pwc-archive/papers-with-abstracts huggingface dataset:
DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphs
Authors: Xindi Zheng, Yuwei Wu, Yu Pan, WanYu Lin, Lei Ma, Jianjun Zhao
Date: 2024-04-26 00:00:00
Tasks: Generative Adversarial Network, Graph Neural Network, Imputation

Missing data imputation poses a paramount challenge when dealing with graph data. Prior works typically are based on feature propagation or graph autoencoders to address this issue. However, these methods usually encounter the over-smoothing issue when dealing with missing data, as the graph neural network (GNN) modules are not explicitly designed for handling missing data. This paper proposes a novel framework, called Dual-Path Generative Adversarial Network (DPGAN), that can deal simultaneously with missing data and avoid over-smoothing problems. The crux of our work is that it admits both global and local representations of the input graph signal, which can capture the long-range dependencies. It is realized via our proposed generator, consisting of two key components, i.e., MLPUNet++ and GraphUNet++. Our generator is trained with a designated discriminator via an adversarial process. In particular, to avoid assessing the entire graph as did in the literature, our discriminator focuses on the local subgraph fidelity, thereby boosting the quality of the local imputation. The subgraph size is adjustable, allowing for control over the intensity of adversarial regularization. Comprehensive experiments across various benchmark datasets substantiate that DPGAN consistently rivals, if not outperforms, existing state-of-the-art imputation algorithms. The code is provided at \url{https://github.com/momoxia/DPGAN}.
Online $\mathrm{L}^{\natural}$-Convex Minimization
Authors: Ken Yokoyama, Shinji Ito, Tatsuya Matsuoka, Kei Kimura, Makoto Yokoo
Date: 2024-04-26 00:00:00
Tasks: Decision Making

An online decision-making problem is a learning problem in which a player repeatedly makes decisions in order to minimize the long-term loss. These problems that emerge in applications often have nonlinear combinatorial objective functions, and developing algorithms for such problems has attracted considerable attention. An existing general framework for dealing with such objective functions is the online submodular minimization. However, practical problems are often out of the scope of this framework, since the domain of a submodular function is limited to a subset of the unit hypercube. To manage this limitation of the existing framework, we in this paper introduce the online $\mathrm{L}^{\natural}$-convex minimization, where an $\mathrm{L}^{\natural}$-convex function generalizes a submodular function so that the domain is a subset of the integer lattice. We propose computationally efficient algorithms for the online $\mathrm{L}^{\natural}$-convex function minimization in two major settings: the full information and the bandit settings. We analyze the regrets of these algorithms and show in particular that our algorithm for the full information setting obtains a tight regret bound up to a constant factor. We also demonstrate several motivating examples that illustrate the usefulness of the online $\mathrm{L}^{\natural}$-convex minimization.
Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation
Authors: Nanxu Gong, Wangyang Ying, Dongjie Wang, Yanjie Fu
Date: 2024-04-26 00:00:00
Tasks: feature selection

Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall performance. However, existing works are often time-intensive to identify the effective feature subset within high-dimensional feature spaces. Meanwhile, these methods mainly utilize a single downstream task performance as the selection criterion, leading to the selected subsets that are not only redundant but also lack generalizability. To bridge these gaps, we reformulate feature selection through a neuro-symbolic lens and introduce a novel generative framework aimed at identifying short and effective feature subsets. More specifically, we found that feature ID tokens of the selected subset can be formulated as symbols to reflect the intricate correlations among features. Thus, in this framework, we first create a data collector to automatically collect numerous feature selection samples consisting of feature ID tokens, model performance, and the measurement of feature subset redundancy. Building on the collected data, an encoder-decoder-evaluator learning paradigm is developed to preserve the intelligence of feature selection into a continuous embedding space for efficient search. Within the learned embedding space, we leverage a multi-gradient search algorithm to find more robust and generalized embeddings with the objective of improving model performance and reducing feature subset redundancy. These embeddings are then utilized to reconstruct the feature ID tokens for executing the final feature selection. Ultimately, comprehensive experiments and case studies are conducted to validate the effectiveness of the proposed framework.
Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting
Authors: Simon J. Ward, Muhamed Baljevic, Sharon M. Weiss
Date: 2024-04-26 00:00:00
Tasks: Time Series Forecasting, Uncertainty Quantification

