pwc-archive/papers-with-abstracts huggingface dataset:
Event-based Solutions for Human-centered Applications: A Comprehensive Review
Authors: Mira Adra, Simone Melcarne, Nelida Mirabet-Herranz, Jean-Luc Dugelay
Date: 2025-02-17 00:00:00
Tasks: Survey

Event cameras, often referred to as dynamic vision sensors, are groundbreaking sensors capable of capturing changes in light intensity asynchronously, offering exceptional temporal resolution and energy efficiency. These attributes make them particularly suited for human-centered applications, as they capture both the most intricate details of facial expressions and the complex motion dynamics of the human body. Despite growing interest, research in human-centered applications of event cameras remains scattered, with no comprehensive overview encompassing both body and face tasks. This survey bridges that gap by being the first to unify these domains, presenting an extensive review of advancements, challenges, and opportunities. We also examine less-explored areas, including event compression techniques and simulation frameworks, which are essential for the broader adoption of event cameras. This survey is designed to serve as a foundational reference that helps both new and experienced researchers understand the current state of the field and identify promising directions for future work in human-centered event camera applications. A summary of this survey can be found at https://github.com/nmirabeth/event_human
Learning Efficient Positional Encodings with Graph Neural Networks
Authors: Charilaos I. Kanatsoulis, Evelyn Choi, Stephanie Jegelka, Jure Leskovec, Alejandro Ribeiro
Date: 2025-02-03 00:00:00
Tasks: Graph Regression, Graph Representation Learning, Position, Representation Learning

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node features in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity.
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
Authors: Oddy Virgantara Putra, Kohichi Ogata, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo
Date: 2025-01-02 00:00:00
Proceeding: International Journal of Intelligent Engineering and Systems 2025 1
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Point Cloud Linear Classification, Contrastive Learning, Self-Supervised Learning

Manual annotation of large-scale point cloud datasets is laborious due to their irregular structure. While cross-modal contrastive learning methods such as CrossPoint and CrossNet have progressed in utilizing multimodal data for self-supervised learning, they still suffer from instability during training caused by the static weighting of intra-modal (IM) and cross-modal (CM) losses. These static weights fail to account for the varying convergence rates of different modalities. We propose AdaCrossNet, a novel self-supervised learning framework for point cloud understanding that utilizes a dynamic weight adjustment mechanism for IM and CM contrastive learning. AdaCrossNet learns representations by simultaneously enhancing the alignment between 3-D point clouds and their associated 2D- rendered images within a common latent space. Our dynamic weight adjustment mechanism adaptively balances the contributions of IM and CM losses during training, guided by the convergence behavior of each modality. To ensure stability in the training process, we employ an exponentially weighted moving average (EWMA) to smooth the weight updates. We experimented with benchmark datasets, ModelNet40, ShapeNetPart, and ScanObjectNN. The results demonstrate that AdaCrossNet achieves superiority over other methods, with 91.4% accuracy on the ModelNet40 classification task. While on the segmentation task, AdaCrossNet achieved the mIoU score of 85.1% on the ShapeNetPart segmentation task. Additionally, AdaCrossNet, when combined with the DGCNN backbone, showed significant improvements in the ScanObjectNN dataset with 82.1% accuracy. Our method boosts training efficiency while increasing the generalizability of the learned representations across downstream tasks
Interpretable3D: An Ad-Hoc Interpretable Classifier for 3D Point Clouds
Authors: Tuo Feng, Ruijie Quan, Xiaohan Wang, Wenguan Wang, Yi Yang
Date: 2024-03-24 00:00:00
Proceeding: AAAI 2024 3
Tasks: 3D Shape Classification

