pwc-archive/papers-with-abstracts huggingface dataset:
The effect of differential victim crime reporting on predictive policing systems
Authors: Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova
Date: 2021-01-30 00:00:00
Tasks: Fairness

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot\'a, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing.
Rates of convergence for density estimation with generative adversarial networks
Authors: Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov
Date: 2021-01-30 00:00:00
Tasks: Density Estimation

In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs). We prove an oracle inequality for the Jensen-Shannon (JS) divergence between the underlying density $\mathsf{p}^*$ and the GAN estimate with a significantly better statistical error term compared to the previously known results. The advantage of our bound becomes clear in application to nonparametric density estimation. We show that the JS-divergence between the GAN estimate and $\mathsf{p}^*$ decays as fast as $(\log{n}/n)^{2\beta/(2\beta + d)}$, where $n$ is the sample size and $\beta$ determines the smoothness of $\mathsf{p}^*$. This rate of convergence coincides (up to logarithmic factors) with minimax optimal for the considered class of densities.
Spike and slab Bayesian sparse principal component analysis
Authors: Bo Y. -C. Ning, Ning Ning
Date: 2021-01-30 00:00:00
Tasks: Dimensionality Reduction, Variational Inference

Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data. However, there is still a lack of theoretically justified Bayesian SPCA methods that can scale well computationally. One of the major challenges in Bayesian SPCA is selecting an appropriate prior for the loadings matrix, considering that principal components are mutually orthogonal. We propose a novel parameter-expanded coordinate ascent variational inference (PX-CAVI) algorithm. This algorithm utilizes a spike and slab prior, which incorporates parameter expansion to cope with the orthogonality constraint. Besides comparing to two popular SPCA approaches, we introduce the PX-EM algorithm as an EM analogue to the PX-CAVI algorithm for comparison. Through extensive numerical simulations, we demonstrate that the PX-CAVI algorithm outperforms these SPCA approaches, showcasing its superiority in terms of performance. We study the posterior contraction rate of the variational posterior, providing a novel contribution to the existing literature. The PX-CAVI algorithm is then applied to study a lung cancer gene expression dataset. The R package VBsparsePCA with an implementation of the algorithm is available on the Comprehensive R Archive Network (CRAN).
Multilingual Email Zoning
Authors: Bruno Jardim, Ricardo Rei, Mariana S. C. Almeida
Date: 2021-01-31 00:00:00
Proceeding: EACL 2021 2
Tasks: Domain Adaptation, Segmentation, Sentence

The segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. However, despite the multilingual character of emails and their applications, previous literature regarding email zoning corpora and systems was developed essentially for English. In this paper, we analyse the existing email zoning corpora and propose a new multilingual benchmark composed of 625 emails in Portuguese, Spanish and French. Moreover, we introduce OKAPI, the first multilingual email segmentation model based on a language agnostic sentence encoder. Besides generalizing well for unseen languages, our model is competitive with current English benchmarks, and reached new state-of-the-art performances for domain adaptation tasks in English.
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm
Authors: Ambedkar Dukkipati, Rajarshi Banerjee, Ranga Shaarad Ayyagari, Dhaval Parmar Udaybhai
Date: 2021-01-30 00:00:00
Tasks: Autonomous Navigation, Hierarchical Reinforcement Learning, Navigate, Reinforcement Learning (RL)

Solving complex problems using reinforcement learning necessitates breaking down the problem into manageable tasks and learning policies to solve these tasks. These policies, in turn, have to be controlled by a master policy that takes high-level decisions. Hence learning policies involves hierarchical decision structures. However, training such methods in practice may lead to poor generalization, with either sub-policies executing actions for too few time steps or devolving into a single policy altogether. In our work, we introduce an alternative approach to learn such skills sequentially without using an overarching hierarchical policy. We propose this method in the context of environments where a major component of the objective of a learning agent is to prolong the episode for as long as possible. We refer to our proposed method as Sequential Soft Option Critic. We demonstrate the utility of our approach on navigation and goal-based tasks in a flexible simulated 3D navigation environment that we have developed. We also show that our method outperforms prior methods such as Soft Actor-Critic and Soft Option Critic on various environments, including the Atari River Raid environment and the Gym-Duckietown self-driving car simulator.
SNR-adaptive deep joint source-channel coding for wireless image transmission
Authors: Mingze Ding, Jiahui Li, Mengyao Ma, Xiaopeng Fan
Date: 2021-01-30 00:00:00
Tasks: Decoder

