pwc-archive/papers-with-abstracts huggingface dataset:
Which price to pay? Auto-tuning building MPC controller for optimal economic cost
Authors: Jiarui Yu, Jicheng Shi, Wenjie Xu, Colin N. Jones
Date: 2025-01-18 00:00:00
Tasks: Bayesian Optimization, Computational Efficiency, Hyperparameter Optimization, Model Predictive Control

Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to attain optimal performance under diverse contracts. However, conventional building controller design is an open-loop process without critical hyperparameter optimization, often leading to suboptimal performance due to unexpected environmental disturbances and modeling errors. Furthermore, these hyperparameters are not adapted to different pricing schemes and may lead to non-economic operations. To address these issues, we propose an efficient performance-oriented building MPC controller tuning method based on a cutting-edge efficient constrained Bayesian optimization algorithm, CONFIG, with global optimality guarantees. We demonstrate that this technique can be applied to efficiently deal with real-world DSM program selection problems under customized black-box constraints and objectives. In this study, a simple MPC controller, which offers the advantages of reduced commissioning costs, enhanced computational efficiency, was optimized to perform on a comparable level to a delicately designed and computationally expensive MPC controller. The results also indicate that with an optimized simple MPC, the monthly electricity cost of a household can be reduced by up to 26.90% compared with the cost when controlled by a basic rule-based controller under the same constraints. Then we compared 12 real electricity contracts in Belgium for a household family with customized black-box occupant comfort constraints. The results indicate a monthly electricity bill saving up to 20.18% when the most economic contract is compared with the worst one, which again illustrates the significance of choosing a proper electricity contract.
Learning Nonverbal Cues in Multiparty Social Interactions for Robotic Facilitators
Authors: Antonio Lech Martin-Ozimek, Isuru Jayarathne, Su Larb Mon, Jouhyeong Chew
Date: 2025-01-18 00:00:00

Conventional behavior cloning (BC) models often struggle to replicate the subtleties of human actions. Previous studies have attempted to address this issue through the development of a new BC technique: Implicit Behavior Cloning (IBC). This new technique consistently outperformed the conventional Mean Squared Error (MSE) BC models in a variety of tasks. Our goal is to replicate the performance of the IBC model by Florence [in Proceedings of the 5th Conference on Robot Learning, 164:158-168, 2022], for social interaction tasks using our custom dataset. While previous studies have explored the use of large language models (LLMs) for enhancing group conversations, they often overlook the significance of non-verbal cues, which constitute a substantial part of human communication. We propose using IBC to replicate nonverbal cues like gaze behaviors. The model is evaluated against various types of facilitator data and compared to an explicit, MSE BC model. Results show that the IBC model outperforms the MSE BC model across session types using the same metrics used in the previous IBC paper. Despite some metrics showing mixed results which are explainable for the custom dataset for social interaction, we successfully replicated the IBC model to generate nonverbal cues. Our contributions are (1) the replication and extension of the IBC model, and (2) a nonverbal cues generation model for social interaction. These advancements facilitate the integration of robots into the complex interactions between robots and humans, e.g., in the absence of a human facilitator.
An Interpretable Measure for Quantifying Predictive Dependence between Continuous Random Variables -- Extended Version
Authors: Renato Assunção, Flávio Figueiredo, Francisco N. Tinoco Júnior, Léo M. de Sá-Freire, Fábio Silva
Date: 2025-01-18 00:00:00
Tasks: Benchmarking

A fundamental task in statistical learning is quantifying the joint dependence or association between two continuous random variables. We introduce a novel, fully non-parametric measure that assesses the degree of association between continuous variables $X$ and $Y$, capable of capturing a wide range of relationships, including non-functional ones. A key advantage of this measure is its interpretability: it quantifies the expected relative loss in predictive accuracy when the distribution of $X$ is ignored in predicting $Y$. This measure is bounded within the interval [0,1] and is equal to zero if and only if $X$ and $Y$ are independent. We evaluate the performance of our measure on over 90,000 real and synthetic datasets, benchmarking it against leading alternatives. Our results demonstrate that the proposed measure provides valuable insights into underlying relationships, particularly in cases where existing methods fail to capture important dependencies.
Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation: Finite-Time Analysis
Authors: Siddharth Chandak
Date: 2025-01-18 00:00:00

