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ORB-Peer-Review

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### Title: Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data ### Abstract: In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victims floor level via their mobile devices sensor dat...
ryca0nYef ### Summary: The paper combines existing methods to outperform baseline methods on floor level estimation. Limitations of their approach are not explored. ### Review: The authors motivate the problem of floor level estimation and tackle it with a RNN. The results are good. The models the authors compare to a...
ry1E-75eG ### Summary: A fairly simple application of existing methods to a problem, and there remain some methodological issues ### Review: Update: Based on the discussions and the revisions, I have improved my rating. However I still feel like the novelty is somewhat limited, hence the recommendation. =============...
### Title: Some Considerations on Learning to Explore via Meta-Reinforcement Learning ### Abstract: We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call Krazy World and ...
ryse_yclM ### Summary: A new exploration algorithm for reinforcement learning ### Review: Summary: this paper proposes algorithmic extensions to two existing RL algorithms to improve exploration in meta-reinforcement learning. The new approach is compared to the baselines on which they are built on a new domain, and a...
SkE07mveG ### Summary: Interesting direction for exploration in meta-RL. Many relations to prior work missing though. Lets wait for rebuttal. ### Review: The paper proposes a trick of extending objective functions to drive exploration in meta-RL on top of two recent so-called meta-RL algorithms, Model-Agnostic Meta-Le...
### Title: MACH: Embarrassingly parallel $K$-class classification in $O(d\\logK)$ memory and $O(K\\logK + d\\logK)$ time, instead of $O(Kd)$ ### Abstract: We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ ...
H1tJH9FxM ### Summary: Extreme multi-class classification with Hashing ### Review: Thanks to the authors for their feedback. ============================== The paper presents a method for classification scheme for problems involving large number of classes in multi-class setting. This is related to the theme of extrem...
SJB-0Mtlz ### Summary: Good ideas, but insufficient results ### Review: The manuscript proposes an efficient hashing method, namely MACH, for softmax approximation in the context of large output space, which saves both memory and computation. In particular, the proposed MACH uses 2-universal hashing to randomly group ...
### Title: Deterministic Policy Imitation Gradient Algorithm ### Abstract: The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous cont...
S1_na_OlG ### Summary: Hard to read ### Review: This paper proposes to extend the determinist policy gradient algorithm to learn from demonstrations. The method is combined with a type of density estimation of the expert to avoid noisy policy updates. It is tested on Mujoco tasks with expert demonstrations generated w...
S1tVQ5Kef ### Summary: Combines IRL, adversarial training, and ideas from deterministic policy gradients. Paper is hard to read. MuJoCo results are good. ### Review: The paper lists 5 previous very recent papers that combine IRL, adversarial learning, and stochastic policies. The goal of this paper is to do the same t...
### Title: Searching for Activation Functions ### Abstract: The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed a...
Sy-QnQHef ### Summary: Another approach for arriving at proven concepts on activation functions ### Review: Authors propose a reinforcement learning based approach for finding a non-linearity by searching through combinations from a set of unary and binary operators. The best one found is termed Swish unit; x * sigmoi...
HylYITVZG ### Summary: Well written paper and well conducted experiments. ### Review: The author uses reinforcement learning to find new potential activation functions from a rich set of possible candidates. The search is performed by maximizing the validation performance on CIFAR-10 for a given network architecture. ...
### Title: Improving Search Through A3C Reinforcement Learning Based Conversational Agent ### Abstract: We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search wh...
BkL816Ygf ### Summary: lack of details ### Review: The paper describes reinforcement learning techniques for digital asset search. The RL techniques consist of A3C and DQN. This is an application paper since the techniques described already exist. Unfortunately, there is a lack of detail throughout the paper and th...
H1f_jh_ef ### Summary: Lack of context ### Review: This paper proposes to use RL (Q-learning and A3C) to optimize the interaction strategy of a search assistant. The method is trained against a simulated user to bootstrap the learning process. The algorithm is tested on some search base of assets such as images or vid...
### Title: Identifying Analogies Across Domains ### Abstract: Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fid...
HJ08-bCef ### Summary: The approach is interesting but the paper lacks clarity of presentation ### Review: The paper presents a method for finding related images (analogies) from different domains based on matching-by-synthesis. The general idea is interesting and the results show improvements over previous approaches...