Considering the problem of joint source-channel coding (JSCC) for multi-user transmission of images over noisy channels, an autoencoder-based novel deep joint source-channel coding scheme is proposed in this paper. In the proposed JSCC scheme, the decoder can estimate the signal-to-noise ratio (SNR) and use it to adaptively decode the transmitted image. Experiments demonstrate that the proposed scheme achieves impressive results in adaptability for different SNRs and is robust to the decoder's estimation error of the SNR. To the best of our knowledge, this is the first deep JSCC scheme that focuses on the adaptability for different SNRs and can be applied to multi-user scenarios.
MUSE: Multi-Scale Temporal Features Evolution for Knowledge Tracing
Authors: Chengwei Zhang, Yangzhou Jiang, Wei zhang, Chengyu Gu
Date: 2021-01-30 00:00:00
Tasks: Decoder, Knowledge Tracing

Transformer based knowledge tracing model is an extensively studied problem in the field of computer-aided education. By integrating temporal features into the encoder-decoder structure, transformers can processes the exercise information and student response information in a natural way. However, current state-of-the-art transformer-based variants still share two limitations. First, extremely long temporal features cannot well handled as the complexity of self-attention mechanism is O(n2). Second, existing approaches track the knowledge drifts under fixed a window size, without considering different temporal-ranges. To conquer these problems, we propose MUSE, which is equipped with multi-scale temporal sensor unit, that takes either local or global temporal features into consideration. The proposed model is capable to capture the dynamic changes in users knowledge states at different temporal-ranges, and provides an efficient and powerful way to combine local and global features to make predictions. Our method won the 5-th place over 3,395 teams in the Riiid AIEd Challenge 2020.
Fairness through Social Welfare Optimization
Authors: Violet Xinying Chen, J. N. Hooker
Date: 2021-01-30 00:00:00
Tasks: Decision Making, Fairness

We propose social welfare optimization as a general paradigm for formalizing fairness in AI systems. We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology. Rather than attempting to reduce bias between selected groups, one can achieve equity across all groups by incorporating fairness into the social welfare function. This also allows a fuller accounting of the welfare of the individuals involved. We show how to integrate social welfare optimization with both rule-based AI and machine learning, using either an in-processing or a post-processing approach. We present empirical results from a case study as a preliminary examination of the validity and potential of these integration strategies.
Deep Reinforcement Learning-Based Product Recommender for Online Advertising
Authors: Milad Vaali Esfahaani, Yanbo Xue, Peyman Setoodeh
Date: 2021-01-30 00:00:00
Tasks: Deep Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

In online advertising, recommender systems try to propose items from a list of products to potential customers according to their interests. Such systems have been increasingly deployed in E-commerce due to the rapid growth of information technology and availability of large datasets. The ever-increasing progress in the field of artificial intelligence has provided powerful tools for dealing with such real-life problems. Deep reinforcement learning (RL) that deploys deep neural networks as universal function approximators can be viewed as a valid approach for design and implementation of recommender systems. This paper provides a comparative study between value-based and policy-based deep RL algorithms for designing recommender systems for online advertising. The RecoGym environment is adopted for training these RL-based recommender systems, where the long short term memory (LSTM) is deployed to build value and policy networks in these two approaches, respectively. LSTM is used to take account of the key role that order plays in the sequence of item observations by users. The designed recommender systems aim at maximising the click-through rate (CTR) for the recommended items. Finally, guidelines are provided for choosing proper RL algorithms for different scenarios that the recommender system is expected to handle.
Enacted Visual Perception: A Computational Model based on Piaget Equilibrium
Authors: Aref Hakimzadeh, Yanbo Xue, Peyman Setoodeh
Date: 2021-01-30 00:00:00

