pwc-archive/papers-with-abstracts huggingface dataset:
On evaluation of joint risk for non-negative multivariate risks under dependence uncertainty
Authors: Shuo Gong, Yijun Hu, Linxiao Wei
Date: 2022-12-09 00:00:00

In this paper, we propose a novel axiomatic approach to evaluating the joint risk of multiple insurance risks under dependence uncertainty. Motivated by both the theory of expected utility and the Cobb-Dauglas utility function, we establish a joint risk measure for non-negative multivariate risks, which we refer to as a scalar distortion joint risk measure. After having studied its fundamental properties, we provide an axiomatic characterization of it by proposing a set of new axioms. The most novel axiom is the component-wise positive homogeneity. Then, based on the resulting distortion joint risk measures, we also propose a new class of vector-valued distortion joint risk measures for non-negative multivariate risks. Finally, we make comparisons with some vector-valued multivariate risk measures known in the literature, such as multivariate lower-orthant value at risk, multivariate upper-orthant conditional-tail-expectation, multivariate tail conditional expectation and multivariate tail distortion risk measures. It turns out that those vector-valued multivariate risk measures have forms of vector-valued distortion joint risk measures, respectively. This paper mainly gives some theoretical results about the evaluation of joint risk under dependence uncertainty, and it is expected to be helpful for measuring joint risk.
Blurred Palmprint Recognition Based on Stable-FeatureExtraction Using a Vese-Osher Decomposition Model
Authors: Danfeng Hong, Jian Su, Qinggen Hong, Zhenkuan Pan, Guodong Wang
Date: 2014-07-01 00:00:00
Proceeding: 7 2014 7

Abstract
As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused statusThis dearades the recoanition performance of the svstem, To solve this problem. we propose a stable-feature extractionmethod based on a Vese-0sher (0) decomposition model to recognize blurred palmprints effectively. A Gaussian defocusdegradation model is first established to simulate image blur. With different degrees of bluring, stable features are found toexist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used toobtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees otblurring this is a theoretical conclusion that needs to be further proved via experiment,. Next, an algorithm based onweighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structurelayer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity inthe palmprint features. We also desicned and pertormed a series of exoerments to show the benefits of the proposecmethod. The experimental results are used to demonstrate the theoretica conclusion that the structure laver is stable foidifferent bluring scales The WRHOG method also proves to be an advanced and robust method of distinauishing blurredpalmprints. The recognition results obtained using the proposed method and data from two palmprint databases (Polyland Blurred-PolyU) are stable and superior in comparison to previous high-performance   methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands.
Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
Authors: Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang
Date: 2022-12-12 00:00:00
Tasks: Multi-Armed Bandits, Reinforcement Learning (RL)

Despite the significant interest and progress in reinforcement learning (RL) problems with adversarial corruption, current works are either confined to the linear setting or lead to an undesired $\tilde{O}(\sqrt{T}\zeta)$ regret bound, where $T$ is the number of rounds and $\zeta$ is the total amount of corruption. In this paper, we consider the contextual bandit with general function approximation and propose a computationally efficient algorithm to achieve a regret of $\tilde{O}(\sqrt{T}+\zeta)$. The proposed algorithm relies on the recently developed uncertainty-weighted least-squares regression from linear contextual bandit and a new weighted estimator of uncertainty for the general function class. In contrast to the existing analysis that heavily relies on the linear structure, we develop a novel technique to control the sum of weighted uncertainty, thus establishing the final regret bounds. We then generalize our algorithm to the episodic MDP setting and first achieve an additive dependence on the corruption level $\zeta$ in the scenario of general function approximation. Notably, our algorithms achieve regret bounds either nearly match the performance lower bound or improve the existing methods for all the corruption levels and in both known and unknown $\zeta$ cases.
Client Selection for Federated Bayesian Learning
Authors: Jiarong Yang, YuAn Liu, Rahif Kassab
Date: 2022-12-11 00:00:00

Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server. Since communication resources are limited, selecting the clients with most informative local learning updates can improve the model convergence and communication efficiency. In this paper, we propose two selection schemes for DSVGD based on Kernelized Stein Discrepancy (KSD) and Hilbert Inner Product (HIP). We derive the upper bound on the decrease of the global free energy per iteration for both schemes, which is then minimized to speed up the model convergence. We evaluate and compare our schemes with conventional schemes in terms of model accuracy, convergence speed, and stability using various learning tasks and datasets.
RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding
Authors: Zhengqing Yuan, Xiaolong Zhang, Yue Wang, Xuecong Hou, Huiwen Xue, Zhuanzhe Zhao, Yongming Liu
Date: 2022-12-12 00:00:00
Tasks: CoLA, Data Augmentation, Natural Language Inference, Natural Language Understanding, Sentiment Analysis, SST-2, Text Augmentation, Word Embeddings

