pwc-archive/papers-with-abstracts huggingface dataset:
A Value-based Trust Assessment Model for Multi-agent Systems
Authors: Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Hoa Khanh Dam
Date: 2019-05-31 00:00:00

An agent's assessment of its trust in another agent is commonly taken to be a measure of the reliability/predictability of the latter's actions. It is based on the trustor's past observations of the behaviour of the trustee and requires no knowledge of the inner-workings of the trustee. However, in situations that are new or unfamiliar, past observations are of little help in assessing trust. In such cases, knowledge about the trustee can help. A particular type of knowledge is that of values - things that are important to the trustor and the trustee. In this paper, based on the premise that the more values two agents share, the more they should trust one another, we propose a simple approach to trust assessment between agents based on values, taking into account if agents trust cautiously or boldly, and if they depend on others in carrying out a task.
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
Authors: Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek
Date: 2019-05-31 00:00:00
Tasks: Binarization, Deep Learning, Distributed Optimization, Management, Quantization

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.
MolecularRNN: Generating realistic molecular graphs with optimized properties
Authors: Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev
Date: 2019-05-31 00:00:00
Tasks: Drug Discovery, Molecular Graph Generation, Reinforcement Learning

Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse realistic molecular graphs after likelihood pretraining on a big database of molecules. We perform an analysis of our pretrained models on large-scale generated datasets of 1 million samples. Further, the model is tuned with policy gradient algorithm, provided a critic that estimates the reward for the property of interest. We show a significant distribution shift to the desired range for lipophilicity, drug-likeness, and melting point outperforming state-of-the-art works. With the use of rejection sampling based on valency constraints, our model yields 100% validity. Moreover, we show that invalid molecules provide a rich signal to the model through the use of structure penalty in our reinforcement learning pipeline.
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Authors: Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros
Date: 2019-05-31 00:00:00
Proceeding: ACL 2019 7
Tasks: AMR Parsing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser
Fast Online "Next Best Offers" using Deep Learning
Authors: Rekha Singhal, Gautam Shroff, Mukund Kumar, Sharod Roy, Sanket Kadarkar, Rupinder virk, Siddharth Verma, Vartika Tiwari
Date: 2019-05-31 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and compares its performance for implementations using different real-time streaming technology stacks. iPrescribe uses an ensemble of deep learning and machine learning algorithms for prediction. We describe the scalable real-time streaming technology stack and optimized machine-learning implementations to achieve a 90th percentile recommendation latency of 38 milliseconds. Optimizations include a novel mechanism to deploy recurrent Long Short Term Memory (LSTM) deep learning networks efficiently.
PAC-Bayes Un-Expected Bernstein Inequality
Authors: Zakaria Mhammedi, Peter D. Grunwald, Benjamin Guedj
Date: 2019-05-31 00:00:00
Proceeding: NeurIPS 2019 12

We present a new PAC-Bayesian generalization bound. Standard bounds contain a $\sqrt{L_n \cdot \KL/n}$ complexity term which dominates unless $L_n$, the empirical error of the learning algorithm's randomized predictions, vanishes. We manage to replace $L_n$ by a term which vanishes in many more situations, essentially whenever the employed learning algorithm is sufficiently stable on the dataset at hand. Our new bound consistently beats state-of-the-art bounds both on a toy example and on UCI datasets (with large enough $n$). Theoretically, unlike existing bounds, our new bound can be expected to converge to $0$ faster whenever a Bernstein/Tsybakov condition holds, thus connecting PAC-Bayesian generalization and {\em excess risk\/} bounds---for the latter it has long been known that faster convergence can be obtained under Bernstein conditions. Our main technical tool is a new concentration inequality which is like Bernstein's but with $X^2$ taken outside its expectation.
Can We Derive Explicit and Implicit Bias from Corpus?
Authors: Bo Wang, Baixiang Xue, Anthony G. Greenwald
Date: 2019-05-31 00:00:00

Language is a popular resource to mine speakers' attitude bias, supposing that speakers' statements represent their bias on concepts. However, psychology studies show that people's explicit bias in statements can be different from their implicit bias in mind. Although both explicit and implicit bias are useful for different applications, current automatic techniques do not distinguish them. Inspired by psychological measurements of explicit and implicit bias, we develop an automatic language-based technique to reproduce psychological measurements on large population. By connecting each psychological measurement with the statements containing the certain combination of special words, we derive explicit and implicit bias by understanding the sentiment of corresponding category of statements. Extensive experiments on English and Chinese serious media (Wikipedia) and non-serious media (social media) show that our method successfully reproduce the small-scale psychological observations on large population and achieve new findings.
Efficient Forward Architecture Search
Authors: Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey
Date: 2019-05-31 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: feature selection, GPU, Lifelong learning, Neural Architecture Search, regression

