pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations
Authors: Jing Wu, Ran Tao, Pan Zhao, Nicolas F. Martin, Naira Hovakimyan
Date: 2022-04-21 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT). We first formulate the N management problem as an RL problem. We then train management policies with deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface that allows for daily interactions between the simulated crop environment and RL agents. According to the experiments on the maize crop in both Iowa and Florida in the US, our RL-trained policies outperform previous empirical methods by achieving higher or similar yield while using less fertilizers
SoftEdge: Regularizing Graph Classification with Random Soft Edges
Authors: Hongyu Guo, Sun Sun
Date: 2022-04-21 00:00:00
Tasks: Classification, Data Augmentation, Graph Classification

Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning. Due to their effectiveness, simple edge and node manipulations (e.g., addition and deletion) have been widely used in graph augmentation. Nevertheless, such common augmentation techniques can dramatically change the semantics of the original graph, causing overaggressive augmentation and thus under-fitting in the GNN learning. To address this problem arising from dropping or adding graph edges and nodes, we propose SoftEdge, which assigns random weights to a portion of the edges of a given graph for augmentation. The synthetic graph generated by SoftEdge maintains the same nodes and their connectivities as the original graph, thus mitigating the semantic changes of the original graph. We empirically show that this simple method obtains superior accuracy to popular node and edge manipulation approaches and notable resilience to the accuracy degradation with the GNN depth.
Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework
Authors: Chase Cockrell, Scott Christley, Gary An
Date: 2022-04-21 00:00:00
Tasks: Code Generation

The Machine Assisted Generation, Comparison, and Calibration (MAGCC) framework provides machine assistance and automation of recurrent crucial steps and processes in the development, implementation, testing, and use of scientific simulation models. MAGCC bridges systems for knowledge extraction via natural language processing or extracted from existing mathematical models and provides a comprehensive workflow encompassing the composition of scientific models and artificial intelligence (AI) assisted code generation. MAGCC accomplishes this through: 1) the development of a comprehensively expressive formal knowledge representation knowledgebase, the Structured Scientific Knowledge Representation (SSKR) that encompasses all the types of information needed to make any simulation model, 2) the use of an artificially intelligent logic reasoning system, the Computational Modeling Assistant (CMA), that takes information from the SSKR and generates, in a traceable fashion, model specifications across a range of simulation modeling methods, and 3) the use of the CMA to generate executable code for a simulation model from those model specifications. The MAGCC framework can be customized any scientific domain, and future work will integrate newly developed code-generating AI systems.
Differentially Private Learning with Margin Guarantees
Authors: Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh
Date: 2022-04-21 00:00:00
Tasks: Model Selection

We present a series of new differentially private (DP) algorithms with dimension-independent margin guarantees. For the family of linear hypotheses, we give a pure DP learning algorithm that benefits from relative deviation margin guarantees, as well as an efficient DP learning algorithm with margin guarantees. We also present a new efficient DP learning algorithm with margin guarantees for kernel-based hypotheses with shift-invariant kernels, such as Gaussian kernels, and point out how our results can be extended to other kernels using oblivious sketching techniques. We further give a pure DP learning algorithm for a family of feed-forward neural networks for which we prove margin guarantees that are independent of the input dimension. Additionally, we describe a general label DP learning algorithm, which benefits from relative deviation margin bounds and is applicable to a broad family of hypothesis sets, including that of neural networks. Finally, we show how our DP learning algorithms can be augmented in a general way to include model selection, to select the best confidence margin parameter.
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
Authors: Gheorghe Comanici, Amelia Glaese, Anita Gergely, Daniel Toyama, Zafarali Ahmed, Tyler Jackson, Philippe Hamel, Doina Precup
Date: 2022-04-21 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were primitive actions. In this work, we study the utility of hierarchical decompositions for learning an appropriate way to interact with a complex interface. Specifically, we train HRL agents that can interface with applications in a simulated Android device. We introduce a Hierarchical Distributed Deep Reinforcement Learning architecture that learns (1) subtasks corresponding to simple finger gestures, and (2) how to combine these gestures to solve several Android tasks. Our approach relies on goal conditioning and can be used more generally to convert any base RL agent into an HRL agent. We use the AndroidEnv environment to evaluate our approach. For the experiments, the HRL agent uses a distributed version of the popular DQN algorithm to train different components of the hierarchy. While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.
Model-free Learning of Regions of Attraction via Recurrent Sets
Authors: Yue Shen, Maxim Bichuch, Enrique Mallada
Date: 2022-04-21 00:00:00