The response time of a biosensor is a crucial metric in safety-critical applications such as medical diagnostics where an earlier diagnosis can markedly improve patient outcomes. However, the speed at which a biosensor reaches a final equilibrium state can be limited by poor mass transport and long molecular diffusion times that increase the time it takes target molecules to reach the active sensing region of a biosensor. While optimization of system and sensor design can promote molecules reaching the sensing element faster, a simpler and complementary approach for response time reduction that is widely applicable across all sensor platforms is to use time-series forecasting to predict the ultimate steady-state sensor response. In this work, we show that ensembles of long short-term memory (LSTM) networks can accurately predict equilibrium biosensor response from a small quantity of initial time-dependent biosensor measurements, allowing for significant reduction in response time by a mean and median factor of improvement of 18.6 and 5.1 respectively. The ensemble of models simultaneously estimates uncertainty, which is vital for ensuring confidence in the predictions and subsequent safety-related decisions that are made. This approach is demonstrated on real-time experimental data collected by exposing porous silicon biosensors to buffered protein solutions using a multi-channel fluidic cell that enables the automated measurement of 100 porous silicon biosensors in parallel. The dramatic improvement in sensor response time achieved using LSTM network ensembles and associated uncertainty quantification opens the door to trustworthy and faster responding biosensors, enabling more rapid medical diagnostics for faster clinical decision making that can lead to improved patient outcomes and healthcare access, as well as quicker identification of toxins in food and the environment.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Authors: Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen, Siyuan Zhang, Shun Chen, Hao Gu, Jinming Zhao, Ziyang Ma, Xie Chen, Jiangyan Yi, Rui Liu, Kele Xu, Bin Liu, Erik Cambria, Guoying Zhao, Björn W. Schuller, JianHua Tao
Date: 2024-04-26 00:00:00
Tasks: Emotion Recognition, Multi-Label Learning, Multimodal Emotion Recognition

Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dataset size and building more effective algorithms. However, due to problems such as complex environments and inaccurate annotations, current systems are hard to meet the demands of practical applications. Therefore, we organize the MER series of competitions to promote the development of this field. Last year, we launched MER2023, focusing on three interesting topics: multi-label learning, noise robustness, and semi-supervised learning. In this year's MER2024, besides expanding the dataset size, we further introduce a new track around open-vocabulary emotion recognition. The main purpose of this track is that existing datasets usually fix the label space and use majority voting to enhance the annotator consistency. However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels. In this track, we encourage participants to generate any number of labels in any category, aiming to describe emotional states as accurately as possible. Our baseline code relies on MERTools and is available at: https://github.com/zeroQiaoba/MERTools/tree/master/MER2024.
Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT
Authors: Triet H. M. Le, M. Ali Babar, Tung Hoang Thai
Date: 2024-04-26 00:00:00
Tasks: Prediction

Background: Software Vulnerability (SV) prediction in emerging languages is increasingly important to ensure software security in modern systems. However, these languages usually have limited SV data for developing high-performing prediction models. Aims: We conduct an empirical study to evaluate the impact of SV data scarcity in emerging languages on the state-of-the-art SV prediction model and investigate potential solutions to enhance the performance. Method: We train and test the state-of-the-art model based on CodeBERT with and without data sampling techniques for function-level and line-level SV prediction in three low-resource languages - Kotlin, Swift, and Rust. We also assess the effectiveness of ChatGPT for low-resource SV prediction given its recent success in other domains. Results: Compared to the original work in C/C++ with large data, CodeBERT's performance of function-level and line-level SV prediction significantly declines in low-resource languages, signifying the negative impact of data scarcity. Regarding remediation, data sampling techniques fail to improve CodeBERT; whereas, ChatGPT showcases promising results, substantially enhancing predictive performance by up to 34.4% for the function level and up to 53.5% for the line level. Conclusion: We have highlighted the challenge and made the first promising step for low-resource SV prediction, paving the way for future research in this direction.
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Authors: Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang song, Wee Peng Tay
Date: 2024-04-26 00:00:00
Tasks: Graph Neural Network, Graph Representation Learning, Representation Learning

We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on integer-order differential equations, FROND employs the Caputo fractional derivative to leverage the non-local properties of fractional calculus. This approach enables the capture of long-term dependencies in feature updates, moving beyond the Markovian update mechanisms in conventional integer-order models and offering enhanced capabilities in graph representation learning. We offer an interpretation of the node feature updating process in FROND from a non-Markovian random walk perspective when the feature updating is particularly governed by a diffusion process. We demonstrate analytically that oversmoothing can be mitigated in this setting. Experimentally, we validate the FROND framework by comparing the fractional adaptations of various established integer-order continuous GNNs, demonstrating their consistently improved performance and underscoring the framework's potential as an effective extension to enhance traditional continuous GNNs. The code is available at \url{https://github.com/zknus/ICLR2024-FROND}.
Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks
Authors: Yaqi Hu, Mingsheng Yin, Marco Mezzavilla, Hao Guo, Sundeep Rangan
Date: 2024-04-25 00:00:00

The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data.
Transductive Spiking Graph Neural Networks for Loihi
Authors: Shay Snyder, Victoria Clerico, Guojing Cong, Shruti Kulkarni, Catherine Schuman, Sumedh R. Risbud, Maryam Parsa
Date: 2024-04-25 00:00:00
Tasks: Bayesian Optimization, Graph Classification, Hyperparameter Optimization

Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations.
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Authors: Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob Foerster
Date: 2024-04-25 00:00:00

