wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential  ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Revised:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, and provide (exponential ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Edit intent: coherence
Original:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential  ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Revised:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential in p ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem.
Edit intent: meaning-changed
Original:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential  ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs , and (iii) we characterize DFAs of minimum p for a given \mathcal L via a co-lexicographic variant of the Myhill-Nerode theorem. Our findings imply that in polynomial time  we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Revised:  Our main contributions are the following: (i) we show that the hierarchy is strict and does not collapse, (ii) we provide (exponential ) upper and lower bounds relating the minimum widths of equivalent NFAs and DFAs . Our bounds indicate the importance of being able to index NFAs, as they enable indexing regular languages with much faster and smaller indexes. Our second contribution solves precisely this problem, optimally: we devise a polynomial-time algorithm that indexes any NFA with the optimal value p for its width, without explicitly computing p (NP-hard to find). In particular, this implies that we can index in polynomial time we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Edit intent: meaning-changed
Original:  Our findings imply that in polynomial time  we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Revised:  Our findings imply that in polynomial time the well-studied case p=1 (Wheeler NFAs). More in general, in polynomial time we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Edit intent: meaning-changed
Original:  Our findings imply that in polynomial time  we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Revised:  Our findings imply that in polynomial time we can build an index breaking the worst-case conditional lower bound of \Omega( |P| m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Edit intent: meaning-changed
Original:  Our findings imply that in polynomial time  we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is at most \epsilon \log_2 m, for any constant 0 \leq \epsilon < 1/2.
Revised:  Our findings imply that in polynomial time we can build an index breaking the worst-case conditional lower bound of \Omega( \pi m), whenever the input NFA's width is p \in o(\sqrt{m
Edit intent: meaning-changed
Original: Contrastive approaches to self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing the same from different data points (negative pairs) .
Revised: While contrastive approaches of self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing the same from different data points (negative pairs) .
Edit intent: coherence
Original: Contrastive approaches to self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing the same from different data points (negative pairs) .
Revised: Contrastive approaches to self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing views from different data points (negative pairs) .
Edit intent: clarity
Original: Contrastive approaches to self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing the same from different data points (negative pairs) . However, recent approaches like  BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Revised: Contrastive approaches to self-supervised learning (SSL) learn representations by minimizing the distance between two augmented views of the same data point (positive pairs) and maximizing the same from different data points (negative pairs) , recent BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Edit intent: coherence
Original:  However, recent approaches like  BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Revised:  However, recent approaches likenon-contrastive BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Edit intent: meaning-changed
Original:  However, recent approaches like  BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Revised:  However, recent approaches like SSL (e.g., BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ?
Edit intent: meaning-changed
Original:  However, recent approaches like  BYOL and SimSiam , show remarkable performance {\it without} negative pairs, raising a fundamental theoretical question : how can SSL with only positive pairs avoid representational collapse ? We study the nonlinear learning dynamics of non-contrastive SSL in simple linear networks. Our analysis yields conceptual insights into how non-contrastive SSL methods learn, how they avoid representational collapse, and how multiple factors, like predictor networks, stop-gradients, exponential moving averages, and weight decay all come into play. Our simple theory recapitulates the results of real-world ablation studies in both STL-10 and ImageNet. Furthermore, motivated by our theory we propose a novel approach  that directly sets the  predictor based on the statistics of its inputs .
Revised:  However, recent approaches like BYOL and SimSiam , show remarkable performance {\it without} negative pairs, with an extra learnable predictor and a stop-gradient operation. A fundamental question arises: why do these methods not collapse into trivial representations? We answer this question via a simple theoretical study and propose a novel approach that directly sets the predictor based on the statistics of its inputs .
Edit intent: clarity
Original:  Furthermore, motivated by our theory we propose a novel approach  that directly sets the  predictor based on the statistics of its inputs . In the case of linear predictors, our approach outperforms gradient trainingof the predictor by 5 \\%DIF < and on ImageNet it performs comparably with more complex two-layer non-linear predictors that employ BatchNorm.
Revised:  Furthermore, motivated by our theory we propose a novel approach that directly sets the predictor based on the statistics of its inputs , without gradient training. On ImageNet, it performs comparably with more complex two-layer non-linear predictors that employ BatchNorm and outperforms a linear predictor by 2.5 \\%DIF < and on ImageNet it performs comparably with more complex two-layer non-linear predictors that employ BatchNorm.
Edit intent: meaning-changed
Original:  The expressive power of LTL supports the specification of a diversity of complex temporally extended behaviours that include conditionals and alternative realizations  . Experiments on discrete and continuous domains demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
Revised:  The expressive power of LTL supports the specification of a diversity of complex temporally extended behaviours that include conditionals and alternative realizations . To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments . Experiments on discrete and continuous domains demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
Edit intent: meaning-changed
Original:  The combinatorial task sets we target consist of up to ~10^{39 a well-known formal language -- linear temporal logic (LTL) -- to specify instructions, using a domain-specific vocabulary. We propose a novel approach to learning that exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training.
Revised:  The combinatorial task sets we target consist of up to ~10^{39 a well-known formal language -- linear temporal logic (LTL) -- and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training.
Edit intent: meaning-changed
Original:  Secondly, our regret bounds are the first of their kind in the reinforcement learning setting to have the number of corruptions show up additively with respect to \sqrt{T, PolicyGapComplexity\} } rather than multiplicatively.
Revised:  Secondly, our regret bounds are the first of their kind in the reinforcement learning setting to have the number of corruptions show up additively with respect to \min\{\sqrt{T, PolicyGapComplexity\} } rather than multiplicatively.
