pwc-archive/papers-with-abstracts huggingface dataset:
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe
Authors: Hong Qin
Date: 2019-10-22 00:00:00
Tasks: BIG-bench Machine Learning

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach to learn discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are also applicable when effects of special relativity and general relativity are important. The illustrated advantages of discrete field theories relative to continuous theories in terms of machine learning compatibility are consistent with Bostrom's simulation hypothesis.
Influence Maximization for Social Good: Use of Social Networks in Low Resource Communities
Authors: Amulya Yadav
Date: 2019-12-03 00:00:00

This thesis proposal makes the following technical contributions: (i) we provide a definition of the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models the problem faced by homeless shelters accurately; (ii) we propose a novel Partially Observable Markov Decision Process (POMDP) model for solving the DIME problem; (iii) we design two scalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem, since conventional POMDP solvers fail to scale up to sizes of interest; and (iv) we test our algorithms effectiveness in the real world by conducting a pilot study with actual homeless youth in Los Angeles. The success of this pilot (as explained later) shows the promise of using influence maximization for social good on a larger scale.
Artificial Intelligence for Low-Resource Communities: Influence Maximization in an Uncertain World
Authors: Amulya Yadav
Date: 2019-12-03 00:00:00

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.
Expressiveness and Learning of Hidden Quantum Markov Models
Authors: Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon, Byron Boots
Date: 2019-12-02 00:00:00

Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
Authors: Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang
Date: 2019-12-02 00:00:00

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side, especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers, has been remarkable. Yet, while SAT solvers aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF) have become quite mature, SAT solvers that are effective on other types of constraints, e.g., cardinality constraints and XORs, are less well studied; a general approach to handling non-CNF constraints is still lacking. In addition, previous work indicated that for specific classes of benchmarks, the running time of extant SAT solvers depends heavily on properties of the formula and details of encoding, instead of the scale of the benchmarks, which adds uncertainty to expectations of running time. To address the issues above, we design FourierSAT, an incomplete SAT solver based on Fourier analysis of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. Empirical results demonstrate that FourierSAT is more robust than other solvers on certain classes of benchmarks.
Neural Academic Paper Generation
Authors: Samet Demir, Uras Mutlu, Özgur Özdemir
Date: 2019-12-02 00:00:00
Tasks: Language Modeling, Language Modelling, Paper generation, Text Generation

In this work, we tackle the problem of structured text generation, specifically academic paper generation in $\LaTeX{}$, inspired by the surprisingly good results of basic character-level language models. Our motivation is using more recent and advanced methods of language modeling on a more complex dataset of $\LaTeX{}$ source files to generate realistic academic papers. Our first contribution is preparing a dataset with $\LaTeX{}$ source files on recent open-source computer vision papers. Our second contribution is experimenting with recent methods of language modeling and text generation such as Transformer and Transformer-XL to generate consistent $\LaTeX{}$ code. We report cross-entropy and bits-per-character (BPC) results of the trained models, and we also discuss interesting points on some examples of the generated $\LaTeX{}$ code.
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)
Authors: Tilmann Gneiting, Eva-Maria Walz
Date: 2019-11-29 00:00:00
Tasks: Binary Classification

Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been subject to a fundamental restriction, in that it applies to dichotomous (yes or no) outcomes only. Here we introduce ROC movies and universal ROC (UROC) curves that apply to just any linearly ordered outcome, along with an associated coefficient of predictive ability (CPA) measure. CPA equals the area under the UROC curve, and admits appealing interpretations in terms of probabilities and rank based covariances. For binary outcomes CPA equals AUC, and for pairwise distinct outcomes CPA relates linearly to Spearman's coefficient, in the same way that the C index relates linearly to Kendall's coefficient. ROC movies, UROC curves, and CPA nest and generalize the tools of classical ROC analysis, and are bound to supersede them in a wealth of applications. Their usage is illustrated in data examples from biomedicine and meteorology, where rank based measures yield new insights in the WeatherBench comparison of the predictive performance of convolutional neural networks and physical-numerical models for weather prediction.
Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions
Authors: Jonas Tjomsland, Ali Shafti, A. Aldo Faisal
Date: 2019-12-02 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning. Leveraging the sample-efficiency of the Soft Actor-Critic algorithm, we have implemented a robotic platform able to learn a non-trivial collaborative task with a human partner, without pre-training in simulation, and using only 30 minutes of real-world interactions. This enables us to study Human-Robot and Human-Human collaborative learning through real-world interactions. We present preliminary results, showing that state-of-the-art deep learning methods can take human-robot collaborative learning a step closer to that of humans interacting with each other.
A probabilistic assessment of the Indo-Aryan Inner-Outer Hypothesis
Authors: Chundra A. Cathcart
Date: 2019-11-29 00:00:00