SkHatuolz ### Summary: AN-GAN: match-aware translation of images across domains, new ideas for combining image matching and GANs ### Review: This paper presents an image-to-image cross domain translation framework based on generative adversarial networks. The contribution is the addition of an explicit exemplar constr...
### Title: Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling ### Abstract: Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is har...
ryOYfeaef ### Summary: solid experiments, but the model is not very exciting ### Review: This paper introduces bi-directional block self-attention model (Bi-BioSAN) as a general-purpose encoder for sequence modeling tasks in NLP. The experiments include tasks like natural language inference, reading comprehension (Squ...
rkcETx9lf ### Summary: Strong support for more efficient attention ### Review: This high-quality paper tackles the quadratic dependency of memory on sequence length in attention-based models, and presents strong empirical results across multiple evaluation tasks. The approach is basically to apply self-attention at tw...
### Title: WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling ### Abstract: To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Diri...
SyESlFoef ### Summary: WHAI: WEIBULL HYBRID AUTOENCODING INFERENCE FOR DEEP TOPIC MODELING ### Review: The authors propose a hybrid Bayesian inference approach for deep topic models that integrates stochastic gradient MCMC for global parameters and Weibull-based multilayer variational autoencoders (VAEs) for local par...
B1gG5N5ez ### Summary: official review ### Review: The authors develop a hybrid amortized variational inference MCMC inference framework for deep latent Dirichlet allocation. Their model consists of a stack of gamma factorization layers with a Poisson layer at the bottom. They amortize inference at the observation le...
### Title: The loss surface and expressivity of deep convolutional neural networks ### Abstract: We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a “wide” laye...
rkvS6-9gG ### Summary: Review ### Review: This paper analyzes the expressiveness and loss surface of deep CNN. I think the paper is clearly written, and has some interesting insights. ### Rating: ratings: final: score: 7, description: Good paper, accept, confidence: score: 2, description: The reviewer is willing to de...
S136E0hZf ### Summary: The loss surface and expressivity of deep convolutional neural networks ### Review: This paper analyzes the loss function and properties of CNNs with one wide layer, i.e., a layer with number of neurons greater than the train sample size. Under this and some additional technique conditions, the ...
BkIW6fYxz ### Summary: Review of The loss surface and expressivity of deep convolutional neural networks ### Review: This paper presents several theoretical results on the loss functions of CNNs and fully-connected neural networks. I summarize the results as follows: (1) Under certain assumptions, if the network cont...
### Title: Seq2SQL: Generating Structured Queries From Natural Language Using Reinforcement Learning ### Abstract: Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural netwo...
ByL2SX9ez ### Summary: Good dataset but problematic claims. ### Review: This work introduces a new semantic parsing dataset, which focuses on generating SQL from natural language. It also proposes a reinforcement-learning based model for this task. First of all, Id like to emphasize that the creation of a large scale...
SkGbiIKxz ### Summary: This is a decent work but contains certain obvious drawbacks ### Review: This paper presents a new approach to support the conversion from natural language to database queries. One of the major contributions of the work is the introduction of a new real-world benchmark dataset based on question...
### Title: Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement ### Abstract: This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. Th...
SkMJBHOez ### Summary: This work suggest how to train a NN in incremental way so for the same performance less memory is needed or for the same memory higher performance can be achieved. ### Review: The idea of this work is fairly simple. Two main problems exist in end devices for deep learning: power and memory. Ther...
BkYwge9ef ### Summary: Not ready yet; needs more work ### Review: There could be an interesting idea here, but the limitations and applicability of the proposed approach are not clear yet. More analysis should be done to clarify its potential. Besides, the paper seriously needs to be reworked. The text in general, but...
### Title: Learning to select examples for program synthesis ### Abstract: Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, that maps the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, it is commonly formul...
SJC_Polgz ### Summary: Interesting work, but underwhelming empirical evaluation. ### Review: The paper proposes a method for identifying representative examples for program synthesis to increase the scalability of existing constraint programming solutions. The authors present their approach and evaluate it empirically...
Bycm6ytgf ### Summary: Interesting formulation, but execution lets the paper down ### Review: This paper presents a method for choosing a subset of examples on which to run a constraint solver in order to solve program synthesis problems. This problem is basically active learning for programming by example, but the co...
### Title: Adversarial Learning for Semi-Supervised Semantic Segmentation ### Abstract: We propose a method for semi-supervised semantic segmentation using the adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a ...
