wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To this end, in this paper we propose a novel approach for the domain generalization problem called Model-Based Domain Generalization. In our approach, we first use unlabeled data from the training domains to learn multi-modal domain transformation models that map data from one training domain to any other domain.
Revised:  To this end, in this paper we propose a novel approach for the domain generalization problem is equivalent to an infinite-dimensional constrained statistical learning problem; this problem forms the basis of our approach, we first use unlabeled data from the training domains to learn multi-modal domain transformation models that map data from one training domain to any other domain.
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Original:  In our approach, we first use unlabeled data from the training domains to learn multi-modal domain transformation models that map data from one training domain to any other domain. Next, we propose a constrained optimization-based formulation for domain generalization which enforces that a trained predictor be invariant to distributional shifts under the underlying domain transformation model. Finally , we propose a novel algorithmic framework for efficiently solving this constrained optimization problem .
Revised:  In our approach, which we call Model-Based Domain Generalization. Due to the inherent challenges in solving constrained optimization problems in deep learning, we exploit nonconvex duality theory to develop unconstrained relaxations of this statistical problem with tight bounds on the duality gap. Based on this theoretical motivation , we propose a novel algorithmic framework for efficiently solving this constrained optimization problem .
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Original:  In our experiments, we show that this approach outperforms both ERM and domain generalization algorithms on numerous well-known, challenging datasets, including WILDS, PACS, and ImageNet. In particular, our algorithms beat the current state-of-the-art methods on the very-recently-proposed WILDS benchmark by up to 20 percentage points .
Revised:  In our experiments, we report improvements of up to 30 percentage points over state-of-the-art domain generalization baselines on several benchmarks including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS .
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Original:  At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages logged historical operational data of a TGPU to solve a highly complex constrained Markov decision process problem via purely offline training.
Revised:  At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages logged historical operational data of a TPGU to solve a highly complex constrained Markov decision process problem via purely offline training.
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Original:  This paper initiates the theoretical study of sample-efficient learning of the Stackelberg equilibrium in two-player turn-based general-sum games . We identify a fundamental gap between the exact value of the Stackelberg equilibrium and its estimated version using finite samples, which can not be closed information-theoretically regardless of the algorithm.
Revised:  This paper initiates the theoretical study of sample-efficient learning of the Stackelberg equilibrium in two-player turn-based general-sum games : bandit games, bandit-reinforcement learning (bandit-RL) games, and linear bandit games. In all these games, we identify a fundamental gap between the exact value of the Stackelberg equilibrium and its estimated version using finite samples, which can not be closed information-theoretically regardless of the algorithm.
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Original:  We then establish a positive result on sample-efficient learning of Stackelberg equilibrium with value optimal up to the gap identified above . We show that our sample complexity is tight with matching upper and lower bounds . Finally, we extend our learning results to the setting where the follower plays in a Markov Decision Process (MDP), and the setting where the leader and the follower act simultaneously .
Revised:  We then establish a positive result on sample-efficient learning of Stackelberg equilibrium with value optimal up to the gap identified above , with matching lower bounds in the dependency on the gap, error tolerance, and the size of the action spaces. Overall, our results unveil unique challenges in learning Stackelberg equilibria under noisy bandit feedback, which we hope could shed light on future research on this topic .
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Original:  We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 759x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 160 \%) to offline methods.
Revised:  We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 464x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 160 \%) to offline methods.
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Original:  We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 759x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 160 \%) to offline methods.
Revised:  We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 759x faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 100 \%) to offline methods.
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Original:  To demonstrate this point, we construct regions (phase diagrams) in higher order moment space, where a GARCH(1,1) model can be used to fit the higher order moments and compare this with empirical data from different sectors of the economy.
Revised:  To demonstrate this point, we construct regions (phase diagrams) in the fourth and sixth order standardised moment space, where a GARCH(1,1) model can be used to fit the higher order moments and compare this with empirical data from different sectors of the economy.
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Original:  To demonstrate this point, we construct regions (phase diagrams) in higher order moment space, where a GARCH(1,1) model can be used to fit the higher order moments and compare this with empirical data from different sectors of the economy.
Revised:  To demonstrate this point, we construct regions (phase diagrams) in higher order moment space, where a GARCH(1,1) model can be used to fit these moments and compare this with empirical data from different sectors of the economy.