Data augmentation is a widely used technique in machine learning to improve model performance. However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets. This paper proposes the Random Position Noise (RPN) algorithm, a novel data augmentation technique that operates at the word vector level. RPN modifies the word embeddings of the original text by introducing noise based on the existing values of selected word vectors, allowing for more fine-grained modifications and better capturing natural language variations. Unlike traditional data augmentation methods, RPN does not require gradients in the computational graph during virtual sample updates, making it simpler to apply to large datasets. Experimental results demonstrate that RPN consistently outperforms existing data augmentation techniques across various NLU tasks, including sentiment analysis, natural language inference, and paraphrase detection. Moreover, RPN performs well in low-resource settings and is applicable to any model featuring a word embeddings layer. The proposed RPN algorithm is a promising approach for enhancing NLU performance and addressing the challenges associated with traditional data augmentation techniques in large-scale NLU tasks. Our experimental results demonstrated that the RPN algorithm achieved state-of-the-art performance in all seven NLU tasks, thereby highlighting its effectiveness and potential for real-world NLU applications.
Semantic Decomposition Improves Learning of Large Language Models on EHR Data
Authors: David A. Bloore, Romane Gauriau, Anna L. Decker, Jacob Oppenheim
Date: 2022-11-14 00:00:00
Tasks: Graph Attention, Medical Diagnosis

Electronic health records (EHR) are widely believed to hold a profusion of actionable insights, encrypted in an irregular, semi-structured format, amidst a loud noise background. To simplify learning patterns of health and disease, medical codes in EHR can be decomposed into semantic units connected by hierarchical graphs. Building on earlier synergy between Bidirectional Encoder Representations from Transformers (BERT) and Graph Attention Networks (GAT), we present H-BERT, which ingests complete graph tree expansions of hierarchical medical codes as opposed to only ingesting the leaves and pushes patient-level labels down to each visit. This methodology significantly improves prediction of patient membership in over 500 medical diagnosis classes as measured by aggregated AUC and APS, and creates distinct representations of patients in closely related but clinically distinct phenotypes.
Graph algorithms for predicting subcellular localization at the pathway level
Authors: Chris S. Magnano, Anthony Gitter
Date: 2022-12-12 00:00:00

Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.
Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments
Authors: Aljaž Božič, Denis Gladkov, Luke Doukakis, Christoph Lassner
Date: 2022-12-12 00:00:00
Tasks: Decoder, Neural Rendering, Object

Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems.
Prompting Is Programming: A Query Language for Large Language Models
Authors: Luca Beurer-Kellner, Marc Fischer, Martin Vechev
Date: 2022-12-12 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Question Answering

Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in a statistically-likely way. Based on this, users prompt these models with language instructions or examples, to implement a variety of downstream tasks. Advanced prompting methods can even imply interaction between the language model, a user, and external tools such as calculators. However, to obtain state-of-the-art performance or adapt language models for specific tasks, complex task- and model-specific programs have to be implemented, which may still require ad-hoc interaction. Based on this, we present the novel idea of Language Model Programming (LMP). LMP generalizes language model prompting from pure text prompts to an intuitive combination of text prompting and scripting. Additionally, LMP allows constraints to be specified over the language model output. This enables easy adaption to many tasks while abstracting language model internals and providing high-level semantics. To enable LMP, we implement LMQL(short for Language Model Query Language), which leverages the constraints and control flow from an LMP prompt to generate an efficient inference procedure that minimizes the number of expensive calls to the underlying language model. We show that LMQL can capture a wide range of state-of-the-art prompting methods in an intuitive way, especially facilitating interactive flows that are challenging to implement with existing high-level APIs. Our evaluation shows that we retain or increase the accuracy on several downstream tasks, while also significantly reducing the required amount of computation or cost in the case of pay-to-use APIs (26-85% cost savings).
Opponent Modeling in Multiplayer Imperfect-Information Games
Authors: Sam Ganzfried, Kevin A. Wang, Max Chiswick
Date: 2022-12-12 00:00:00

In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations
Authors: Amr Farahat, Felix Effenberger, Martin Vinck
Date: 2022-12-12 00:00:00
Tasks: Object, Object Recognition, Specificity

Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification.
The Stable Artist: Steering Semantics in Diffusion Latent Space
Authors: Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, Kristian Kersting
Date: 2022-12-12 00:00:00
Tasks: Image Generation

Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity. To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as 'carbon emission'. We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition.
A Neural ODE Interpretation of Transformer Layers
Authors: Yaofeng Desmond Zhong, Tongtao Zhang, Amit Chakraborty, Biswadip Dey
Date: 2022-12-12 00:00:00
Tasks: image-classification, Image Classification, Numerical Integration

Transformer layers, which use an alternating pattern of multi-head attention and multi-layer perceptron (MLP) layers, provide an effective tool for a variety of machine learning problems. As the transformer layers use residual connections to avoid the problem of vanishing gradients, they can be viewed as the numerical integration of a differential equation. In this extended abstract, we build upon this connection and propose a modification of the internal architecture of a transformer layer. The proposed model places the multi-head attention sublayer and the MLP sublayer parallel to each other. Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks. Moreover, for the image classification task, we show that using neural ODE solvers with a sophisticated integration scheme further improves performance.
Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators
Authors: Pietro Liguori, Cristina Improta, Roberto Natella, Bojan Cukic, Domenico Cotroneo
Date: 2022-12-12 00:00:00
Tasks: Machine Translation, NMT

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses output similarity metrics, i.e., automatic metrics that compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This work analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.
On Computing Probabilistic Abductive Explanations
Authors: Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, Joao Marques-Silva
Date: 2022-12-12 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Authors: Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Toyotaro Suzumura, Shintaro Fukushima
Date: 2022-12-12 00:00:00
Tasks: Decoder, Graph Learning, Graph structure learning, Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting, Traffic Prediction

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.