pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Authors: Amutheezan Sivagnanam, Ava Pettet, Hunter Lee, Ayan Mukhopadhyay, Abhishek Dubey, Aron Laszka
Date: 2024-05-21 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning (RL)

An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated requests. Optimal repositioning is computationally challenging due to the exponential number of ways to allocate responders between locations and the uncertainty in future requests. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search, which may require minutes of computation for each decision in a domain where seconds can save lives. We address the issue of long decision times by introducing a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning. To address the computational challenges posed by large, variable-dimensional, and discrete state and action spaces, we propose: (1) actor-critic based agents that incorporate transformers to handle variable-dimensional states and actions, (2) projections to fixed-dimensional observations to handle complex states, and (3) combinatorial techniques to map continuous actions to discrete allocations. We evaluate our approach using real-world data from two U.S. cities, Nashville, TN and Seattle, WA. Our experiments show that compared to the state of the art, our approach reduces computation time per decision by three orders of magnitude, while also slightly reducing average ambulance response time by 5 seconds.
Stochastic Online Conformal Prediction with Semi-Bandit Feedback
Authors: Haosen Ge, Hamsa Bastani, Osbert Bastani
Date: 2024-05-22 00:00:00
Tasks: Conformal Prediction, image-classification, Image Classification, Prediction, Retrieval, Uncertainty Quantification

Conformal prediction has emerged as an effective strategy for uncertainty quantification by modifying a model to output sets of labels instead of a single label. These prediction sets come with the guarantee that they contain the true label with high probability. However, conformal prediction typically requires a large calibration dataset of i.i.d. examples. We consider the online learning setting, where examples arrive over time, and the goal is to construct prediction sets dynamically. Departing from existing work, we assume semi-bandit feedback, where we only observe the true label if it is contained in the prediction set. For instance, consider calibrating a document retrieval model to a new domain; in this setting, a user would only be able to provide the true label if the target document is in the prediction set of retrieved documents. We propose a novel conformal prediction algorithm targeted at this setting, and prove that it obtains sublinear regret compared to the optimal conformal predictor. We evaluate our algorithm on a retrieval task, an image classification task, and an auction price-setting task, and demonstrate that it empirically achieves good performance compared to several baselines.
Remarks on Loss Function of Threshold Method for Ordinal Regression Problem
Authors: Ryoya Yamasaki, Toshiyuki Tanaka
Date: 2024-05-22 00:00:00
Tasks: regression

Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation. They learn a one-dimensional transformation (1DT) of observations of the explanatory variable, and then assign label predictions to the observations by thresholding their 1DT values. In this paper, we study the influence of the underlying data distribution and of the learning procedure of the 1DT on the classification performance of the threshold method via theoretical considerations and numerical experiments. Consequently, for example, we found that threshold methods based on typical learning procedures may perform poorly when the probability distribution of the target variable conditioned on an observation of the explanatory variable tends to be non-unimodal. Another instance of our findings is that learned 1DT values are concentrated at a few points under the learning procedure based on a piecewise-linear loss function, which can make difficult to classify data well.
On the Challenges of Creating Datasets for Analyzing Commercial Sex Advertisements to Assess Human Trafficking Risk and Organized Activity
Authors: Pablo Rivas, Tomas Cerny, Alejandro Rodriguez Perez, Javier Turek, Laurie Giddens, Gisela Bichler, Stacie Petter
Date: 2024-05-22 00:00:00

Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addressing these issues. We have developed a reproducible and automated methodology to analyze five million advertisements. In the process, we identified further challenges in dataset creation within this sensitive domain. This paper presents a streamlined methodology to assist researchers in constructing effective datasets for combating organized crime, allowing them to focus on advancing detection technologies.
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Authors: Shuai Wang, David W. Zhang, Jia-Hong Huang, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring
Date: 2024-05-22 00:00:00

Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study, we introduce a new adaptive sampling strategy specifically designed for hypergraphs, which tackles their unique complexities in an efficient manner. We also present a Random Hyperedge Augmentation (RHA) technique and an additional Multilayer Perceptron (MLP) module to improve the robustness and generalization capabilities of our approach. Thorough experiments with real-world datasets have proven the effectiveness of our method, markedly reducing computational and memory demands while maintaining performance levels akin to conventional HGNNs and other baseline models. This research paves the way for improving both the scalability and efficacy of HGNNs in extensive applications. We will also make our codebase publicly accessible.
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
Authors: Quyang Pan, Sheng Sun, Zhiyuan Wu, Yuwei Wang, Min Liu, Bo Gao, Jingyuan Wang
Date: 2024-05-22 00:00:00
Tasks: Dataset Distillation, Federated Learning

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. In this paper, we introduce FedCache 2.0, a novel personalized FEL architecture that simultaneously addresses these challenges. FedCache 2.0 incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) FedCache 2.0 significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) FedCache 2.0 can train splendid personalized on-device models with at least $\times$28.6 improvement in communication efficiency.
Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding
Authors: Chang Zhou, Yang Zhao, Jin Cao, Yi Shen, Xiaoling Cui, Chiyu Cheng
Date: 2024-05-22 00:00:00
Tasks: reinforcement-learning