Two-time-scale stochastic approximation is an iterative algorithm used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point iterations where both time-scales have contractive mappings. In this paper, we study two-time-scale iterations, where the slower time-scale has a non-expansive mapping. For such algorithms, the slower time-scale can be considered a stochastic inexact Krasnoselskii-Mann iteration. We show that the mean square error decays at a rate $O(1/k^{1/4-\epsilon})$, where $\epsilon>0$ is arbitrarily small. We also show almost sure convergence of iterates to the set of fixed points. We show the applicability of our framework by applying our results to minimax optimization, linear stochastic approximation, and Lagrangian optimization.
Jailbreaking Large Language Models in Infinitely Many Ways
Authors: Oliver Goldstein, Emanuele La Malfa, Felix Drinkall, Samuele Marro, Michael Wooldridge
Date: 2025-01-18 00:00:00

We discuss the ``Infinitely Many Paraphrases'' attacks (IMP), a category of jailbreaks that leverages the increasing capabilities of a model to handle paraphrases and encoded communications to bypass their defensive mechanisms. IMPs' viability pairs and grows with a model's capabilities to handle and bind the semantics of simple mappings between tokens and work extremely well in practice, posing a concrete threat to the users of the most powerful LLMs in commerce. We show how one can bypass the safeguards of the most powerful open- and closed-source LLMs and generate content that explicitly violates their safety policies. One can protect against IMPs by improving the guardrails and making them scale with the LLMs' capabilities. For two categories of attacks that are straightforward to implement, i.e., bijection and encoding, we discuss two defensive strategies, one in token and the other in embedding space. We conclude with some research questions we believe should be prioritised to enhance the defensive mechanisms of LLMs and our understanding of their safety.
Dynamic Trend Fusion Module for Traffic Flow Prediction
Authors: Jing Chen, Haocheng Ye, Zhian Ying, Yuntao Sun, Wenqiang Xu
Date: 2025-01-18 00:00:00
Tasks: Prediction, Traffic Prediction

Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the Dynamic Spatial-Temporal Trend Transformer DST2former is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the Dynamic Trend Representation Transformer (DTRformer) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.
Measuring Fairness in Financial Transaction Machine Learning Models
Authors: Deniz Sezin Ayvaz, Lorenzo Belenguer, Hankun He, Deborah Dormah Kanubala, Mingxu Li, Soung Low, Carlos Mougan, Faithful Chiagoziem Onwuegbuche, Yulu Pi, Natalia Sikora, Dan Tran, Shresth Verma, Hanzhi Wang, Skyler Xie, Adeline Pelletier
Date: 2025-01-18 00:00:00
Tasks: Fairness

Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, to tailor bank offerings and maximize revenue opportunities. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency. As part of this effort, Mastercard has sought expertise from the Turing Institute through a Data Study Group to better assess fairness in more complex AI/ML models. The Data Study Group challenge lies in defining, measuring, and mitigating fairness in these predictions, which can be complex due to the various interpretations of fairness, gaps in the research literature, and ML-operations challenges.
Model Monitoring in the Absence of Labeled Data via Feature Attributions Distributions
Authors: Carlos Mougan
Date: 2025-01-18 00:00:00

Model monitoring involves analyzing AI algorithms once they have been deployed and detecting changes in their behaviour. This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. This step is characterized by one particular condition: the absence of labelled data at test time, which makes it challenging, even often impossible, to calculate performance metrics. The thesis is structured around two main themes: (i) AI alignment, measuring if AI models behave in a manner consistent with human values and (ii) performance monitoring, measuring if the models achieve specific accuracy goals or desires. The thesis uses a common methodology that unifies all its sections. It explores feature attribution distributions for both monitoring dimensions. Using these feature attribution explanations, we can exploit their theoretical properties to derive and establish certain guarantees and insights into model monitoring.
An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation
Authors: Yuxuan Dong, Qing Wang, Hengyi Hong, Ya Jiang, Shi Cheng
Date: 2025-01-18 00:00:00
Tasks: Event Detection, Sound Event Detection, Sound Event Localization and Detection