In Maurice Merleau-Ponty's phenomenology of perception, analysis of perception accounts for an element of intentionality, and in effect therefore, perception and action cannot be viewed as distinct procedures. In the same line of thinking, Alva No\"{e} considers perception as a thoughtful activity that relies on capacities for action and thought. Here, by looking into psychology as a source of inspiration, we propose a computational model for the action involved in visual perception based on the notion of equilibrium as defined by Jean Piaget. In such a model, Piaget's equilibrium reflects the mind's status, which is used to control the observation process. The proposed model is built around a modified version of convolutional neural networks (CNNs) with enhanced filter performance, where characteristics of filters are adaptively adjusted via a high-level control signal that accounts for the thoughtful activity in perception. While the CNN plays the role of the visual system, the control signal is assumed to be a product of mind.
The distance between the weights of the neural network is meaningful
Authors: Liqun Yang, Yijun Yang, Yao Wang, Zhenyu Yang, Wei Zeng
Date: 2021-01-31 00:00:00

In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network's capacity, quantifying the information learned by the network is necessary. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly. The experiment results verify the utility of this method. An application of this method related to the label corruption is shown at the end.
Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness
Authors: Pranay Lohia
Date: 2021-01-31 00:00:00
Tasks: Fairness

Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels. We propose a priority-based post-processing bias mitigation on both group and individual fairness with the notion that similar individuals should get similar outcomes irrespective of socio-economic factors and more the unfairness, more the injustice. We establish this proposition by a case study on tariff allotment in a smart grid. Our novel framework establishes it by using a user segmentation algorithm to capture the consumption strategy better. This process ensures priority-based fair pricing for group and individual facing the maximum injustice. It upholds the notion of fair tariff allotment to the entire population taken into consideration without modifying the in-built process for tariff calculation. We also validate our method and show superior performance to previous work on a real-world dataset in criminal sentencing.
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning
Authors: Saksham Consul, Lovis Heindrich, Jugoslav Stojcheski, Falk Lieder
Date: 2021-01-31 00:00:00
Tasks: Computational Efficiency, Decision Making, reinforcement-learning, Reinforcement Learning (RL), Sequential Decision Making

To make good decisions in the real world people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. But our ability to compute those strategies used to be limited to very small and very simple planning tasks. To overcome this computational bottleneck, we introduce a cognitively-inspired reinforcement learning method that can overcome this limitation by exploiting the hierarchical structure of human behavior. The basic idea is to decompose sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. This hierarchical decomposition enables us to discover optimal strategies for human planning in larger and more complex tasks than was previously possible. The discovered strategies outperform existing planning algorithms and achieve a super-human level of computational efficiency. We demonstrate that teaching people to use those strategies significantly improves their performance in sequential decision-making tasks that require planning up to eight steps ahead. By contrast, none of the previous approaches was able to improve human performance on these problems. These findings suggest that our cognitively-informed approach makes it possible to leverage reinforcement learning to improve human decision-making in complex sequential decision-problems. Future work can leverage our method to develop decision support systems that improve human decision making in the real world.
Using Recursive KMeans and Dijkstra Algorithm to Solve CVRP
Authors: Hassan Moussa
Date: 2021-02-01 00:00:00

Capacitated vehicle routing problem (CVRP) is being one of the most common optimization problems in our days
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development
Authors: Aref Hakimzadeh, Yanbo Xue, Peyman Setoodeh
Date: 2021-02-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Endeavors for designing robots with human-level cognitive abilities have led to different categories of learning machines. According to Skinner's theory, reinforcement learning (RL) plays a key role in human intuition and cognition. Majority of the state-of-the-art methods including deep RL algorithms are strongly influenced by the connectionist viewpoint. Such algorithms can significantly benefit from theories of mind and learning in other disciplines. This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches, which will enrich the RL field. Following this line of thinking, a general computational building block is proposed for Piaget's schema theory that supports the notions of productivity, systematicity, and inferential coherence as described by Fodor in contrast with the connectionism theory. Abstraction in the proposed method is completely upon the system itself and is not externally constrained by any predefined architecture. The whole process matches the Neisser's perceptual cycle model. Performed experiments on three typical control problems followed by behavioral analysis confirm the interpretability of the proposed method and its competitiveness compared to the state-of-the-art algorithms. Hence, the proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.
The Controllability of Planning, Responsibility, and Security in Automatic Driving Technology
Authors: Dan Wan, Hao Zhan
Date: 2021-02-01 00:00:00