We consider the problem of learning an inner approximation of the region of attraction (ROA) of an asymptotically stable equilibrium point without an explicit model of the dynamics. Rather than leveraging approximate models with bounded uncertainty to find a (robust) invariant set contained in the ROA, we propose to learn sets that satisfy a more relaxed notion of containment known as recurrence. We define a set to be $\tau$-recurrent (resp. $k$-recurrent) if every trajectory that starts within the set, returns to it after at most $\tau$ seconds (resp. $k$ steps). We show that under mild assumptions a $\tau$-recurrent set containing a stable equilibrium must be a subset of its ROA. We then leverage this property to develop algorithms that compute inner approximations of the ROA using counter-examples of recurrence that are obtained by sampling finite-length trajectories. Our algorithms process samples sequentially, which allow them to continue being executed even after an initial offline training stage. We further provide an upper bound on the number of counter-examples used by the algorithm, and almost sure convergence guarantees.
A Framework for Interactive Knowledge-Aided Machine Teaching
Authors: Karan Taneja, Harshvardhan Sikka, Ashok Goel
Date: 2022-04-21 00:00:00
Tasks: text-classification, Text Classification

Machine Teaching (MT) is an interactive process where humans train a machine learning model by playing the role of a teacher. The process of designing an MT system involves decisions that can impact both efficiency of human teachers and performance of machine learners. Previous research has proposed and evaluated specific MT systems but there is limited discussion on a general framework for designing them. We propose a framework for designing MT systems and also detail a system for the text classification problem as a specific instance. Our framework focuses on three components i.e. teaching interface, machine learner, and knowledge base; and their relations describe how each component can benefit the others. Our preliminary experiments show how MT systems can reduce both human teaching time and machine learner error rate.
Provably Efficient Kernelized Q-Learning
Authors: Shuang Liu, Hao Su
Date: 2022-04-21 00:00:00
Tasks: Q-Learning

We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected by the effective dimension of the data. We incorporate such ideas into the Q-learning framework and derive regret bounds for arbitrary kernels. In particular, we provide concrete bounds for linear kernels and Gaussian RBF kernels; notably, the latter bound looks almost identical to the former, only that the actual dimension is replaced by a different notion of dimensionality. Finally, we test our algorithm on a suite of classic control tasks; remarkably, under the Gaussian RBF kernel, it achieves reasonably good performance after only 1000 environmental steps, while its neural network counterpart, deep Q-learning, still struggles.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Authors: Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi Jaakkola
Date: 2022-04-21 00:00:00
Tasks: BIG-bench Machine Learning, Graph Clustering, Graph Neural Network

Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelerating ab-initio MD simulation but are not fast enough for many real-world applications due to slow inference for large systems and small time steps (femtosecond-level). We aim to address these challenges by learning a multi-scale graph neural network that directly simulates coarse-grained MD with a very large time step (nanosecond-level) and a novel refinement module based on diffusion models to mitigate simulation instability. The effectiveness of our method is demonstrated in two complex systems: single-chain coarse-grained polymers and multi-component Li-ion polymer electrolytes. For evaluation, we simulate trajectories much longer than the training trajectories for systems with different chemical compositions that the model is not trained on. Structural and dynamical properties can be accurately recovered at several orders of magnitude higher speed than classical force fields by getting out of the femtosecond regime.
Generative De Novo Protein Design with Global Context
Authors: Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li
Date: 2022-04-21 00:00:00
Tasks: Protein Design, Protein Structure Prediction