Edit intent: clarity
Original:  To demonstrate this point, we construct regions (phase diagrams) in higher order moment space, where a GARCH(1,1) model can be used to fit the higher order moments and compare this with empirical data from different sectors of the economy.
Revised:  To demonstrate this point, we construct regions (phase diagrams) in higher order moment space, where a GARCH(1,1) model can be used to fit the higher order moments and compare them with ones obtained from empirical data for different sectors of the economy.
Edit intent: clarity
Original:  Primarily, if we have a time series, using a GARCH(1,1) model with a double gaussian conditional probability (a GARCH-double-normal model) we cannot necessarily fit the statistical moments of the time series. Highlighting, that the GARCH-double-normal model only allows fitting of specific lengths of time series .
Revised:  It is seen for this model that we can only fit data collected within specific time window lengths and only with certain parameters of the conditional double gaussian distribution .
Edit intent: clarity
Original:  To demonstrate this point, we construct regions (phase diagrams) in the fourth and sixth order standardised moment space, where a GARCH(1,1) model can be used to fit these moments and compare them with ones obtained from empirical data for different sectors of the economy.
Revised:  To demonstrate this point, we construct regions (phase diagrams) in the fourth and sixth order standardised moment space, where a GARCH(1,1) model can be used to fit these moments and compare them with the corresponding moments from empirical data for different sectors of the economy.
Edit intent: clarity
Original:  As a result, we obtain forces  which are on average an order of magnitude more accurate compared to the direct boundary-integral computation from the Navier-Stokes solution  .
Revised:  As a result, we obtain forces which are on average an order of magnitude more accurate compared to the direct boundary-integral computation from the Navier-Stokes solution , leading to solid motion comparable to that obtained on highly resolved meshes that would substantially increase the simulation costs .
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Original:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot  \log \log n)  MPC rounds in the sublinear memory regime .
Revised:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot \log \log n) MPC rounds in the sublinear memory regime .
Edit intent: fluency
Original:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot  \log \log n)  MPC rounds in the sublinear memory regime .
Revised:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot poly \log \log n) MPC rounds in the sublinear memory regime .
Edit intent: fluency
Original:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot  \log \log n)  MPC rounds in the sublinear memory regime .
Revised:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot ( \log \log n) MPC rounds in the sublinear memory regime .
Edit intent: fluency
Original:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot  \log \log n)  MPC rounds in the sublinear memory regime .
Revised:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot \log \log n) ) MPC rounds in the sublinear memory regime .
Edit intent: fluency
Original:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot  \log \log n)  MPC rounds in the sublinear memory regime .
Revised:  Our main result is a 3-approximation ( in expectation ) algorithm that runs in O(\log \lambda \cdot \log \log n) MPC rounds in the strongly sublinear memory regime .
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Original:  This is obtained by combining structural properties of correlation clustering on bounded arboricity graphs with the insights of Fischer and Noever (SODA '18) on randomized greedy MIS and the %DIFDELCMD < \PIVOT %%%  algorithm of Ailon, Charikar, and Newman (STOC '05).
Revised:  This is obtained by combining structural properties of correlation clustering on bounded arboricity graphs with the insights of Fischer and Noever (SODA '18) on randomized greedy MIS and the %DIFDELCMD < \PIVOT %%% PIVOT algorithm of Ailon, Charikar, and Newman (STOC '05).
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Original: Multi-step off-policy reinforcement learninghas achieved great success. However, existing multi-step methods usually impose a fixed prior on the bootstrap steps, while the off-policy methods often require additional correction, suffering from certain undesired effects. In this paper , we propose a novel bootstrapping method , which greedily takes the maximum value among the bootstrapping values with varying steps. The new method has two desired properties:1) it can flexibly adjust the bootstrap step based on the quality of the data and the learned value function;
Revised: Efficiently propagating credit to responsible actions is a central and challenging task in reinforcement learning. To accelerate information propagation, this paper presents a new method has two desired properties:1) it can flexibly adjust the bootstrap step based on the quality of the data and the learned value function;
Edit intent: clarity
Original:  The new method has two desired properties:1) it can flexibly adjust the bootstrap step based on the quality of the data and the learned value function; 2) it can safely and robustly utilize data from arbitrary behavior policy without additional correction, whatever its quality or "off-policyness". We analyze the theoretical properties of the related operator, showing that it is able to converge to the global optimal value function , with a ratio faster than the traditional Bellman Optimality Operator.