People hope automated driving technology is always in a stable and controllable state; specifically, it can be divided into controllable planning, controllable responsibility, and controllable information. When this controllability is undermined, it brings about the problems, e.g., trolley dilemma, responsibility attribution, information leakage, and security. This article discusses these three types of issues separately and clarifies the misunderstandings.
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
Authors: Qisheng Wang, Xiao Li, Shi Jin, Yijiain Chen
Date: 2021-02-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) system. A multi-agent DRL method is proposed to solve the exploration efficiency problem in DRL. In the proposed method, prioritized replay buffer and more informative reward are applied to accelerate the convergence. Simulation results show that the proposed architecture achieves higher spectral efficiency and less time consumption than the benchmarks, thus is more suitable for practical applications.
Counterfactual Planning in AGI Systems
Authors: Koen Holtman
Date: 2021-01-29 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual, Counterfactual Planning

We present counterfactual planning as a design approach for creating a range of safety mechanisms that can be applied in hypothetical future AI systems which have Artificial General Intelligence. The key step in counterfactual planning is to use an AGI machine learning system to construct a counterfactual world model, designed to be different from the real world the system is in. A counterfactual planning agent determines the action that best maximizes expected utility in this counterfactual planning world, and then performs the same action in the real world. We use counterfactual planning to construct an AGI agent emergency stop button, and a safety interlock that will automatically stop the agent before it undergoes an intelligence explosion. We also construct an agent with an input terminal that can be used by humans to iteratively improve the agent's reward function, where the incentive for the agent to manipulate this improvement process is suppressed. As an example of counterfactual planning in a non-agent AGI system, we construct a counterfactual oracle. As a design approach, counterfactual planning is built around the use of a graphical notation for defining mathematical counterfactuals. This two-diagram notation also provides a compact and readable language for reasoning about the complex types of self-referencing and indirect representation which are typically present inside machine learning agents.
Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction
Authors: Tiffany Hwu, Mia Levy, Steven Skorheim, David Huber
Date: 2021-01-30 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Specificity

Rapid non-verbal communication of task-based stimuli is a challenge in human-machine teaming, particularly in closed-loop interactions such as driving. To achieve this, we must understand the representations of information for both the human and machine, and determine a basis for bridging these representations. Techniques of explainable artificial intelligence (XAI) such as layer-wise relevance propagation (LRP) provide visual heatmap explanations for high-dimensional machine learning techniques such as deep neural networks. On the side of human cognition, visual attention is driven by the bottom-up and top-down processing of sensory input related to the current task. Since both XAI and human cognition should focus on task-related stimuli, there may be overlaps between their representations of visual attention, potentially providing a means of nonverbal communication between the human and machine. In this work, we examine the correlations between LRP heatmap explanations of a neural network trained to predict driving behavior and eye gaze heatmaps of human drivers. The analysis is used to determine the feasibility of using such a technique for enhancing driving performance. We find that LRP heatmaps show increasing levels of similarity with eye gaze according to the task specificity of the neural network. We then propose how these findings may assist humans by visually directing attention towards relevant areas. To our knowledge, our work provides the first known analysis of LRP and eye gaze for driving tasks.
Taxonomic survey of Hindi Language NLP systems
Authors: Nikita P. Desai, Prof., Vipul K. Dabhi
Date: 2021-01-30 00:00:00
Tasks: Survey

Natural Language processing (NLP) represents the task of automatic handling of natural human language by machines.There is large spectrum of possible applications of NLP which help in automating tasks like translating text from one language to other, retrieving and summarizing data from very huge repositories, spam email filtering, identifying fake news in digital media, find sentiment and feedback of people, find political opinions and views of people on various government policies, provide effective medical assistance based on past history records of patient etc. Hindi is the official language of India with nearly 691 million users in India and 366 million in rest of world. At present, a number of government and private sector projects and researchers in India and abroad, are working towards developing NLP applications and resources for Indian languages. This survey gives a report of the resources and applications available for Hindi language NLP.