We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers. The added shortcut connections effectively perform gradient boosting on the augmented layers. The proposed algorithm is motivated by the feature selection algorithm forward stage-wise linear regression, since we consider NAS as a generalization of feature selection for regression, where NAS selects shortcuts among layers instead of selecting features. In order to reduce the number of trials of possible connection combinations, we train jointly all possible connections at each stage of growth while leveraging feature selection techniques to choose a subset of them. We experimentally show this process to be an efficient forward architecture search algorithm that can find competitive models using few GPU days in both the search space of repeatable network modules (cell-search) and the space of general networks (macro-search). Petridish is particularly well-suited for warm-starting from existing models crucial for lifelong-learning scenarios.
Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data
Authors: Fahad AlGhamdi, Mona Diab
Date: 2019-05-31 00:00:00
Proceeding: WS 2019 6
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, Single Particle Analysis, Word Embeddings

Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.
Multi-modal Discriminative Model for Vision-and-Language Navigation
Authors: Haoshuo Huang, Vihan Jain, Harsh Mehta, Jason Baldridge, Eugene Ie
Date: 2019-05-31 00:00:00
Proceeding: WS 2019 6
Tasks: Vision and Language Navigation

Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic environment to achieve prescribed navigation goals. Successful agents must have the ability to parse natural language of varying linguistic styles, ground them in potentially unfamiliar scenes, plan and react with ambiguous environmental feedback. Generalization ability is limited by the amount of human annotated data. In particular, \emph{paired} vision-language sequence data is expensive to collect. We develop a discriminator that evaluates how well an instruction explains a given path in VLN task using multi-modal alignment. Our study reveals that only a small fraction of the high-quality augmented data from \citet{Fried:2018:Speaker}, as scored by our discriminator, is useful for training VLN agents with similar performance on previously unseen environments. We also show that a VLN agent warm-started with pre-trained components from the discriminator outperforms the benchmark success rates of 35.5 by 10\% relative measure on previously unseen environments.
Reinforcement Learning for Mean Field Game
Authors: Mridul Agarwal, Vaneet Aggarwal, Arnob Ghosh, Nilay Tiwari
Date: 2019-05-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stochastic games provide a framework for interactions among multiple agents and enable a myriad of applications. In these games, agents decide on actions simultaneously, the state of every agent moves to the next state, and each agent receives a reward. However, finding an equilibrium (if exists) in this game is often difficult when the number of agents becomes large. This paper focuses on finding a mean-field equilibrium (MFE) in an action coupled stochastic game setting in an episodic framework. It is assumed that the impact of the other agents' can be assumed by the empirical distribution of the mean of the actions. All agents know the action distribution and employ lower-myopic best response dynamics to choose the optimal oblivious strategy. This paper proposes a posterior sampling based approach for reinforcement learning in the mean-field game, where each agent samples a transition probability from the previous transitions. We show that the policy and action distributions converge to the optimal oblivious strategy and the limiting distribution, respectively, which constitute an MFE.
DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
Authors: Rachel Bawden, Sophie Rosset, Thomas Lavergne, Eric Bilinski
Date: 2019-05-30 00:00:00
Tasks: Machine Translation, Sentence, Translation

We present a new English-French test set for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue. The test set contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. The motivation for the corpus is two-fold: to provide (i) a unique resource for evaluating MT models, and (ii) a corpus for the analysis of MT-mediated communication. We provide a preliminary analysis of the corpus to confirm that the participants' judgments reveal perceptible differences in MT quality between the two MT systems used.
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions
Authors: Sabine Wehnert, Sayed Anisul Hoque, Wolfram Fenske, Gunter Saake
Date: 2019-05-30 00:00:00
Tasks: Natural Language Inference, Question Answering, Retrieval, Word Embeddings

Getting an overview over the legal domain has become challenging, especially in a broad, international context. Legal question answering systems have the potential to alleviate this task by automatically retrieving relevant legal texts for a specific statement and checking whether the meaning of the statement can be inferred from the found documents. We investigate a combination of the BM25 scoring method of Elasticsearch with word embeddings trained on English translations of the German and Japanese civil law. For this, we define criteria which select a dynamic number of relevant documents according to threshold scores. Exploiting two deep learning classifiers and their respective prediction bias with a threshold-based answer inclusion criterion has shown to be beneficial for the textual entailment task, when compared to the baseline.
INFaaS: A Model-less and Managed Inference Serving System
Authors: Francisco Romero, Qian Li, Neeraja J. Yadwadkar, Christos Kozyrakis
Date: 2019-05-30 00:00:00
Tasks: Model Selection