Revised:  The new method that bridges a highway that allows unimpeded information to flow across long horizons. The key to our method is a newly proposed Bellman equation, called Greedy-Step Bellman Optimality Equation, through which the high-credit information can fast propagate across a long horizon. We theoretically show that the solution of the new equation is exactly the optimal value function , with a ratio faster than the traditional Bellman Optimality Operator.
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Original:  We analyze the theoretical properties of the related operator, showing that it is able to converge to the global optimal value function , with a ratio faster than the traditional Bellman Optimality Operator. Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN) . Experiments reveal that the proposed methods are reliable, easy to implement , and achieve state-of-the-art performance on a series of standard benchmark datasets .
Revised:  We analyze the theoretical properties of the related operator, showing that it is able to converge to the global optimal value function , with a ratio faster than the classical operator. Besides, it leads to a new multi-step off-policy algorithm, which is capable of safely utilizing any off-policy data collected by the arbitrary policy . Experiments reveal that the proposed methods are reliable, easy to implement , and achieve state-of-the-art performance on a series of standard benchmark datasets .
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Original:  Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN) . Experiments reveal that the proposed methods are reliable, easy to implement , and achieve state-of-the-art performance on a series of standard benchmark datasets .
Revised:  Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN) . Experiments reveal that the proposed method is reliable, easy to implement , and achieve state-of-the-art performance on a series of standard benchmark datasets .
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Original:  Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN) . Experiments reveal that the proposed methods are reliable, easy to implement , and achieve state-of-the-art performance on a series of standard benchmark datasets .
Revised:  Furthermore, based on this new operator, we derive new model-free RL algorithms named Greedy Multi-Step Q Learning (and Greedy Multi-step DQN) . Experiments reveal that the proposed methods are reliable, easy to implement . Moreover, without employing additional components of Rainbow except Double DQN, our method achieves competitive performance with Rainbow on the benchmark tasks .
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Original:   A local algorithm, as defined by Rubinfeld et al. (ICS 2011), for constructing a spanning subgraph with almost minimum weight, specifically, at most a factor (1+\epsilon) of the optimum, with poly(1/\epsilon)-query complexity.
Revised:  (2) A local algorithm, as defined by Rubinfeld et al. (ICS 2011), for constructing a spanning subgraph with almost minimum weight, specifically, at most a factor (1+\epsilon) of the optimum, with poly(1/\epsilon)-query complexity.
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Original:  Both our algorithms use partition-oracles , a tool introduced by Hassidim et al. (FOCS 2009), which are oracles that provide access to a partition of the graph such that the number of cut-edges is small and each part of the partition is small.
Revised:  Both our algorithms use partition oracles , a tool introduced by Hassidim et al. (FOCS 2009), which are oracles that provide access to a partition of the graph such that the number of cut-edges is small and each part of the partition is small.
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Original:  For bounded degree minor-free graphs we introduce the notion of %DIFDELCMD < {\em %%% covering partition oracles which is a relaxed version of partition oracles and design a poly(d/\epsilon)-time covering partition oracle for this family of graphs . Using our covering partition oracle we provide the same results as above (except that the tester for Hamiltonicity has one sided error)
Revised:  For bounded degree minor-free graphs we introduce the notion of %DIFDELCMD < {\em %%% covering partition oracles which is a relaxed version of partition oracles and design a poly(d/\epsilon)-time covering partition oracle . Using our covering partition oracle we provide the same results as above (except that the tester for Hamiltonicity has one sided error)
Edit intent: clarity
Original:  For bounded degree minor-free graphs we introduce the notion of %DIFDELCMD < {\em %%% covering partition oracles which is a relaxed version of partition oracles and design a poly(d/\epsilon)-time covering partition oracle for this family of graphs . Using our covering partition oracle we provide the same results as above (except that the tester for Hamiltonicity has one sided error)
Revised:  For bounded degree minor-free graphs we introduce the notion of %DIFDELCMD < {\em %%% covering partition oracles which is a relaxed version of partition oracles and design a poly(d/\epsilon)-time covering partition oracle for this family of graphs . Using our covering partition oracle we provide the same results as above (except that the tester for Hamiltonicity has one-sided error)
Edit intent: fluency
Original:  for minor free bounded degree graphs, as well as showing that any property which is monotone and additive (e.g. bipartiteness) can be tested in minor-free graphs by making poly(d/\epsilon)-queries.