Edit intent: meaning-changed
Original:  We show that if G has treewidth w and maximum degree d, then any MWIS-circuit of G has 2^{\Omega(w/d)} gates and that if G is planar  then any MWIS-circuit of G has 2^{\Omega(w)} gates.
Revised:  We show that if G has treewidth w and maximum degree d, then any MWIS-circuit of G has 2^{\Omega(w/d)} gates and that if G is planar , or more generally H-minor-free for any fixed graph H, then any MWIS-circuit of G has 2^{\Omega(w)} gates.
Edit intent: meaning-changed
Original:  It follows that treewidth characterizes optimal MWIS-circuits up to polynomials for all bounded degree graphs and planar graphs, and treedepth characterizes optimal MWIS-formulas up to polynomials for all bounded degree graphs.
Revised:  It follows that treewidth characterizes optimal MWIS-circuits up to polynomials for all bounded degree graphs and H-minor-free graphs, and treedepth characterizes optimal MWIS-formulas up to polynomials for all bounded degree graphs.
Edit intent: meaning-changed
Original: Online gaming is a multi-billion-dollar industry, which is growing faster than ever before  . Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types.
Revised: Online gaming is growing faster than ever before . Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types.
Edit intent: clarity
Original: Online gaming is a multi-billion-dollar industry, which is growing faster than ever before  . Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types.
Revised: Online gaming is a multi-billion-dollar industry, which is growing faster than ever before , with increasing challenges of providing better user experience . Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types.
Edit intent: meaning-changed
Original: Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior  with mediated equilibria.
Revised: Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior with mediated equilibria.
Edit intent: meaning-changed
Original:  We explore the space of deviation types in extensive-form games (EFGs) and discover powerful types that are efficient to compute in games with moderate lengths.
Revised:  To develop hindsight rational learning in sequential decision-making settings, we formalize behavioral deviations as a general class of deviations that respect the structure of extensive-form games (EFGs) and discover powerful types that are efficient to compute in games with moderate lengths.
Edit intent: meaning-changed
Original:  Integrating the idea of time selection regret minimization into counterfactual regret minimization (CFR), we introduce the extensive-form regret minimization (EFR) algorithm that is hindsight rational for a general and natural class of deviations in EFGs.
Revised:  Integrating the idea of time selection into counterfactual regret minimization (CFR), we introduce the extensive-form regret minimization (EFR) algorithm that is hindsight rational for a general and natural class of deviations in EFGs.
Edit intent: fluency
Original:  Integrating the idea of time selection regret minimization into counterfactual regret minimization (CFR), we introduce the extensive-form regret minimization (EFR) algorithm that is hindsight rational for a general and natural class of deviations in EFGs. We provide instantiations and regret bounds for EFR that correspond to each partial sequence deviation type .
Revised:  Integrating the idea of time selection regret minimization into counterfactual regret minimization (CFR), we introduce the extensive-form regret minimization (EFR) algorithm that achieves hindsight rationality for any given set of behavioral deviations with computation that scales closely with the complexity of the set. We identify behavioral deviation subsets, the partial sequence deviation type .
Edit intent: meaning-changed
Original:  In addition, we present a thorough empirical analysis of EFR 's performance with different deviation types in common benchmark games. As theory suggests, instantiating EFR with stronger deviations leads to behavior that tends to outperform that of weaker deviations .
Revised:  In addition, we present a thorough empirical analysis of EFR 's performance with different deviation types in benchmark games, where we find that stronger types typically induce better performance .
Edit intent: clarity
Original:  Compared to ReLU, shrinkage in framelet convolution improves the graph neural network model in terms of denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with the prediction performancewell preserved .
Revised:  Compared to ReLU, shrinkage activation improves model performance on denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with the prediction performancewell preserved .
Edit intent: clarity
Original:  Compared to ReLU, shrinkage in framelet convolution improves the graph neural network model in terms of denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with the prediction performancewell preserved .
Revised:  Compared to ReLU, shrinkage in framelet convolution improves the graph neural network model in terms of denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with well-preserved prediction performance .
Edit intent: clarity
Original:  The goal is to have a single pass over the stream, use O(n polylog n ) space, and output a large matching of G. We prove that for an absolute constant \epsilon_0 > 0, one can find a (2/3 + \epsilon_0)-approximate maximum matching of G using O(n \log n) space with high probability.
Revised:  The goal is to have a single pass over the stream, use n \cdot poly(\log n ) space, and output a large matching of G. We prove that for an absolute constant \epsilon_0 > 0, one can find a (2/3 + \epsilon_0)-approximate maximum matching of G using O(n \log n) space with high probability.
Edit intent: meaning-changed
Original:  This breaks the natural boundary of 2/3 for this problem prevalent in the prior work and resolves an open problem of Bernstein ~ [ICALP'20] on whether a (2/3 + \Omega(1))-approximation is achievable.
Revised:  This breaks the natural boundary of 2/3 for this problem prevalent in the prior work and resolves an open problem of Bernstein [ICALP'20] on whether a (2/3 + \Omega(1))-approximation is achievable.
Edit intent: coherence
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Revised:  Unlike imitation learning (IL) and reinforcement learning (RL) . We develop an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Edit intent: clarity
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Revised:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) , our protocol allows the teaching agent to provide feedback in a language that is most appropriate for them. Compared with reward in reinforcement learning (RL) . We develop an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Edit intent: meaning-changed
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Revised:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL) , the description feedback is richer and allows for improved sample complexity . We develop an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Edit intent: meaning-changed
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Revised:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL) . We develop a probabilistic framework and an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Edit intent: meaning-changed
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback.
Revised:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL) . We develop an algorithm that practically implements our protocol. Empirical results in two challenging request-fulfilling problems using purely language-description feedback.
Edit intent: clarity