3D decision-critical tasks urgently require research on explanations to ensure system reliability and transparency. Extensive explanatory research has been conducted on 2D images, but there is a lack in the 3D field. Furthermore, the existing explanations for 3D models are post-hoc and can be misleading, as they separate explanations from the original model. To address these issues, we propose an ad-hoc interpretable classifier for 3D point clouds (i.e., Interpretable3D). As an intuitive case-based classifier, Interpretable3D can provide reliable ad-hoc explanations without any embarrassing nuances. It allows users to understand how queries are embedded within past observations in prototype sets. Interpretable3D has two iterative training steps: 1) updating one prototype with the mean of the embeddings within the same sub-class in Prototype Estimation, and 2) penalizing or rewarding the estimated prototypes in Prototype Optimization. The mean of embeddings has a clear statistical meaning, i.e., class sub-centers. Moreover, we update prototypes with their most similar observations in the last few epochs. Finally, Interpretable3D classifies new samples according to prototypes. We evaluate the performance of Interpretable3D on four popular point cloud models: DGCNN, PointNet2, PointMLP, and PointNeXt. Our Interpretable3D demonstrates comparable or superior performance compared to softmax-based black-box models in the tasks of 3D shape classification and part segmentation.
Habitizing Diffusion Planning for Efficient and Effective Decision Making
Authors: Haofei Lu, Yifei Shen, Dongsheng Li, Junliang Xing, Dongqi Han
Date: 2025-02-10 00:00:00
Tasks: CPU, D4RL, Decision Making

Diffusion models have shown great promise in decision-making, also known as diffusion planning. However, the slow inference speeds limit their potential for broader real-world applications. Here, we introduce Habi, a general framework that transforms powerful but slow diffusion planning models into fast decision-making models, which mimics the cognitive process in the brain that costly goal-directed behavior gradually transitions to efficient habitual behavior with repetitive practice. Even using a laptop CPU, the habitized model can achieve an average 800+ Hz decision-making frequency (faster than previous diffusion planners by orders of magnitude) on standard offline reinforcement learning benchmarks D4RL, while maintaining comparable or even higher performance compared to its corresponding diffusion planner. Our work proposes a fresh perspective of leveraging powerful diffusion models for real-world decision-making tasks. We also provide robust evaluations and analysis, offering insights from both biological and engineering perspectives for efficient and effective decision-making.
What Makes a Good Diffusion Planner for Decision Making?
Authors: Haofei Lu, Dongqi Han, Yifei Shen, Dongsheng Li
Date: 2025-03-01 00:00:00
Proceeding: ICLR 2025 3
Tasks: Action Generation, Decision Making, Denoising, Offline RL, Reinforcement Learning (RL)

Diffusion models have recently shown significant potential in solving
decision-making problems, particularly in generating behavior plans -- also
known as diffusion planning. While numerous studies have demonstrated the
impressive performance of diffusion planning, the mechanisms behind the key
components of a good diffusion planner remain unclear and the design choices
are highly inconsistent in existing studies. In this work, we address this
issue through systematic empirical experiments on diffusion planning in an
offline reinforcement learning (RL) setting, providing practical insights into
the essential components of diffusion planning. We trained and evaluated over
6,000 diffusion models, identifying the critical components such as guided
sampling, network architecture, action generation and planning strategy. We
revealed that some design choices opposite to the common practice in previous
work in diffusion planning actually lead to better performance, e.g.,
unconditional sampling with selection can be better than guided sampling and
Transformer outperforms U-Net as denoising network. Based on these insights, we
suggest a simple yet strong diffusion planning baseline that achieves
state-of-the-art results on standard offline RL benchmarks.
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Authors: Jayden Teoh, Wenjun Li, Pradeep Varakantham
Date: 2025-02-08 00:00:00

Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to guide curriculum design. Regret-driven methods generate curricula that progressively increase environment complexity for the student but overlook environment novelty -- a critical element for enhancing an agent's generalizability. Measuring environment novelty is especially challenging due to the underspecified nature of environment parameters in UED, and existing approaches face significant limitations. To address this, this paper introduces the Coverage-based Evaluation of Novelty In Environment (CENIE) framework. CENIE proposes a scalable, domain-agnostic, and curriculum-aware approach to quantifying environment novelty by leveraging the student's state-action space coverage from previous curriculum experiences. We then propose an implementation of CENIE that models this coverage and measures environment novelty using Gaussian Mixture Models. By integrating both regret and novelty as complementary objectives for curriculum design, CENIE facilitates effective exploration across the state-action space while progressively increasing curriculum complexity. Empirical evaluations demonstrate that augmenting existing regret-based UED algorithms with CENIE achieves state-of-the-art performance across multiple benchmarks, underscoring the effectiveness of novelty-driven autocurricula for robust generalization.
Pausing Policy Learning in Non-stationary Reinforcement Learning
Authors: Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
Date: 2024-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Real-time inference is a challenge of real-world reinforcement learning due to temporal differences in time-varying environments: the system collects data from the past, updates the decision model in the present, and deploys it in the future. We tackle a common belief that continually updating the decision is optimal to minimize the temporal gap. We propose forecasting an online reinforcement learning framework and show that strategically pausing decision updates yields better overall performance by effectively managing aleatoric uncertainty. Theoretically, we compute an optimal ratio between policy update and hold duration, and show that a non-zero policy hold duration provides a sharper upper bound on the dynamic regret. Our experimental evaluations on three different environments also reveal that a non-zero policy hold duration yields higher rewards compared to continuous decision updates.
Tempo Adaptation in Non-stationary Reinforcement Learning
Authors: Hyunin Lee, Yuhao Ding, Jongmin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
Date: 2023-09-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-world applications. In reality, environmental changes occur over wall-clock time ($t$) rather than episode progress ($k$), where wall-clock time signifies the actual elapsed time within the fixed duration $t \in [0, T]$. In existing works, at episode $k$, the agent rolls a trajectory and trains a policy before transitioning to episode $k+1$. In the context of the time-desynchronized environment, however, the agent at time $t_{k}$ allocates $\Delta t$ for trajectory generation and training, subsequently moves to the next episode at $t_{k+1}=t_{k}+\Delta t$. Despite a fixed total number of episodes ($K$), the agent accumulates different trajectories influenced by the choice of interaction times ($t_1,t_2,...,t_K$), significantly impacting the suboptimality gap of the policy. We propose a Proactively Synchronizing Tempo ($\texttt{ProST}$) framework that computes a suboptimal sequence {$t_1,t_2,...,t_K$} (= { $t_{1:K}$}) by minimizing an upper bound on its performance measure, i.e., the dynamic regret. Our main contribution is that we show that a suboptimal {$t_{1:K}$} trades-off between the policy training time (agent tempo) and how fast the environment changes (environment tempo). Theoretically, this work develops a suboptimal {$t_{1:K}$} as a function of the degree of the environment's non-stationarity while also achieving a sublinear dynamic regret. Our experimental evaluation on various high-dimensional non-stationary environments shows that the $\texttt{ProST}$ framework achieves a higher online return at suboptimal {$t_{1:K}$} than the existing methods.
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
Authors: Tianci Liu, Haoyu Wang, Shiyang Wang, Yu Cheng, Jing Gao
Date: 2024-06-01 00:00:00
Tasks: Fairness, Instruction Following, Text Generation

Large language models (LLMs) have achieved impressive performance on various natural language generation tasks. Nonetheless, they suffer from generating negative and harmful contents that are biased against certain demographic groups (e.g., female), raising severe fairness concerns. As remedies, prior works intervened the generation by removing attitude or demographic information, inevitably degrading the generation quality and resulting in notable \textit{fairness-fluency} trade-offs. However, it is still under-explored to what extent the fluency \textit{has to} be affected in order to achieve a desired level of fairness. In this work, we conduct the first formal study from an information-theoretic perspective. We show that previous approaches are excessive for debiasing and propose LIDAO, a general framework to debias a (L)LM at a better fluency provably. We further robustify LIDAO in adversarial scenarios, where a carefully-crafted prompt may stimulate LLMs exhibiting instruction-following abilities to generate texts with fairness issue appears only when the prompt is also taken into account. Experiments on three LMs ranging from 0.7B to 7B parameters demonstrate the superiority of our method.
Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning
Authors: Xuetao Ma, Wenbin Jiang, Hua Huang
Date: 2025-02-21 00:00:00
Tasks: In-Context Learning