In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source Generative AI. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact.
Learning Actionable Counterfactual Explanations in Large State Spaces
Authors: Keziah Naggita, Matthew R. Walter, Avrim Blum
Date: 2024-04-25 00:00:00
Tasks: counterfactual

Counterfactual explanations (CFEs) are sets of actions that an agent with a negative classification could take to achieve a (desired) positive classification, for consequential decisions such as loan applications, hiring, admissions, etc. In this work, we consider settings where optimal CFEs correspond to solutions of weighted set cover problems. In particular, there is a collection of actions that agents can perform that each have their own cost and each provide the agent with different sets of capabilities. The agent wants to perform the cheapest subset of actions that together provide all the needed capabilities to achieve a positive classification. Since this is an NP-hard optimization problem, we are interested in the question: can we, from training data (instances of agents and their optimal CFEs) learn a CFE generator that will quickly provide optimal sets of actions for new agents? In this work, we provide a deep-network learning procedure that we show experimentally is able to achieve strong performance at this task. We consider several problem formulations, including formulations in which the underlying "capabilities" and effects of actions are not explicitly provided, and so there is an informational challenge in addition to the computational challenge. Our problem can also be viewed as one of learning an optimal policy in a family of large but deterministic Markov Decision Processes (MDPs).
Out-of-Distribution Detection using Maximum Entropy Coding
Authors: Mojtaba Abolfazli, Mohammad Zaeri Amirani, Anders Høst-Madsen, June Zhang, Andras Bratincsak
Date: 2024-04-25 00:00:00
Tasks: Out-of-Distribution Detection

Given a default distribution $P$ and a set of test data $x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was likely that $x^M$ was generated by $P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-L\"{o}f randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\ldots$. To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with $-\log P(x^M)$. We show that this approach satisfied a number of theoretical properties. For real world data $P$ usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
Authors: Michael Lingzhi Li, Kosuke Imai
Date: 2024-04-25 00:00:00
Tasks: Causal Inference

A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today's scientists across disciplines. In this paper, we demonstrate that Neyman's methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning algorithms. In particular, we show how to account for additional uncertainty resulting from a training process based on cross-fitting. The primary advantage of Neyman's approach is that it can be applied to any ITR regardless of the properties of machine learning algorithms that are used to derive the ITR. We also show, somewhat surprisingly, that for certain metrics, it is more efficient to conduct this ex-post experimental evaluation of an ITR than to conduct an ex-ante experimental evaluation that randomly assigns some units to the ITR. Our analysis demonstrates that Neyman's repeated sampling framework is as relevant for causal inference today as it has been since its inception.
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
Authors: Ruben Ciranni, Giorgio Mariani, Michele Mancusi, Emilian Postolache, Giorgio Fabbro, Emanuele Rodolà, Luca Cosmo
Date: 2024-04-25 00:00:00
Tasks: Contrastive Learning, Music Generation

We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples. Our method operates at the level of the stems composing music tracks and can input features obtained via Harmonic-Percussive Separation (HPS). COCOLA allows the objective evaluation of generative models for music accompaniment generation, which are difficult to benchmark with established metrics. In this regard, we evaluate recent music accompaniment generation models, demonstrating the effectiveness of the proposed method. We release the model checkpoints trained on public datasets containing separate stems (MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales).
A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice
Authors: Juri Opitz
Date: 2024-04-25 00:00:00
Tasks: Misconceptions

Classification systems are evaluated in a countless number of papers. However, we find that evaluation practice is often nebulous. Frequently, metrics are selected without arguments, and blurry terminology invites misconceptions. For instance, many works use so-called 'macro' metrics to rank systems (e.g., 'macro F1') but do not clearly specify what they would expect from such a `macro' metric. This is problematic, since picking a metric can affect research findings, and thus any clarity in the process should be maximized. Starting from the intuitive concepts of bias and prevalence, we perform an analysis of common evaluation metrics. The analysis helps us understand the metrics' underlying properties, and how they align with expectations as found expressed in papers. Then we reflect on the practical situation in the field, and survey evaluation practice in recent shared tasks. We find that metric selection is often not supported with convincing arguments, an issue that can make a system ranking seem arbitrary. Our work aims at providing overview and guidance for more informed and transparent metric selection, fostering meaningful evaluation.
A Notion of Uniqueness for the Adversarial Bayes Classifier
Authors: Natalie S. Frank
Date: 2024-04-25 00:00:00
Tasks: Binary Classification

We propose a new notion of uniqueness for the adversarial Bayes classifier in the setting of binary classification. Analyzing this concept produces a simple procedure for computing all adversarial Bayes classifiers for a well-motivated family of one dimensional data distributions. This characterization is then leveraged to show that as the perturbation radius increases, certain the regularity of adversarial Bayes classifiers improves. Various examples demonstrate that the boundary of the adversarial Bayes classifier frequently lies near the boundary of the Bayes classifier.