SJRdYLhgM ### Summary: review ### Review: The paper presents an alternative adversarial loss function for image segmentation, and an additional loss for unlabeled images. + well written + good evaluation + good performance compared to prior state of art - technical novelty - semi-supervised loss does not yield signif...
r1RDwROeG ### Summary: not enough for a first-tier conference ### Review: This paper describes techniques for training semantic segmentation networks. There are two key ideas: - Attach a pixel-level GAN loss to the output semantic segmentation map. That is, add a discriminator network that decides whether each pixel ...
### Title: The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling ### Abstract: A variety of learning objectives have been recently proposed for training generative models. We show that many of them, including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, VAE, $\\beta$-VAE, adversar...
S1ufxZqlG ### Summary: Not clear what specific insights exist or what problem this solves ### Review: EDIT: I have read the authors' rebuttals and other reviews. My opinion has not been changed. I recommend the authors significantly revise their work, streamlining the narrative and making clear what problems and solut...
BJ8bKuOlM ### Summary: Contains some interesting results but the presentation is not focused ### Review: Thank you for the feedback, I have read it. I do think that developing unifying frameworks is important. But not all unifying perspective is interesting; rather, a good unifying perspective should identify the beh...
### Title: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models ### Abstract: In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefull...
rJOVWxjez ### Summary: A novel idea with room for future work. ### Review: The authors describe a new defense mechanism against adversarial attacks on classifiers (e.g., FGSM). They propose utilizing Generative Adversarial Networks (GAN), which are usually used for training generative models for an unknown distributio...
BympCwwgf ### Summary: review ### Review: This paper presents a method to cope with adversarial examples in classification tasks, leveraging a generative model of the inputs. Given an accurate generative model of the input, this approach first projects the input onto the manifold learned by the generative model (the ...
### Title: Ground-Truth Adversarial Examples ### Abstract: The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for...
H1TnZzcgz ### Summary: Interesting but not too convincing ### Review: The authors propose to employ provably minimal-distance examples as a tool to evaluate the robustness of a trained network. This is demonstrated on a small-scale network using the MNIST data set. First of all, I find it striking that a trained netw...
S1Q_cbqxf ### Summary: Theoretically interesting but practically maybe limited ### Review: Summary: The paper proposes a method to compute adversarial examples with minimum distance to the original inputs, and to use the method to do two things: Show how well heuristic methods do in finding optimal/minimal adversarial...
### Title: CNNs as Inverse Problem Solvers and Double Network Superresolution ### Abstract: In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. ...
rkHX_Bjlf ### Summary: Official review ### Review: The method proposes a new architecture for solving image super-resolution task. They provide an analysis that connects aims to establish a connection between how CNNs for solving super resolution and solving sparse regularized inverse problems. The writing of the pap...
rkDK2NwgG ### Summary: Interesting paper bringing up different domains. It could be written more reader friendly. ### Review: The paper proposes an understanding of the relation between inverse problems, CNNs and sparse representations. Using the ground work for each proposes a new competitive super resolution techniq...
### Title: Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration ### Abstract: Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to ...
Bk9oIe5gG ### Summary: Interesting, but not substantial enough -> updated now good enough ### Review: The paper investigates different representation learning methods to create a latent space for intrinsic goal generation in guided exploration algorithms. The research is in principle very important and interesting. ...
ByvGgjhez ### Summary: Some interesting ideas, yet no clear message ### Review: [Edit: After revisions, the authors have made a good-faith effort to improve the clarity and presentation of their paper: figures have been revised, key descriptions have been added, and (perhaps most critically) a couple of small sections...
### Title: Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning ### Abstract: Deep lifelong learning systems need to efficiently manage resources to scale to large numbers of experiences and non-stationary goals. In this paper, we explore the relationship between lossy compression and the ...
ryfA9SYez ### Summary: Recollections for efficient deep lifelong learning ### Review: The paper proposes an architecture for efficient deep lifelong learning. The key idea is to use recollection generator (autoencoder) to remember the previously processed data in a compact representation. Then when training a reasonin...
B1GkSWIWM ### Summary: Deep Lifelong learning with recollections under resource constraints. ### Review: This paper presents an approach to lifelong learning with episodic experience storage under resource constraints. The key idea of the approach is to store the latent code obtained from a categorical Variational Aut...
