paper_id large_stringlengths 9 12 | review_id large_stringlengths 9 12 | year int64 2.02k 2.03k | paper_title large_stringlengths 6 202 | paper_abstract large_stringlengths 306 4.31k | system_prompt large_stringclasses 5
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zzk231Ms1Ih | TvyIJI1TlM- | 2,022 | A Theory of Tournament Representations | Real-world tournaments are almost always intransitive. Recent works have noted that parametric models which assume $d$ dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed $d$ dimensional r... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper studies the theory of tournament representations, i.e. low-rank matrices $M$ whose sign agrees with the sign matrix of a tournament $T$.\n\nThe authors show several properties of such representations, reducing the study to so called $R$-cones, i.e. tournaments where one vertex beats... |
zzk231Ms1Ih | mvJh08SL8ke | 2,022 | A Theory of Tournament Representations | Real-world tournaments are almost always intransitive. Recent works have noted that parametric models which assume $d$ dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed $d$ dimensional r... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper provides fundamental theories of tournament representations. The authors study two main questions. First they characterize the class of tournaments that can be represented in d dimensions. Second they give lower and upper bounds on the minimum dimension needed to represent a tournam... |
zzk231Ms1Ih | C9NhLV0gKlP | 2,022 | A Theory of Tournament Representations | Real-world tournaments are almost always intransitive. Recent works have noted that parametric models which assume $d$ dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed $d$ dimensional r... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The paper studies the relationship between dimensional representation of tournament and their structural characterization. In particular, a relationship is established between rank d tournament and their forbidden configurations in terms of flip classes, introduced by Fisher&Ryan(1995) as a wa... |
zzk231Ms1Ih | pUE59TP-ax | 2,022 | A Theory of Tournament Representations | Real-world tournaments are almost always intransitive. Recent works have noted that parametric models which assume $d$ dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed $d$ dimensional r... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "A tournament is made by choosing a direction for each of the edges in a complete graph. A tournament can be induced of by skew symmetric matrices M where entries M_{ij} > 0 if and only if (i,j) is an edge. A tournament on n edges can be represented by a set of d-dimensional vectores {h_1, … , ... |
zz9hXVhf40 | KkJogqpzEwc | 2,022 | Revisiting Design Choices in Offline Model Based Reinforcement Learning | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, appro... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "Recent successful model-based offline RL techniques have relied on heuristics to penalize rewards according to the uncertainty of the estimated MDP. This paper reviews the different penalties that have been designed in the literature. The impact and importance of associated hyperparameters suc... |
zz9hXVhf40 | yfOp35kKfgD | 2,022 | Revisiting Design Choices in Offline Model Based Reinforcement Learning | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, appro... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "Model-based offline reinforcement learning algorithms typically involve constructing a pessimistic MDP, which is implemented based on an uncertainty estimation of the learned model. This paper conducts empirical analysis to compare different design choices of the uncertainty estimation in prac... |
zz9hXVhf40 | L1E57BlqSOY | 2,022 | Revisiting Design Choices in Offline Model Based Reinforcement Learning | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, appro... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The paper provides an evaluation of many of the design choices and hyperparameter decisions made in offline model-based reinforcement learning methods which have emerged recently. Particularly, the empirical study looks at uncertainty penalties used in these methods, as well as hyperparameters... |
zz9hXVhf40 | pCMrUs15dnj | 2,022 | Revisiting Design Choices in Offline Model Based Reinforcement Learning | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, appro... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The authors present an empirical study of several uncertainty quantification heuristics applied to model learning in offline model-based reinforcement learning. Specifically, they consider the basic architecture of MOPO, in which an uncertainty based state-action penalty function is applied on... |
zz9hXVhf40 | 7QJWP1izpM4 | 2,022 | Revisiting Design Choices in Offline Model Based Reinforcement Learning | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, appro... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The paper provides detailed analysis of different uncertainty quantifications in Model-based offline RL, from both statistical and empirical perspectives. Further, the paper performs Bayesian optimization to find hyper-parameters and the optimized hyper-parameters perform better empirically. \... |
zyrhwrd9EYs | -aEdXoBehI4 | 2,022 | To Impute or Not To Impute? Missing Data in Treatment Effect Estimation | Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to th... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "\"we identify a new missingness mechanism, which we term mixed confounded missingness (MCM), where some missingness determines treatment selection and other missingness is determined by treatment selection.\"\n\nThe author gave a new term called “MCM” which is not something new. MCM is a type ... |
zyrhwrd9EYs | Sw15Q7x-eNY | 2,022 | To Impute or Not To Impute? Missing Data in Treatment Effect Estimation | Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to th... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper studies dealing with missing values in estimating treatment effects. Authors identify a new missingness mechanism, mixed confounded missingness (MCM), including missingness that determines treatment selection and missingness that is determined by treatment selection. The authors sho... |
zyrhwrd9EYs | vop8d7q0nC4 | 2,022 | To Impute or Not To Impute? Missing Data in Treatment Effect Estimation | Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to th... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "In this paper, the authors mainly study the problem of missing data in treatment effect estimation and highlight the importance of addressing this problem. The authors propose a selective imputation scheme which is more well suited for addressing missingness in such scenarios. Authors also pre... |