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost efficiency, demonstrating the model's applicability in real-world scenarios.
Gradient Projection For Continual Parameter-Efficient Tuning
Authors: Jingyang Qiao, Zhizhong Zhang, Xin Tan, Yanyun Qu, Wensheng Zhang, Zhi Han, Yuan Xie
Date: 2024-05-22 00:00:00
Tasks: Continual Learning, Hallucination, Zero-shot Generalization

Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, leading to zero-shot generalization collapse, and cross-modal hallucination. In this paper, we reformulate Adapter, LoRA, Prefix-tuning, and Prompt-tuning from the perspective of gradient projection, and firstly propose a unified framework called Parameter Efficient Gradient Projection (PEGP). We introduce orthogonal gradient projection into different PET paradigms and theoretically demonstrate that the orthogonal condition for the gradient can effectively resist forgetting even for large-scale models. It therefore modifies the gradient towards the direction that has less impact on the old feature space, with less extra memory space and training time. We extensively evaluate our method with different backbones, including ViT and CLIP, on diverse datasets, and experiments comprehensively demonstrate its efficiency in reducing forgetting in class, online class, domain, task, and multi-modality continual settings. The project page is available at https://dmcv-ecnu-pegp.github.io/.
Adaptive Fuzzy C-Means with Graph Embedding
Authors: Qiang Chen, Weizhong Yu, Feiping Nie, Xuelong Li
Date: 2024-05-22 00:00:00
Tasks: Clustering, Graph Embedding

Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. However, for almost all existing FCM based methods, how to automatically selecting proper membership degree hyper-parameter values remains a challenging and unsolved problem. Mixture model based methods, while circumventing the difficulty of manually adjusting membership degree hyper-parameters inherent in FCM based methods, often have a preference for specific distributions, such as the Gaussian distribution. In this paper, we propose a novel FCM based clustering model that is capable of automatically learning an appropriate membership degree hyper-parameter value and handling data with non-Gaussian clusters. Moreover, by removing the graph embedding regularization, the proposed FCM model can degenerate into the simplified generalized Gaussian mixture model. Therefore, the proposed FCM model can be also seen as the generalized Gaussian mixture model with graph embedding. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed model.
Latent Space Alignment for Semantic Channel Equalization
Authors: Tomás Hüttebräucker, Mohamed Sana, Emilio Calvanese Strinati
Date: 2024-05-22 00:00:00

We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving. We propose a mathematical framework, which provides a modelling and a measure of the semantic distortion introduced in the communication when agents use distinct languages. We then propose a new approach to semantic channel equalization with proven effectiveness through numerical evaluations.
Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity
Authors: Joshua Southern, Francesco Di Giovanni, Michael Bronstein, Johannes F. Lutzeyer
Date: 2024-05-22 00:00:00
Tasks: Sensitivity

Message passing neural networks (MPNNs) have been shown to have limitations in terms of expressivity and modeling long-range interactions. Augmenting MPNNs with a virtual node (VN) removes the locality constraint of the layer aggregation and has been found to improve performance on a range of benchmarks. We provide a comprehensive theoretical analysis of the role of VNs and benefits thereof, through the lenses of oversmoothing, oversquashing, and sensitivity analysis. First, in contrast to prior belief, we find that VNs typically avoid replicating anti-smoothing approaches to maintain expressive power. Second, we characterize, precisely, how the improvement afforded by VNs on the mixing abilities of the network and hence in mitigating oversquashing, depends on the underlying topology. Finally, we highlight that, unlike Graph-Transformers (GT), classical instantiations of the VN are often constrained to assign uniform importance to different nodes. Consequently, we propose a variant of VN with the same computational complexity, which can have different sensitivity to nodes based on the graph structure. We show that this is an extremely effective and computationally efficient baseline on graph-level tasks.
LogRCA: Log-based Root Cause Analysis for Distributed Services
Authors: Thorsten Wittkopp, Philipp Wiesner, Odej Kao
Date: 2024-05-22 00:00:00
Tasks: Anomaly Detection