Revised:  for minor-free bounded degree graphs, as well as showing that any property which is monotone and additive (e.g. bipartiteness) can be tested in minor-free graphs by making poly(d/\epsilon)-queries.
Edit intent: fluency
Original:  In this expository review, we introduce and contrast three important recent approaches attractive in their simplicity and their suitability for high-dimensional problems: physics-informed neural networks, methods based on the Feynman-Kac formula and the Deep BSDE solver .
Revised:  In this expository review, we introduce and contrast three important recent approaches attractive in their simplicity and their suitability for high-dimensional problems: physics-informed neural networks, methods based on the Feynman-Kac formula and methods based on the solution of backward stochastic differential equations .
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Original: We introduce a variant of the three-sided stable matching problem for a PhD market with students, advisors, and co-advisors . In our formalization, students have consistent (lexicographic) preferences over advisors and co-advisors , and the latter have preferences over students only (hence advisors and co-advisors are cooperative).
Revised: We introduce a variant of the three-sided stable matching problem for a PhD market with students, advisors, and co-advisors , and can be generalized to supply chain networks viewed as n-sided markets. In the three-sided PhD market, students primarily care about advisors and then about co-advisors , and the latter have preferences over students only (hence advisors and co-advisors are cooperative).
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Original:  In our formalization, students have consistent (lexicographic) preferences over advisors and co-advisors , and the latter have preferences over students only (hence advisors and co-advisors are cooperative).
Revised:  In our formalization, students have consistent (lexicographic) preferences over advisors and co-advisors (consistent preferences), while advisors and co-advisors have preferences over students only (hence advisors and co-advisors are cooperative).
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Original:  In contrast to previous work, advisor-student and student-co-advisor pairs may not be mutually acceptable , e.g., a student may not want to work with an advisor or co-advisor and vice versa  .
Revised:  In contrast to previous work, advisor-student and student-co-advisor pairs may not be mutually acceptable ( e.g., a student may not want to work with an advisor or co-advisor and vice versa .
Edit intent: fluency
Original:  In contrast to previous work, advisor-student and student-co-advisor pairs may not be mutually acceptable , e.g., a student may not want to work with an advisor or co-advisor and vice versa  .
Revised:  In contrast to previous work, advisor-student and student-co-advisor pairs may not be mutually acceptable , e.g., a student may not want to work with an advisor or co-advisor and vice versa ) .
Edit intent: fluency
Original:  We show that stable three-sided  matchings always exist, and present the PhD algorithm , a three-sided matching algorithm with polynomial running time which uses any two-sided stable matching algorithm as matching engine.
Revised:  We show that stable three-sided matchings always exist, and present an algorithm that, in time quadratic in the market size (up to log factors), finds a three-sided matching algorithm with polynomial running time which uses any two-sided stable matching algorithm as matching engine.
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Original:  Borrowing from results on two-sided markets, we provide some approximate optimality results. We also present an extension to three-sided markets with quotas , where each student conducts several projects, and each project is supervised by one advisor and one co-advisor.
Revised:  We illustrate the challenges that arise when not all advisor-co-advisor pairs are compatible. We then generalize our algorithm to n-sided markets with quotas , where each student conducts several projects, and each project is supervised by one advisor and one co-advisor.
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Original:  We also present an extension to three-sided markets with quotas , where each student conducts several projects, and each project is supervised by one advisor and one co-advisor. As it is often the case in practice that the same student should not do more than one project with the same advisor or co-advisor, we modify our PhD algorithm for this setting by adapting the two-sided Gale--Shapley algorithm to many-to-many two-sided markets, in which the same pair can match at most once. We also generalize the three-sided market to an n-sided market consisting of n-1 two-sided markets. We extend the PhD algorithm to this multi-sided setting to compute ]  a stable matching in polynomial time, and we discuss its extension to arbitrary quotas.
Revised:  We also present an extension to three-sided markets with quotas and show how they can model supply chain networks. Finally, we show how our algorithm outperforms the baseline given by ] a stable matching in polynomial time, and we discuss its extension to arbitrary quotas.
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