zxEfpcmTDnF | zuD4vNVLIxz | 2,022 | Learning and controlling the source-filter representation of speech with a variational autoencoder | Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are p... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper shows that the fundamental frequency and formant frequency information is encoded in a speech VAE model.\nThis can be found by using artificially controlled/generated dataset.\nAfter finding how to manipulate the latent space, one can control arbitrary speech samples in a desirable ... |
zxEfpcmTDnF | z-B1OnWOtp_ | 2,022 | Learning and controlling the source-filter representation of speech with a variational autoencoder | Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are p... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper analyzes the latent representations of speech spectrograms learned by unsupervised variational autoencoders (VAEs) and discovers that the VAE learns to model the variation of fundamental frequencies (F0/source) and formant frequencies (filters) using orthogonal subspaces. Based on t... |
zxEfpcmTDnF | 56VxUr-PO5h | 2,022 | Learning and controlling the source-filter representation of speech with a variational autoencoder | Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are p... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The paper proposes a method for utilizing labeled synthetic data in order to characterize and control the latent space of a VAE trained on individual frames of speech spectrograms.\nKey properties of the data which one might want explicit control over are identified, i.e., pitch and formant fr... |
zxEfpcmTDnF | DmvCsvKK-C0 | 2,022 | Learning and controlling the source-filter representation of speech with a variational autoencoder | Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are p... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper analyzes VAE latent embeddings to extract subspaces that relate to pitch (f0) and formant frequencies (f1 through fN). This is done through first training a frame-synchronous IS-VAE model from clean speech data. The authors pass controlled synthesized speech through the model and o... |
zuqcmNVK4c2 | 4l7GAqeKj4I | 2,022 | Self-Joint Supervised Learning | Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples ... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The Authors propose a new way of training classifier models. Instead of classifying each i.i.d. example independently during training and inference, they jointly classify a pair (X1, X2) of them, returning a joint distribution of labels (Y1, Y2). The loss function is modified so that the model... |
zuqcmNVK4c2 | bgq04xhGww | 2,022 | Self-Joint Supervised Learning | Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples ... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper introduces a pairwise loss function for supervised learning, named self-joint learning. Instead of predicting the conditional distribution of the label given one data sample as in conventional supervised learning, the proposed self-joint learning framework predicts the conditional j... |
zuqcmNVK4c2 | 79tJyc2F8Dg | 2,022 | Self-Joint Supervised Learning | Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples ... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "Paper propose a simple but effective method of learning. Feeding in a pair of data and learn the join conditional probabilities for predictions. The advantage is that the combination of data goes as the number of ways to partition the original data into pairs. This is a huge number.\n", "main_... |
zuDmDfeoB_1 | RQljuCcy-hp | 2,022 | How Does the Task Landscape Affect MAML Performance? | Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "The authors aim to compare theoretically and empirically the ability of MAML and Non-Adaptive Learning (NAL) to different tasks in a multi-task setting, where tasks are sampled independently from the same distribution. They argue that MAML is better suitable for adapting to hard tasks, while N... |
zuDmDfeoB_1 | XV8T-2YJQ4O | 2,022 | How Does the Task Landscape Affect MAML Performance? | Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper analyzes the performance of MAML algorithm under linear regression setting and compares that with the NAL under the same tasks. The authors show that the excess risk is smaller if there is more discrepency in the hardness of tasks and if the optimal solutions of hard tasks locate cl... |
zuDmDfeoB_1 | nOMar2XTAK- | 2,022 | How Does the Task Landscape Affect MAML Performance? | Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper is about finding the conditions under which MAML outperforms standard multi-task learning. In particular, the authors focus on a linear regression setting and show that MAML outperforms NAL under the following two conditions: (i) there must be some discrepancy in hardness among the ... |
zuDmDfeoB_1 | ofPS6AiV_fA | 2,022 | How Does the Task Landscape Affect MAML Performance? | Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper analytically compares the excess risk of two meta-learning methods: MAML and NAL. NAL is the simple baseline where the initialization for optimizing the test tasks is learned as parameter which minimizes the average loss of train tasks. \n\n1. For a particular simple setting of line... |
zrdUVVAvcP2 | UJH91RO9uJl | 2,022 | GrASP: Gradient-Based Affordance Selection for Planning | Planning with a learned model is arguably a key component of intelligence. There are several challenges in realizing such a component in large-scale reinforcement learning (RL) problems. One such challenge is dealing effectively with continuous action spaces when using tree-search planning (e.g., it is not feasible to ... | You are an expert academic peer reviewer for ICLR 2022.
Read the paper provided in the next message and write a complete review as a JSON object with the following fields:
- "summary_of_the_paper" (string): Provide a brief summary of the paper and its contributions.
- "main_review" (string): Please list both the stren... | {paper_markdown} | {"summary_of_the_paper": "This paper looks incorporating the concept of affordances from ecological psychology into the planning process. \nThe basic premise is that affordances represent the possible relevant actions available to an agent that are potentially moderated by their goals and the state of the world. This h... |
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