### Title: Word translation without parallel data ### Abstract: State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these...
rJEg3TtxM ### Summary: Review ### Review: The paper proposes a method to learn bilingual dictionaries without parallel data using an adversarial technique. The task is interesting and relevant, especially for in low-resource language pair settings. The paper, however, misses comparison against important work from the...
H1Qhqm9ez ### Summary: Well-rounded contribution, nice read, incomplete related work ### Review: An unsupervised approach is proposed to build bilingual dictionaries without parallel corpora, by aligning the monolingual word embeddings spaces, i.a. via adversarial learning. The paper is very well-written and makes fo...
### Title: Towards Provable Control for Unknown Linear Dynamical Systems ### Abstract: We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state. Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control ...
ryr6tuv-G ### Summary: Interesting approach but maybe more suited for a theory conference (no experiments). ### Review: The paper presents a provable algorithm for controlling an unknown linear dynamical system (LDS). Given the recent interest in (deep) reinforcement learning (combined with the lack of theoretical gua...
SydMCJ9gz ### Summary: Review of Towards Provable Control for Unknown Linear Dynamical Systems ### Review: This paper studies the control of symmetric linear dynamical systems with unknown dynamics. Typically this problem is split into a (non-convex) system ID step followed by a derivation of an optimal controller, bu...
### Title: Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties ### Abstract: Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect....
ryOWEcdlM ### Summary: This paper studies the critical points of shallow and deep linear networks. The authors give a (necessary and sufficient) characterization of the form of critical points and use this to derive necessary and sufficient conditions for which critical points are global optima. While the exposition o...
SJ6btV9gz ### Summary: An interesting work on the characterization of critical points of neural networks ### Review: This paper mainly focuses on the square loss function of linear networks. It provides the sufficient and necessary characterization for the forms of critical points of one-hidden-layer linear networks. ...
### Title: WSNet: Learning Compact and Efficient Networks with Weight Sampling ### Abstract: \tWe present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them vi...
S1xBMQtgG ### Summary: Review ### Review: The paper presents a method to compress deep network by weight sampling and channel sharing. The method combined with weight quantization provides 180x compression with a very small accuracy drop. The method is novel and tested on multiple audio classification datasets and ...
rJRJeMoxz ### Summary: Review ### Review: This paper presents a method for reducing the number of parameters of neural networks by sharing the set of weights in a sliding window manner, and replicating the channels, and finally by quantising weights. The paper is clearly written and results seem compelling but on a pr...
### Title: Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm ### Abstract: Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to...
ByJP4Htez ### Summary: Technically interesting work but practical significance seems highly questionable ### Review: This paper studies the capacity of the model-agnostic meta-learning (MAML) framework as a universal learning algorithm approximator. Since a (supervised) learning algorithm can be interpreted as a map f...
SyTFKLYgf ### Summary: Result looks interesting. Presentation could be further improved. ### Review: The paper tries to address an interesting question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm. The authors provide answers, both the...
### Title: Maximum a Posteriori Policy Optimisation ### Abstract: We introduce a new algorithm for reinforcement learning called Maximum a-posteriori Policy Optimisation (MPO) based on coordinate ascent on a relative-entropy objective. We show that several existing methods can directly be related to our derivation. We ...
H1y3N2alf ### Summary: Interesting off-policy algorithms with nice results ### Review: This is an interesting policy-as-inference approach, presented in a reasonably clear and well-motivated way. I have a couple questions which somewhat echo questions of other commenters here. Unfortunately, I am not sufficiently fami...
Hy4_ANE-f ### Summary: some details to discuss ### Review: This paper studies new off-policy policy optimization algorithm using relative entropy objective and use EM algorithm to solve it. The general idea is not new, aka, formulating the MDP problem as a probabilistic inference problem. There are some technical que...
### Title: Trace norm regularization and faster inference for embedded speech recognition RNNs ### Abstract: We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuou...
By29r_AeG ### Summary: This paper presents a trace norm regularization technique for factorized matrix multiplication with the purpose of overcoming the computational complexity in DNN and RNN ### Review: Paper is well written and clearly explained. The paper is a experimental paper as it has more content on the exper...
Bk-k0Ctgz ### Summary: Model compression with trace norm regularization - pertinent details on experiments missing ### Review: The problem considered in the paper is of compressing large networks (GRUs) for faster inference at test time. The proposed algorithm uses a two step approach: 1) use trace norm regularizati...