To assist IT service developers and operators in managing their increasingly complex service landscapes, there is a growing effort to leverage artificial intelligence in operations. To speed up troubleshooting, log anomaly detection has received much attention in particular, dealing with the identification of log events that indicate the reasons for a system failure. However, faults often propagate extensively within systems, which can result in a large number of anomalies being detected by existing approaches. In this case, it can remain very challenging for users to quickly identify the actual root cause of a failure. We propose LogRCA, a novel method for identifying a minimal set of log lines that together describe a root cause. LogRCA uses a semi-supervised learning approach to deal with rare and unknown errors and is designed to handle noisy data. We evaluated our approach on a large-scale production log data set of 44.3 million log lines, which contains 80 failures, whose root causes were labeled by experts. LogRCA consistently outperforms baselines based on deep learning and statistical analysis in terms of precision and recall to detect candidate root causes. In addition, we investigated the impact of our deployed data balancing approach, demonstrating that it considerably improves performance on rare failures.
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
Authors: Chen-Hao Chao, Chien Feng, Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee
Date: 2024-05-22 00:00:00
Tasks: Ingenuity, MuJoCo, Omniverse Isaac Gym, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the policy evaluation steps, the critic is updated to capture the soft Q-function. In the policy improvement steps, the actor is adjusted in accordance with the updated soft Q-function. In this paper, we introduce a new MaxEnt RL framework modeled using Energy-Based Normalizing Flows (EBFlow). This framework integrates the policy evaluation steps and the policy improvement steps, resulting in a single objective training process. Our method enables the calculation of the soft value function used in the policy evaluation target without Monte Carlo approximation. Moreover, this design supports the modeling of multi-modal action distributions while facilitating efficient action sampling. To evaluate the performance of our method, we conducted experiments on the MuJoCo benchmark suite and a number of high-dimensional robotic tasks simulated by Omniverse Isaac Gym. The evaluation results demonstrate that our method achieves superior performance compared to widely-adopted representative baselines.
Task agnostic continual learning with Pairwise layer architecture
Authors: Santtu Keskinen
Date: 2024-05-22 00:00:00
Tasks: Continual Learning, image-classification, Image Classification

Most of the dominant approaches to continual learning are based on either memory replay, parameter isolation, or regularization techniques that require task boundaries to calculate task statistics. We propose a static architecture-based method that doesn't use any of these. We show that we can improve the continual learning performance by replacing the final layer of our networks with our pairwise interaction layer. The pairwise interaction layer uses sparse representations from a Winner-take-all style activation function to find the relevant correlations in the hidden layer representations. The networks using this architecture show competitive performance in MNIST and FashionMNIST-based continual image classification experiments. We demonstrate this in an online streaming continual learning setup where the learning system cannot access task labels or boundaries.
News Recommendation with Category Description by a Large Language Model
Authors: Yuki Yada, Hayato Yamana
Date: 2024-05-13 00:00:00
Tasks: Articles, Language Modeling, Language Modelling, Large Language Model, News Recommendation

Personalized news recommendations are essential for online news platforms to assist users in discovering news articles that match their interests from a vast amount of online content. Appropriately encoded content features, such as text, categories, and images, are essential for recommendations. Among these features, news categories, such as tv-golden-globe, finance-real-estate, and news-politics, play an important role in understanding news content, inspiring us to enhance the categories' descriptions. In this paper, we propose a novel method that automatically generates informative category descriptions using a large language model (LLM) without manual effort or domain-specific knowledge and incorporates them into recommendation models as additional information. In our comprehensive experimental evaluations using the MIND dataset, our method successfully achieved 5.8% improvement at most in AUC compared with baseline approaches without the LLM's generated category descriptions for the state-of-the-art content-based recommendation models including NAML, NRMS, and NPA. These results validate the effectiveness of our approach. The code is available at https://github.com/yamanalab/gpt-augmented-news-recommendation.
A Systematic Analysis on the Temporal Generalization of Language Models in Social Media
Authors: Asahi Ushio, Jose Camacho-Collados
Date: 2024-05-15 00:00:00
Tasks: Hate Speech Detection, named-entity-recognition, Named Entity Recognition, Sentiment Analysis, Sentiment Classification

In machine learning, temporal shifts occur when there are differences between training and test splits in terms of time. For streaming data such as news or social media, models are commonly trained on a fixed corpus from a certain period of time, and they can become obsolete due to the dynamism and evolving nature of online content. This paper focuses on temporal shifts in social media and, in particular, Twitter. We propose a unified evaluation scheme to assess the performance of language models (LMs) under temporal shift on standard social media tasks. LMs are tested on five diverse social media NLP tasks under different temporal settings, which revealed two important findings: (i) the decrease in performance under temporal shift is consistent across different models for entity-focused tasks such as named entity recognition or disambiguation, and hate speech detection, but not significant in the other tasks analysed (i.e., topic and sentiment classification); and (ii) continuous pre-training on the test period does not improve the temporal adaptability of LMs.
LLMs can learn self-restraint through iterative self-reflection
Authors: Alexandre Piché, Aristides Milios, Dzmitry Bahdanau, Chris Pal
Date: 2024-05-15 00:00:00

In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are trained to maximize the next token likelihood, which does not teach the model to modulate its answer based on its level of uncertainty. In order to learn self-restraint, we devise a utility function that can encourage the model to produce responses only when it is confident in them. This utility function can be used to score generation of different length and abstention. To optimize this function, we introduce ReSearch, a process of "self-reflection" consisting of iterative self-prompting and self-evaluation. We use the ReSearch algorithm to generate synthetic data on which we finetune our models. Compared to their original versions, our resulting models generate fewer \emph{hallucinations} overall at no additional inference cost, for both known and unknown topics, as the model learns to selectively restrain itself. In addition, our method elegantly incorporates the ability to abstain by augmenting the samples generated by the model during the search procedure with an answer expressing abstention.
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation
Authors: David Pogorzelski, Peter Arlinghaus, Wenyan Zhang
Date: 2024-05-22 00:00:00
Tasks: Active Learning, Contrastive Learning

In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks.