United States

Portable Document Format (PDF) files are dominantly used for storing and disseminating scientific research, legal documents, and tax information. LaTeX is a popular application for creating PDF documents. Despite its advantages, LaTeX is not WYSWYG---what you see is what you get, i.e., the LaTeX source and rendered PDF images look drastically different, especially for formulae and tables. This gap makes it hard to modify or export LaTeX sources for formulae and tables from PDF images, and existing work is still limited. First, prior work generates LaTeX sources in a single iteration and struggles with complex LaTeX formulae. Second, existing work mainly recognizes and extracts LaTeX sources for formulae; and is incapable or ineffective for tables. This paper proposes LATTE, the first iterative refinement framework for LaTeX recognition. Specifically, we propose delta-view as feedback, which compares and pinpoints the differences between a pair of rendered images of the extracted LaTeX source and the expected correct image. Such delta-view feedback enables our fault localization model to localize the faulty parts of the incorrect recognition more accurately and enables our LaTeX refinement model to repair the incorrect extraction more accurately. LATTE improves the LaTeX source extraction accuracy of both LaTeX formulae and tables, outperforming existing techniques as well as GPT-4V by at least 7.07% of exact match, with a success refinement rate of 46.08% (formula) and 25.51% (table).

AAAI 2025

LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement

categorization

object detection

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Catastrophic forgetting is when a neural network loses previously learnt information after learning a new task sequentially. If we avoid catastrophic forgetting, it would reduce the resources necessary to update neural networks. Recently, Kolmogorov–Arnold Networks (KAN) gained the community's attention as preliminary experiments suggest KANs avoid catastrophic forgetting (Liu et al. 2024). KANs replace neural network edges with learnable B-splines and sum incoming edges in nodes. Proponents of KANs argue they can avoid forgetting, are more accurate, are interpretable, and use fewer parameters. Our work investigates the claims that KANs avoid catastrophic forgetting, finding that they fail to do so on more complex datasets containing features that overlap between tasks. We give a simple explanation as to why and how KANs catastrophically forget. Motivated by evidence suggesting KANs have improved accuracy on symbolic regression datasets, we augment KANs in the same ways as MLPs to perform continual learning tasks, and we make special accommodations to support some of KANs' peculiarities. Our experiments found that unmodified KANs often forget more than MLPs, but KANs can be better than MLPs when combined with continual learning strategies. We aim to report our findings to the continual learning research community and highlight some of the current shortcomings and strengths associated with KANs for continual learning.

Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP

Simple random negative sampling is a technique used to enhance decision-making in sequential models with numerous potential negative instances, like recommender systems. However, it ignores the patterns that can be discovered in complex sequences to select the most informative negative samples. In this paper, we address this challenge by introducing a Neighborhood-Aware Negative Sampling (NANS) technique in the context of student knowledge modeling (KM) and behavior modeling (BM). In the education domain, KM quantifies student knowledge based on past performance, while BM focuses on behaviors like student preferences of questions. With the vast number of problems to choose from and the intricate relationship between student knowledge and behavior, selecting the proper negative samples becomes a notable challenge in this problem. NANS, along with our proposed multi-objective, multi-task sequential model for KM and BM, NANS-KoBeM frames the simultaneous modeling of student knowledge and question selection as a multi-task learning problem with dual objectives: predicting students’ performance and their question selections.

Neighborhood-Aware Negative Sampling for Student Knowledge and Behavior Modeling

The problem of federated learning (FL) where users are distributed and partitioned into clusters has been addressed through the framework of clustered federated learning (CFL). However, when users are unwilling to share their cluster identities due to privacy concerns, CFL’s training becomes difficult. To address these issues, we introduce an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed method supports training CFL while maintaining user cluster identity confidentiality. It detects potential poisoning attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. 
Client-side overhead is \(O(ml + m^2)\) for communication and \(O(m^2l)\) for computation. When \(m = 1\), computational efficiency is at least \(\log{n}\) times better than other methods, where \(n\) is the number of clients, \(m\) is the number of cluster identities, and \(l\) is the gradient size.
Our method's theoretical efficiency and security are validated through comprehensive analysis and extensive experiments.

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning, multi-objective reinforcement learning, among many others). Despite the long history of MOO, recent years have witnessed a surge in interest within the ML community in the development of gradient manipulation algorithms for MOO, thanks to the availability of gradient information in many ML problems. However, existing gradient manipulation methods for MOO often suffer from long training times, primarily due to the need for computing dynamic weights by solving an additional optimization problem to determine a common descent direction that can decrease all objectives simultaneously. To address this challenge, we propose a new and efficient algorithm called Periodic Stochastic Multi-Gradient Descent (PSMGD) to accelerate MOO. PSMGD is motivated by the key observation that dynamic weights across objectives exhibit small changes under minor updates over short intervals during the optimization process. Consequently, our PSMGD algorithm is designed to periodically compute these dynamic weights and utilizes them repeatedly, thereby effectively reducing the computational overload. Theoretically, we prove that PSMGD can achieve state-of-the-art convergence rates for strongly-convex, general convex, and non-convex functions. Additionally, we introduce a new computational complexity measure, termed backpropagation complexity, and demonstrate that PSMGD could achieve an objective-independent backpropagation complexity. Through extensive experiments, we verify that PSMGD can provide comparable or superior performance to state-of-the-art MOO algorithms while significantly reducing training time.