The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released.
Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms
Authors: Basma Khelfa, Ibrahima Ba, Antoine Tordeux
Date: 2022-04-20 00:00:00
Tasks: Decision Making, Descriptive, Ensemble Learning

Understanding and predicting highway lane-change maneuvers is essential for driving modeling and its automation. The development of data-based lane-changing decision-making algorithms is nowadays in full expansion. We compare empirically in this article different machine and ensemble learning classification techniques to the MOBIL rule-based model using trajectory data of European two-lane highways. The analysis relies on instantaneous measurements of up to twenty-four spatial-temporal variables with the four neighboring vehicles on current and adjacent lanes. Preliminary descriptive investigations by principal component and logistic analyses allow identifying main variables intending a driver to change lanes. We predict two types of discretionary lane-change maneuvers: overtaking (from the slow to the fast lane) and fold-down (from the fast to the slow lane). The prediction accuracy is quantified using total, lane-changing and lane-keeping errors and associated receiver operating characteristic curves. The benchmark analysis includes logistic model, linear discriminant, decision tree, na\"ive Bayes classifier, support vector machine, neural network machine learning algorithms, and up to ten bagging and stacking ensemble learning meta-heuristics. If the rule-based model provides limited predicting accuracy, the data-based algorithms, devoid of modeling bias, allow significant prediction improvements. Cross validations show that selected neural networks and stacking algorithms allow predicting from a single observation both fold-down and overtaking maneuvers up to four seconds in advance with high accuracy.
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
Authors: Petr Lorenc, Tommaso Gargiani, Jan Pichl, Jakub Konrád, Petr Marek, Ondřej Kobza, Jan Šedivý
Date: 2022-04-22 00:00:00
Tasks: Metric Learning, Open Intent Detection, Out of Distribution (OOD) Detection

Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions. Then, data from different distributions are called out-of-domain (OOD). A robust conversational agent needs to react to these OOD utterances adequately. Thus, the importance of robust OOD detection is emphasized. Unfortunately, collecting OOD data is a challenging task. We have designed an OOD detection algorithm independent of OOD data that outperforms a wide range of current state-of-the-art algorithms on publicly available datasets. Our algorithm is based on a simple but efficient approach of combining metric learning with adaptive decision boundary. Furthermore, compared to other algorithms, we have found that our proposed algorithm has significantly improved OOD performance in a scenario with a lower number of classes while preserving the accuracy for in-domain (IND) classes.
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances
Authors: Seungju Han, Beomsu Kim, Jin Yong Yoo, Seokjun Seo, SangBum Kim, Enkhbayar Erdenee, Buru Chang
Date: 2022-04-22 00:00:00
Proceeding: NAACL 2022 7
Tasks: Chatbot, Retrieval

In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where only a few utterances of each fictional character are available to generate responses mimicking them. Furthermore, we propose a new method named Pseudo Dialog Prompting (PDP) that generates responses by leveraging the power of large-scale language models with prompts containing the target character's utterances. To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history. Since only utterances of the characters are available in the proposed task, PDP matches each utterance with an appropriate pseudo-context from a predefined set of context candidates using a retrieval model. Through human and automatic evaluation, we show that PDP generates responses that better reflect the style of fictional characters than baseline methods.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Authors: Md Mofijul Islam, Gustavo Aguilar, Pragaash Ponnusamy, Clint Solomon Mathialagan, Chengyuan Ma, Chenlei Guo
Date: 2022-04-22 00:00:00
Proceeding: RepL4NLP (ACL) 2022 5
Tasks: Diversity, Sentiment Analysis

Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability to leverage end-to-end task learning. Its frequency-based vocabulary creation compromises tokenization in low-resource languages, leading models to produce suboptimal representations. Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains. In this work, we propose a vocabulary-free neural tokenizer by distilling segmentation information from heuristic-based subword tokenization. We pre-train our character-based tokenizer by processing unique words from multilingual corpus, thereby extensively increasing word diversity across languages. Unlike the predefined and fixed vocabularies in subword methods, our tokenizer allows end-to-end task learning, resulting in optimal task-specific tokenization. The experimental results show that replacing the subword tokenizer with our neural tokenizer consistently improves performance on multilingual (NLI) and code-switching (sentiment analysis) tasks, with larger gains in low-resource languages. Additionally, our neural tokenizer exhibits a robust performance on downstream tasks when adversarial noise is present (typos and misspelling), further increasing the initial improvements over statistical subword tokenizers.
Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review
Authors: Ilia Kuznetsov, Jan Buchmann, Max Eichler, Iryna Gurevych
Date: 2022-04-22 00:00:00

Peer review is a key component of the publishing process in most fields of science. The increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work. While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts -- yet general frameworks and datasets to support this scenario are missing. Relationships between texts are the core object of the intertextuality theory -- a family of approaches in literary studies not yet operationalized in NLP. Inspired by prior theoretical work, we propose the first intertextual model of text-based collaboration, which encompasses three major phenomena that make up a full iteration of the review-revise-and-resubmit cycle: pragmatic tagging, linking and long-document version alignment. While peer review is used across the fields of science and publication formats, existing datasets solely focus on conference-style review in computer science. Addressing this, we instantiate our proposed model in the first annotated multi-domain corpus in journal-style post-publication open peer review, and provide detailed insights into the practical aspects of intertextual annotation. Our resource is a major step towards multi-domain, fine-grained applications of NLP in editorial support for peer review, and our intertextual framework paves the path for general-purpose modeling of text-based collaboration. Our corpus and accompanying code are publicly available.
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
Authors: Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti, Siva Reddy
Date: 2022-04-22 00:00:00
Tasks: Dialogue Generation, Hallucination

The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on knowledge sources. However, dialogue systems often produce unsupported utterances, a phenomenon known as hallucination. To mitigate this behavior, we adopt a data-centric solution and create FaithDial, a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. We observe that FaithDial is more faithful than WoW while also maintaining engaging conversations. We show that FaithDial can serve as training signal for: i) a hallucination critic, which discriminates whether an utterance is faithful or not, and boosts the performance by 12.8 F1 score on the BEGIN benchmark compared to existing datasets for dialogue coherence; ii) high-quality dialogue generation. We benchmark a series of state-of-the-art models and propose an auxiliary contrastive objective that achieves the highest level of faithfulness and abstractiveness based on several automated metrics. Further, we find that the benefits of FaithDial generalize to zero-shot transfer on other datasets, such as CMU-Dog and TopicalChat. Finally, human evaluation reveals that responses generated by models trained on FaithDial are perceived as more interpretable, cooperative, and engaging.
Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions
Authors: Shruti Phadke, Mattia Samory, Tanushree Mitra
Date: 2022-04-22 00:00:00

The disruptive offline mobilization of participants in online conspiracy theory (CT) discussions has highlighted the importance of understanding how online users may form radicalized conspiracy beliefs. While prior work researched the factors leading up to joining online CT discussions and provided theories of how conspiracy beliefs form, we have little understanding of how conspiracy radicalization evolves after users join CT discussion communities. In this paper, we provide the empirical modeling of various radicalization phases in online CT discussion participants. To unpack how conspiracy engagement is related to radicalization, we first characterize the users' journey through CT discussions via conspiracy engagement pathways. Specifically, by studying 36K Reddit users through their 169M contributions, we uncover four distinct pathways of conspiracy engagement: steady high, increasing, decreasing, and steady low. We further model three successive stages of radicalization guided by prior theoretical works. Specific sub-populations of users, namely those on steady high and increasing conspiracy engagement pathways, progress successively through various radicalization stages. In contrast, users on the decreasing engagement pathway show distinct behavior: they limit their CT discussions to specialized topics, participate in diverse discussion groups, and show reduced conformity with conspiracy subreddits. By examining users who disengage from online CT discussions, this paper provides promising insights about conspiracy recovery process.