initial configurations using a pick, stretch, and fling primitive for a dual-arm setup from visual observations.
Revised:  We propose a self-supervised learning framework, FlingBot . Our approach learns how to unfold a piece of fabric from arbitrary initial configurations using a pick, stretch, and fling primitive for a dual-arm setup from visual observations.
Edit intent: clarity
Original:  The project video is available at URLabstract 
Revised:  The project video is available at URLabstract \% coverage within 3 actions on novel cloths, can unfold cloths larger than the system's reach range, and generalizes to T-shirts despite being trained on only rectangular cloths. We also finetuned FlingBot on a real-world dual-arm robot platform, where it increased the cloth coverage over 4 times more than the quasi-static baseline did. The simplicity of FlingBot combined with its superior performance over quasi-static baselines demonstrates the effectiveness of dynamic actions for deformable object manipulation. Code, data, and simulation environment are available at URL .
Edit intent: meaning-changed