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization

Quality control is a crucial issue of label data collection by crowdsourcing. Typically, aggregation methods to redundant crowd labels are proposed for estimating high-quality labels from noisy crowd labels. Most of the existing works concentrate on the label aggregation for Single Crowd Tasks (SCTs) which have a single object set with homogeneous question types. However, it is useful for a requester to combine multiple relevant but different crowd tasks into a Composite Crowd Task (CCT) which have heterogeneous question types and (or) multiple object sets for diverse purposes. Instead of the label aggregation on each crowd task respectively, label aggregation methods by bridging multiple SCTs in CCTs can potentially improve the label quality of all tasks. In this paper, we propose a general label aggregation approach for such CCTs by worker ability constraint satisfaction and relaxed optimization. We collected real crowd datasets of CCTs with diverse task settings based on heterogeneous question types, including categorization, pairwise preference comparisons, and pairwise similarity comparisons. The results demonstrate that our approach can effectively bridge the worker information of CCTs to improve the quality of aggregated labels and outperforms the baselines proposed for SCTs.

Label Aggregation for Composite Crowd Tasks by Worker Ability Constraint Satisfaction

Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is introduced by hidden attributes. Existing literature proposed techniques to maximize group-balanced or worst-group accuracy when such correlation presents, yet, at the cost of lower average accuracy. In addition, many existing works conduct surveys on different subpopulation methods without revealing the inherent connection between these methods, which could hinder the technology advancement in this area. In this paper, we identify important sampling as a simple yet powerful tool for solving the subpopulation problem. On the theory side, we provide a new systematic formulation of the subpopulation problem, and explicitly identify the assumptions that are not clearly stated in the existing works. This helps to uncover the cause of the dropped average accuracy. We provide the first theoretical discussion on the connections of existing methods, revealing the core components that make them different. On the application side, we demonstrate a single estimator is enough to solve the subpopulation problem. In particular, we introduce the estimator in both attribute-known and -unknown scenarios in the subpopulation setup, offering flexibility in practical use cases. And empirically, we achieve state-of-the-art performance on commonly used benchmark datasets.

Boosting Test Performance with Importance Sampling--a Subpopulation Perspective

Offline reinforcement learning has shown tremendous success in behavioral planning by learning from previously collected demonstrations. However, decision-making in multitask missions still presents significant challenges. For instance, a mission might require an agent to explore an unknown environment, discover goals, and navigate to them,
even if it involves interacting with obstacles along the way.
Such behavioral planning problems are difficult to solve due
to: a) agents failing to adapt beyond the single task learned
through their reward function, and b) the inability to generalize to new environments not covered in the training demonstrations, e.g., environments where all doors were unlocked in
the demonstrations. Consequently, state-of-the-art decision making methods are limited to missions where the required
tasks are well-represented in the training demonstrations and
can be solved within a short (temporal) planning horizon. To
address this, we propose GenPlan: a stochastic and adaptive
planner that leverages discrete-flow models for generative sequence modeling, enabling sample-efficient exploration and
exploitation. This framework relies on an iterative denoising procedure to generate a sequence of goals and actions.
This approach captures multi-modal action distributions and
facilitates goal and task discovery, thereby enhancing generalization to out-of-distribution tasks and environments, i.e.,
missions not part of the training data. We demonstrate the effectiveness of our method through multiple simulation environments. Notably, GenPlan outperforms the state-of-the-art
methods by over 10% on adaptive planning tasks, where the
agent adapts to multi-task missions while leveraging demonstrations on single-goal-reaching tasks.

GenPlan: Generative Sequence Models as Adaptive Planners

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to improve the homophily and reduce the impact of the heterophily in the graph structure. We also design a contrastive loss based on a self-expression matrix of the node representation matrix to achieve node-level and class-level consistency. Furthermore, we theoretically prove our method to achieve class-level consistency. Extensive experimental results on real datasets verify the effectiveness of the proposed method with respect to various downstream tasks, compared to SOTA methods.

Multiplex Graph Representation Learning with Homophily and Consistency

Prompt-based approaches offer a cutting-edge solution to data privacy issues in continual learning, particularly in scenarios involving multiple data suppliers where long-term storage of private user data is prohibited. Despite delivering state-of-the-art performance, its impressive remembering capability can become a double-edged sword, raising security concerns as it might inadvertently retain poisoned knowledge injected during learning from private user data. Following this insight, in this paper, we expose continual learning to a potential threat: backdoor attack, which drives the model to follow a desired adversarial target whenever a specific trigger is present while still performing normally on clean samples. We highlight three critical challenges in executing backdoor attacks on incremental learners and propose corresponding solutions: (1) Transferability: We employ a surrogate dataset and manipulate prompt selection to transfer backdoor knowledge to data from other suppliers; (2) Resiliency: We simulate static and dynamic states of the victim to ensure the backdoor trigger remains robust during intense incremental learning processes; and (3) Authenticity: We apply binary cross-entropy loss as an anti-cheating factor to prevent the backdoor trigger from devolving into adversarial noise. Extensive experiments across various benchmark datasets and continual learners validate our continual backdoor framework, with further ablation studies confirming our contributions' effectiveness.

Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning

Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning these hyperparameters is a problem of great practical significance. However this is a challenging problem and in certain cases is information theoretically impossible. To address this challenge, we consider a practically relevant transfer learning setting where one has access to offline data collected from several bandit problems (tasks) coming from an unknown distribution over the tasks. Our aim is to use this offline data to set the hyperparameters for a new task drawn from the unknown distribution. We provide bounds on the inter-task (number of tasks) and intra-task (number of arm pulls for each task) sample complexity for learning near-optimal hyperparameters on unseen tasks drawn from the distribution. Our results apply to several classic algorithms, including tuning the exploration parameters in UCB and LinUCB and the noise parameter in GP-UCB. Our experiments indicate the significance and effectiveness of transfer of hyperparameters from offline problems in online learning with stochastic bandit feedback.

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Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP

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