### Title: Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games ### Abstract: Many deep reinforcement learning approaches use graphical state representations this means visually distinct games that share the same underlying structure cannot effectively share knowledge. This paper outlines a new ...
SyZl4CKeM ### Summary: Interesting idea maybe? Very poor experimental section. ### Review: This paper introduces a method to learn a policy on visually different but otherwise identical games. While the idea would be interesting in general, unfortunately the experiment section is very much toy example so that it is ha...
H1xFygmyz ### Summary: This paper contains interesting ideas, but it is not ready for publication. ### Review: In this paper, the authors propose a new approach for learning underlying structure of visually distinct games. The proposed approach combines convolutional layers for processing input images, Asynchronous A...
### Title: The Implicit Bias of Gradient Descent on Separable Data ### Abstract: We show that gradient descent on an unregularized logistic regression problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss fun...
HkS9oWtef ### Summary: This paper analyzes the implicit regularization introduced by gradient descent for optimizing the smooth monotone exponential tailed loss function with separable data. The proposed result is very interesting since it illustrates that using gradient descent to minimize such loss function can lead...
HyBrwGweG ### Summary: Very interesting characterisation of limiting behaviour of the log-loss minimisaton ### Review: Paper focuses on characterising behaviour of the log loss minimisation on the linearly separable data. As we know, optimisation like this does not converge in a strict mathematical sense, as the norm ...
### Title: Online Learning Rate Adaptation with Hypergradient Descent ### Abstract: We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of the method in a range of optimization problems by a...
r1jLC23Jf ### Summary: Somewhat weak novelty, but well written, complete, and potentially impactful. ### Review: The authors consider a method (which they trace back to 1998, but may have a longer history) of learning the learning rate of a first-order algorithm at the same time as the underlying model is being optimi...
BJ6v0V9ef ### Summary: interesting idea, but weak experiments ### Review: This paper revisits an interesting and important trick to automatically adapt the stepsize. They consider the stepsize as a parameter to be optimized and apply stochastic gradient update for the stepsize. Such simple trick alleviates the effort ...
### Title: Learning Parsimonious Deep Feed-forward Networks ### Abstract: Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively. However, the structure of standard feed-forward neural networks (FNNs) is simp...
rkDPp89xz ### Summary: Learning Parsimonious Deep Feed-forward Networks ### Review: This paper introduces a skip-connection based design of fully connected networks, which is loosely based on learning latent variable tree structure learning via mutual information criteria. The goal is to learn sparse structures across...
BJ25hzHWf ### Summary: Needs improvement ### Review: The main strengths of the paper are the supporting experimental results in comparison to plain feed-forward networks (FNNs). The proposed method is focused on discovering sparse neural networks. The experiments show that sparsity is achieved and still the discover...
### Title: Latent forward model for Real-time Strategy game planning with incomplete information ### Abstract: Model-free deep reinforcement learning approaches have shown superhuman performance in simulated environments (e.g., Atari games, Go, etc). During training, these approaches often implicitly construct a latent...
BJ-32VOxf ### Summary: Interesting way of re-using pre-trained agents with a lot of room for improvement ### Review: The paper proposes to use a pretrained model-free RL agent to extract the developed state representation and further re-use it for learning forward model of the environment and planning. The idea of re-...
HJh2yfcgz ### Summary: Interesting approach to learning a model, but underperforms model-free methods ### Review: Summary: This paper proposes to use the latent representations learned by a model-free RL agent to learn a transition model for use in model-based RL (specifically MCTS). The paper introduces a strong mode...
### Title: Learning Weighted Representations for Generalization Across Designs ### Abstract: Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to machine learning applications. One example is the estimation of treatment effects from observational data, where a s...
ByozI_rlG ### Summary: Deep architecture for shift invariance in predictive modeling ### Review: This paper proposes a deep learning architecture for joint learning of feature representation, a target-task mapping function, and a sample re-weighting function. Specifically, the method tries to discover feature represen...
H1HywYblM ### Summary: Reweighting for causal inference in absence of confounding ### Review: The paper proposes a novel way of causal inference in situations where in causal SEM notation the outcome Y = f(T,X) is a function of a treatment T and covariates X. The goal is to infer the treatment effect E(Y|T=1,X=x) - E(...