This paper uses a novel data-driven probabilistic approach to address the century-old Inner-Outer hypothesis of Indo-Aryan. I develop a Bayesian hierarchical mixed-membership model to assess the validity of this hypothesis using a large data set of automatically extracted sound changes operating between Old Indo-Aryan and Modern Indo-Aryan speech varieties. I employ different prior distributions in order to model sound change, one of which, the logistic normal distribution, has not received much attention in linguistics outside of Natural Language Processing, despite its many attractive features. I find evidence for cohesive dialect groups that have made their imprint on contemporary Indo-Aryan languages, and find that when a logistic normal prior is used, the distribution of dialect components across languages is largely compatible with a core-periphery pattern similar to that proposed under the Inner-Outer hypothesis.
Sparse Bayesian approach for metric learning in latent space
Authors: Davood Zabihzadeh, Reza Monsefi, Hadi Sadoghi Yazdi
Date: 2019-08-15 00:00:00
Proceeding: Knowledge-Based Systems 2019 8
Tasks: Face Recognition, Hyperspectral Image Classification, image-classification, Image Classification, Intrusion Detection, Metric Learning, Network Intrusion Detection

This paper presents a new and efficient approach for metric learning in latent space. Our method
discovers an optimal mapping from the feature space to a latent space that shrinks the distance
between similar data items and also increases the distance between dissimilar ones. The proposed
approach is based on a Bayesian variational framework which iteratively finds the optimal posterior
distribution of parameters and hyperparameters of the model. Advantages of the proposed method to
similar work are 1) Learning the noise of the latent variables on the low-dimensional manifold to find a
more effective transformation. 2) Automatically finding the dimension of latent space and sparsification
of the solution which prevents the overfitting problem. 3) Unlike Mahalanobis metric learning, the
proposed algorithm roughly scales linearly to the dimension of data. Also, the present work is extended
for learning in the feature space induced by an RKHS kernel. The proposed method is evaluated on
small and large datasets coming from real applications such as network intrusion detection, face
recognition, handwritten digits, letter recognition, and hyperspectral image classification. The results
show that our method outperforms related representative and state-of-the-art methods in many small
and large datasets.
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
Authors: C. Boutilier, R. I. Brafman, C. Domshlak, H. H. Hoos, D. Poole
Date: 2011-06-30 00:00:00
Tasks: Decision Making

Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence.
Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol
Authors: Zakia Salod, Yashik Singh
Date: 2019-12-04 00:00:00
Proceeding: Journal of Public Health Research 2019 12
Tasks: BIG-bench Machine Learning, Breast Cancer Detection, feature selection

BACKGROUND:
Breast Cancer (BC) is a known global crisis. The World Health Organization reports a global 2.09 million incidences and 627,000 deaths in 2018 relating to BC. The traditional BC screening method in developed countries is mammography, whilst developing countries employ breast self-examination and clinical breast examination. The prominent gold standard for BC detection is triple assessment: i) clinical examination, ii) mammography and/ or ultrasonography; and iii) Fine Needle Aspirate Cytology. However, the introduction of cheaper, efficient and non-invasive methods of BC screening and detection would be beneficial.  

DESIGN AND METHODS:
We propose the use of eight machine learning algorithms: i) Logistic Regression; ii) Support Vector Machine; iii) K-Nearest Neighbors; iv) Decision Tree; v) Random Forest; vi) Adaptive Boosting; vii) Gradient Boosting; viii) eXtreme Gradient Boosting, and blood test results using BC Coimbra Dataset (BCCD) from University of California Irvine online database to create models for BC prediction. To ensure the models’ robustness, we will employ: i) Stratified k-fold Cross-Validation; ii) Correlation-based Feature Selection (CFS); and iii) parameter tuning. The models will be validated on validation and test sets of BCCD for full features and reduced features. Feature reduction has an impact on algorithm performance. Seven metrics will be used for model evaluation, including accuracy.