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain challenges at large scales. Developers must manually search through thousands of model-variants -- versions of already-trained models that differ in hardware, resource footprints, latencies, costs, and accuracies -- to meet the diverse application requirements. Since requirements, query load, and applications themselves evolve over time, these decisions need to be made dynamically for each inference query to avoid excessive costs through naive autoscaling. To avoid navigating through the large and complex trade-off space of model-variants, developers often fix a variant across queries, and replicate it when load increases. However, given the diversity across variants and hardware platforms in the cloud, a lack of understanding of the trade-off space can incur significant costs to developers. This paper introduces INFaaS, a managed and model-less system for distributed inference serving, where developers simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query. INFaaS generates model-variants, and efficiently navigates the large trade-off space of model-variants on behalf of developers to meet application-specific objectives: (a) for each query, it selects a model, hardware architecture, and model optimizations, (b) it combines VM-level horizontal autoscaling with model-level autoscaling, where multiple, different model-variants are used to serve queries within each machine. By leveraging diverse variants and sharing hardware resources across models, INFaaS achieves 1.3x higher throughput, violates latency objectives 1.6x less often, and saves up to 21.6x in cost (8.5x on average) compared to state-of-the-art inference serving systems on AWS EC2.
Power Weighted Shortest Paths for Clustering Euclidean Data
Authors: Daniel Mckenzie, Steven Damelin
Date: 2019-05-30 00:00:00
Tasks: Clustering

We study the use of power weighted shortest path distance functions for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also present a fast algorithm for computing these distances.
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Authors: Vaishnavh Nagarajan, J. Zico Kolter
Date: 2019-05-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generalization Bounds

The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. So far this observation has been used to provide generalization guarantees only for neural networks whose parameters are either \textit{stochastic} or \textit{compressed}. In this work, we present a general PAC-Bayesian framework that leverages this observation to provide a bound on the original network learned -- a network that is deterministic and uncompressed. What enables us to do this is a key novelty in our approach: our framework allows us to show that if on training data, the interactions between the weight matrices satisfy certain conditions that imply a wide training loss minimum, these conditions themselves {\em generalize} to the interactions between the matrices on test data, thereby implying a wide test loss minimum. We then apply our general framework in a setup where we assume that the pre-activation values of the network are not too small (although we assume this only on the training data). In this setup, we provide a generalization guarantee for the original (deterministic, uncompressed) network, that does not scale with product of the spectral norms of the weight matrices -- a guarantee that would not have been possible with prior approaches.
All SMILES Variational Autoencoder
Authors: Zaccary Alperstein, Artem Cherkasov, Jason Tyler Rolfe
Date: 2019-05-30 00:00:00
Tasks: All, Drug Discovery, Molecular Graph Generation

Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions. To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, pooling hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation. By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior. Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.
All-In-One Underwater Image Enhancement using Domain-Adversarial Learning
Authors: Pritish Uplavikar, Zhen-Yu Wu, Zhangyang Wang
Date: 2019-05-30 00:00:00
Tasks: All, Diversity, Image Enhancement, object-detection, Object Detection, SSIM

Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhancement, by adversarially learning the content features of the images by disentangling the unwanted nuisances corresponding to water types (viewed as different domains). We use the learned domain agnostic features to generate enhanced underwater images. We train our model on a dataset consisting images of 10 Jerlov water types. Experimental results show that the proposed model not only outperforms the previous methods in SSIM and PSNR scores for almost all Jerlov water types but also generalizes well on real-world datasets. The performance of a high-level vision task (object detection) also shows improvement using enhanced images with our model.
On Value Functions and the Agent-Environment Boundary
Authors: Nan Jiang
Date: 2019-05-30 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When function approximation is deployed in reinforcement learning (RL), the same problem may be formulated in different ways, often by treating a pre-processing step as a part of the environment or as part of the agent. As a consequence, fundamental concepts in RL, such as (optimal) value functions, are not uniquely defined as they depend on where we draw this agent-environment boundary, causing problems in theoretical analyses that provide optimality guarantees. We address this issue via a simple and novel boundary-invariant analysis of Fitted Q-Iteration, a representative RL algorithm, where the assumptions and the guarantees are invariant to the choice of boundary. We also discuss closely related issues on state resetting and Monte-Carlo Tree Search, deterministic vs stochastic systems, imitation learning, and the verifiability of theoretical assumptions from data.