In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound source. The 3D SELD task addresses this limitation by integrating source distance estimation (SDE), allowing for complete spatial localization. We propose three approaches to tackle this challenge: a novel method with independent training and joint prediction, which firstly treats DOA and distance estimation as separate tasks and then combines them to solve 3D SELD; a dual-branch representation with source Cartesian coordinate used for simultaneous DOA and distance estimation; and a three-branch structure that jointly models SED, DOA, and SDE within a unified framework. Our proposed method ranked first in the DCASE 2024 Challenge Task 3, demonstrating the effectiveness of joint modeling for addressing the 3D SELD task. The relevant code for this paper will be open-sourced in the future.
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning
Authors: David Millard, Arielle Carr, Stéphane Gaudreault, Ali Baheri
Date: 2025-01-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jacobi, Incomplete LU, and Algebraic Multigrid methods offer problem-specific advantages but rely heavily on hyperparameter tuning. Recent advances have explored using deep neural networks to learn preconditioners, though challenges such as misbehaved objective functions and costly training procedures remain. PEARL introduces a reinforcement learning approach for learning preconditioners, specifically, a contextual bandit formulation. The framework utilizes an actor-critic model, where the actor generates the incomplete Cholesky decomposition of preconditioners, and the critic evaluates them based on reward-specific feedback. To further guide the training, we design a dual-objective function, combining updates from the critic and condition number. PEARL contributes a generalizable preconditioner learning method, dynamic sparsity exploration, and cosine schedulers for improved stability and exploratory power. We compare our approach to traditional and neural preconditioners, demonstrating improved flexibility and iterative solving speed.
Robust Local Polynomial Regression with Similarity Kernels
Authors: Yaniv Shulman
Date: 2025-01-18 00:00:00
Tasks: Density Estimation, regression

Local Polynomial Regression (LPR) is a widely used nonparametric method for modeling complex relationships due to its flexibility and simplicity. It estimates a regression function by fitting low-degree polynomials to localized subsets of the data, weighted by proximity. However, traditional LPR is sensitive to outliers and high-leverage points, which can significantly affect estimation accuracy. This paper revisits the kernel function used to compute regression weights and proposes a novel framework that incorporates both predictor and response variables in the weighting mechanism. By introducing two positive definite kernels, the proposed method robustly estimates weights, mitigating the influence of outliers through localized density estimation. The method is implemented in Python and is publicly available at https://github.com/yaniv-shulman/rsklpr, demonstrating competitive performance in synthetic benchmark experiments. Compared to standard LPR, the proposed approach consistently improves robustness and accuracy, especially in heteroscedastic and noisy environments, without requiring multiple iterations. This advancement provides a promising extension to traditional LPR, opening new possibilities for robust regression applications.
A Unified Regularization Approach to High-Dimensional Generalized Tensor Bandits
Authors: Jiannan Li, Yiyang Yang, Yao Wang, Shaojie Tang
Date: 2025-01-18 00:00:00
Tasks: Decision Making

Modern decision-making scenarios often involve data that is both high-dimensional and rich in higher-order contextual information, where existing bandits algorithms fail to generate effective policies. In response, we propose in this paper a generalized linear tensor bandits algorithm designed to tackle these challenges by incorporating low-dimensional tensor structures, and further derive a unified analytical framework of the proposed algorithm. Specifically, our framework introduces a convex optimization approach with the weakly decomposable regularizers, enabling it to not only achieve better results based on the tensor low-rankness structure assumption but also extend to cases involving other low-dimensional structures such as slice sparsity and low-rankness. The theoretical analysis shows that, compared to existing low-rankness tensor result, our framework not only provides better bounds but also has a broader applicability. Notably, in the special case of degenerating to low-rank matrices, our bounds still offer advantages in certain scenarios.
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
Authors: Xinglin Pan, WenXiang Lin, Lin Zhang, Shaohuai Shi, Zhenheng Tang, Rui Wang, Bo Li, Xiaowen Chu
Date: 2025-01-18 00:00:00
Tasks: GPU, Mixture-of-Experts, Scheduling

Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces four modules, including token routing, token communication, expert computation, and expert parallelism, that impact model quality and training efficiency. To enable versatile usage of MoE models, we introduce FSMoE, a flexible training system optimizing task scheduling with three novel techniques: 1) Unified abstraction and online profiling of MoE modules for task scheduling across various MoE implementations. 2) Co-scheduling intra-node and inter-node communications with computations to minimize communication overheads. 3) To support near-optimal task scheduling, we design an adaptive gradient partitioning method for gradient aggregation and a schedule to adaptively pipeline communications and computations. We conduct extensive experiments with configured MoE layers and real-world MoE models on two GPU clusters. Experimental results show that 1) our FSMoE supports four popular types of MoE routing functions and is more efficient than existing implementations (with up to a 1.42$\times$ speedup), and 2) FSMoE outperforms the state-of-the-art MoE training systems (DeepSpeed-MoE and Tutel) by 1.18$\times$-1.22$\times$ on 1458 MoE layers and 1.19$\times$-3.01$\times$ on real-world MoE models based on GPT-2 and Mixtral using a popular routing function.
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning
Authors: Arthicha Srisuchinnawong, Poramate Manoonpong
Date: 2025-01-18 00:00:00