EXPECTED IMPACT OF THE STUDY FOR PUBLIC HEALTH:
The CFS together with highest performing model(s) can serve to identify important specific blood tests that point towards BC, which may serve as an important BC biomarker. Highest performing model(s) may eventually be used to create an artificial intelligence tool to assist clinicians in BC screening and detection.
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Authors: Hengyuan Hu, Jakob N. Foerster
Date: 2019-12-04 00:00:00
Proceeding: ICLR 2020 1
Tasks: Decoder, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.
Improving Policies via Search in Cooperative Partially Observable Games
Authors: Adam Lerer, Hengyuan Hu, Jakob Foerster, Noam Brown
Date: 2019-12-05 00:00:00
Tasks: Game of Hanabi

Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just like humans, real-world AI systems have to coordinate and communicate with other agents in cooperative partially observable environments as well. These settings commonly require participants to both interpret the actions of others and to act in a way that is informative when being interpreted. Those abilities are typically summarized as theory f mind and are seen as crucial for social interactions. In this paper we propose two different search techniques that can be applied to improve an arbitrary agreed-upon policy in a cooperative partially observable game. The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy. In contrast, in multi-agent search all agents carry out the same common-knowledge search procedure whenever doing so is computationally feasible, and fall back to playing according to the agreed-upon policy otherwise. We prove that these search procedures are theoretically guaranteed to at least maintain the original performance of the agreed-upon policy (up to a bounded approximation error). In the benchmark challenge problem of Hanabi, our search technique greatly improves the performance of every agent we tested and when applied to a policy trained using RL achieves a new state-of-the-art score of 24.61 / 25 in the game, compared to a previous-best of 24.08 / 25.
Inter-Level Cooperation in Hierarchical Reinforcement Learning
Authors: Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco, Samyak Parajuli, Sergey Levine, Alexandre M. Bayen
Date: 2019-12-05 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach an end-to-end training paradigm is needed. However, training these multi-level policies has had limited success due to challenges arising from interactions between the goal-assigning and goal-achieving levels within a hierarchy. In this article, we consider the policy optimization process as a multi-agent process. This allows us to draw on connections between communication and cooperation in multi-agent RL, and demonstrate the benefits of increased cooperation between sub-policies on the training performance of the overall policy. We introduce a simple yet effective technique for inducing inter-level cooperation by modifying the objective function and subsequent gradients of higher-level policies. Experimental results on a wide variety of simulated robotics and traffic control tasks demonstrate that inducing cooperation results in stronger performing policies and increased sample efficiency on a set of difficult long time horizon tasks. We also find that goal-conditioned policies trained using our method display better transfer to new tasks, highlighting the benefits of our method in learning task-agnostic lower-level behaviors. Videos and code are available at: https://sites.google.com/berkeley.edu/cooperative-hrl.
Towards Explainable Deep Neural Networks (xDNN)
Authors: Plamen Angelov, Eduardo Soares
Date: 2019-12-05 00:00:00
Tasks: Deep Learning

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds). The proposed approach, xDNN is using prototypes. Prototypes are actual training data samples (images), which are local peaks of the empirical data distribution called typicality as well as of the data density. This generative model is identified in a closed form and equates to the pdf but is derived automatically and entirely from the training data with no user- or problem-specific thresholds, parameters or intervention. The proposed xDNN offers a new deep learning architecture that combines reasoning and learning in a synergy. It is non-iterative and non-parametric, which explains its efficiency in terms of time and computational resources. From the user perspective, the proposed approach is clearly understandable to human users. We tested it on some well-known benchmark data sets such as iRoads and Caltech-256. xDNN outperforms the other methods including deep learning in terms of accuracy, time to train and offers a clearly explainable classifier. In fact, the result on the very hard Caltech-256 problem (which has 257 classes) represents a world record.