In-context learning (ICL) can significantly enhance the complex reasoning capabilities of large language models (LLMs), with the key lying in the selection and ordering of demonstration examples. Previous methods typically relied on simple features to measure the relevance between examples. We argue that these features are not sufficient to reflect the intrinsic connections between examples. In this study, we propose a curriculum ICL strategy guided by problem-solving logic. We select demonstration examples by analyzing the problem-solving logic and order them based on curriculum learning. Specifically, we constructed a problem-solving logic instruction set based on the BREAK dataset and fine-tuned a language model to analyze the problem-solving logic of examples. Subsequently, we selected appropriate demonstration examples based on problem-solving logic and assessed their difficulty according to the number of problem-solving steps. In accordance with the principles of curriculum learning, we ordered the examples from easy to hard to serve as contextual prompts. Experimental results on multiple benchmarks indicate that our method outperforms previous ICL approaches in terms of performance and efficiency, effectively enhancing the complex reasoning capabilities of LLMs. Our project will be publicly available subsequently.
Hybrid uncertainty quantification for selective text classification in ambiguous tasks
Authors: Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov
Date: 2023-07-01 00:00:00
Proceeding: ACL 2023 7
Tasks: text-classification, Text Classification, Uncertainty Quantification

Many text classification tasks are inherently ambiguous, which results in automatic systems having a high risk of making mistakes, in spite of using advanced machine learning models. For example, toxicity detection in user-generated content is a subjective task, and notions of toxicity can be annotated according to a variety of definitions that can be in conflict with one another. Instead of relying solely on automatic solutions, moderation of the most difficult and ambiguous cases can be delegated to human workers. Potential mistakes in automated classification can be identified by using uncertainty estimation (UE) techniques. Although UE is a rapidly growing field within natural language processing, we find that state-of-the-art UE methods estimate only epistemic uncertainty and show poor performance, or under-perform trivial methods for ambiguous tasks such as toxicity detection. We argue that in order to create robust uncertainty estimation methods for ambiguous tasks it is necessary to account also for aleatoric uncertainty. In this paper, we propose a new uncertainty estimation method that combines epistemic and aleatoric UE methods. We show that by using our hybrid method, we can outperform state-of-the-art UE methods for toxicity detection and other ambiguous text classification tasks.
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems
Authors: Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao, Bin Xu, Lei Hou, Juanzi Li
Date: 2025-02-26 00:00:00
Tasks: Instruction Following

Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs). However, existing reward models primarily focus on human preferences, neglecting verifiable correctness signals which have shown strong potential in training LLMs. In this paper, we propose agentic reward modeling, a reward system that combines reward models with verifiable correctness signals from different aspects to provide reliable rewards. We empirically implement a reward agent, named RewardAgent, that combines human preference rewards with two verifiable signals: factuality and instruction following, to provide more reliable rewards. We conduct comprehensive experiments on existing reward model benchmarks and inference time best-of-n searches on real-world downstream tasks. RewardAgent significantly outperforms vanilla reward models, demonstrating its effectiveness. We further construct training preference pairs using RewardAgent and train an LLM with the DPO objective, achieving superior performance on various NLP benchmarks compared to conventional reward models. Our codes are publicly released to facilitate further research (https://github.com/THU-KEG/Agentic-Reward-Modeling).
Scalable Language Models with Posterior Inference of Latent Thought Vectors
Authors: Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu
Date: 2025-02-03 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Text Generation

We propose a novel family of language models, Latent-Thought Language Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. These latent thought vectors guide the autoregressive generation of ground tokens through a Transformer decoder. Training employs a dual-rate optimization process within the classical variational Bayes framework: fast learning of local variational parameters for the posterior distribution of latent vectors, and slow learning of global decoder parameters. Empirical studies reveal that LTMs possess additional scaling dimensions beyond traditional LLMs, yielding a structured design space. Higher sample efficiency can be achieved by increasing training compute per token, with further gains possible by trading model size for more inference steps. Designed based on these scaling properties, LTMs demonstrate superior sample and parameter efficiency compared to conventional autoregressive models and discrete diffusion models. They significantly outperform these counterparts in validation perplexity and zero-shot language modeling. Additionally, LTMs exhibit emergent few-shot in-context reasoning capabilities that scale with model and latent size, and achieve competitive performance in conditional and unconditional text generation.