Robot locomotion learning using reinforcement learning suffers from training sample inefficiency and exhibits the non-understandable/black-box nature. Thus, this work presents a novel SME-AGOL to address such problems. Firstly, Sequential Motion Executor (SME) is a three-layer interpretable neural network, where the first produces the sequentially propagating hidden states, the second constructs the corresponding triangular bases with minor non-neighbor interference, and the third maps the bases to the motor commands. Secondly, the Adaptable Gradient-weighting Online Learning (AGOL) algorithm prioritizes the update of the parameters with high relevance score, allowing the learning to focus more on the highly relevant ones. Thus, these two components lead to an analyzable framework, where each sequential hidden state/basis represents the learned key poses/robot configuration. Compared to state-of-the-art methods, the SME-AGOL requires 40% fewer samples and receives 150% higher final reward/locomotion performance on a simulated hexapod robot, while taking merely 10 minutes of learning time from scratch on a physical hexapod robot. Taken together, this work not only proposes the SME-AGOL for sample efficient and understandable locomotion learning but also emphasizes the potential exploitation of interpretability for improving sample efficiency and learning performance.
Deep Operator Networks for Bayesian Parameter Estimation in PDEs
Authors: Amogh Raj, Carol Eunice Gudumotou, Sakol Bun, Keerthana Srinivasa, Arash Sarshar
Date: 2025-01-18 00:00:00
Tasks: parameter estimation, PDE Surrogate Modeling, Uncertainty Quantification, Variational Inference

We present a novel framework combining Deep Operator Networks (DeepONets) with Physics-Informed Neural Networks (PINNs) to solve partial differential equations (PDEs) and estimate their unknown parameters. By integrating data-driven learning with physical constraints, our method achieves robust and accurate solutions across diverse scenarios. Bayesian training is implemented through variational inference, allowing for comprehensive uncertainty quantification for both aleatoric and epistemic uncertainties. This ensures reliable predictions and parameter estimates even in noisy conditions or when some of the physical equations governing the problem are missing. The framework demonstrates its efficacy in solving forward and inverse problems, including the 1D unsteady heat equation and 2D reaction-diffusion equations, as well as regression tasks with sparse, noisy observations. This approach provides a computationally efficient and generalizable method for addressing uncertainty quantification in PDE surrogate modeling.
FOCUS: First Order Concentrated Updating Scheme
Authors: Yizhou Liu, Ziming Liu, Jeff Gore
Date: 2025-01-21 00:00:00

Large language models (LLMs) demonstrate remarkable performance, and improving their pre-training process appears to be key to enhancing their capabilities further. Based on the documented success of Adam, learning rate decay, and weight decay, we hypothesize that the pre-training loss landscape features a narrowing valley structure. Through experiments with synthetic loss functions, we discover that when gradient query noise is high relative to the valley's sharpness, Adam's performance falls behind that of Signum because Adam reduces the effective step size too drastically. This observation led us to develop FOCUS, an optimizer that enhances Signum by incorporating attraction toward moving averaged parameters, allowing it to handle noise better while maintaining larger step sizes. In training GPT-2, FOCUS proves to be more stable than Signum and faster than Adam. These results suggest that gradient noise may be an underappreciated limiting factor in LLM training, and FOCUS offers promising solutions.
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token
Authors: Wei Zou, ShuJian Huang, Jiajun Chen
Date: 2025-01-21 00:00:00
Tasks: Machine Translation, NMT, Reinforcement Learning (RL), Translation

Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness. However, popular adversarial policies are apt for fixed tokenization, hindering its efficacy for common character perturbations involving versatile tokenization. Based on existing adversarial generation via reinforcement learning~(RL), we propose the `DexChar policy' that introduces character perturbations for the existing mainstream adversarial policy based on token substitution. Furthermore, we improve the self-supervised matching that provides feedback in RL to cater to the semantic constraints required during training adversaries. Experiments show that our method is compatible with the scenario where baseline adversaries fail, and can generate high-efficiency adversarial examples for analysis and optimization of the system.