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Exclusion is an important and universal linguistic skill that humans use to express what they do not want. There is little research on exclusionary retrieval, where users express what they do not want to be part of the results produced for their queries. We investigate the scenario of exclusionary retrieval in document retrieval for the first time. We present ExcluIR, a set of resources for exclusionary retrieval, consisting of an evaluation benchmark and a training set for helping retrieval models to comprehend exclusionary queries. The evaluation benchmark includes 3,452 high-quality exclusionary queries, each of which has been manually annotated. The training set contains 70,293 exclusionary queries, each paired with a positive document and a negative document. We conduct detailed experiments and analyses, obtaining three main observations: (i) existing retrieval models with different architectures struggle to comprehend exclusionary queries effectively; (ii) although integrating our training data can improve the performance of retrieval models on exclusionary retrieval, there still exists a gap compared to human performance; and (iii) generative retrieval models have a natural advantage in handling exclusionary queries.

AAAI 2025

ExcluIR: Exclusionary Neural Information Retrieval

technical paper

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.



In the $k$-committee election problem, we wish to aggregate the preferences of $n$ agents
over a set of alternatives and select a committee of $k$ alternatives that minimizes the
cost incurred by the agents. While we typically assume that agent preferences are captured
by a cardinal utility function, in many contexts we only have access to ordinal
information, namely the agents' rankings over the outcomes. As ordinal preference rankings
are not as expressive as cardinal utilities, a loss of efficiency is inevitable, and is
quantified by the notion of \emph{distortion}.  
		
		
We study the problem of electing a $k$-committee that minimizes the sum of the
$\ell$-largest costs incurred by the agents, when agents and candidates are embedded in a
metric space. This problem is called the $\ell$-centrum problem and captures both the
utilitarian and egalitarian objectives. When $k \geq 2$, it is not possible to compute a
bounded-distortion committee using purely ordinal information. We develop the first
algorithms (that we call mechanisms) for the $\ell$-centrum problem (when $k \geq 2$),
which achieve $O(1)$-distortion while eliciting only a very limited amount of cardinal
information via value queries. We obtain two types of query-complexity guarantees: $O(\log
k \log n)$ queries \emph{per agent},  and $O(k^2 \log^2 n)$ queries \emph{in total} (while
achieving $O(1)$-distortion in both cases). En route, we give a simple adaptive-sampling
algorithm for the $\ell$-centrum $k$-clustering problem.

Constant-Factor Distortion Mechanisms for k-Committee Election

Correct labels are indispensable for training effective machine learning models. However, creating high-quality labels is expensive, and even professionally labeled data contains errors and ambiguities. Filtering and denoising can be applied to curate labeled data prior to training, at the cost of additional processing and loss of information. An alternative is on-the-fly sample reweighting during the training process to decrease the negative impact of incorrect or ambiguous labels, but this typically requires clean seed data. In this work we propose unsupervised on-the-fly meta loss rescaling to reweight training samples. Crucially, we rely only on features provided by the model being trained, to learn a rescaling function in real time without knowledge of the true clean data distribution. We achieve this via a novel meta learning setup that samples validation data for the meta update directly from the noisy training corpus by employing the rescaling function being trained. Our proposed method consistently improves performance across various NLP tasks with minimal computational overhead. Further, we are among the first to attempt on-the-fly training data reweighting on the challenging task of dialogue modelling, where noisy and ambiguous labels are common. Our strategy is robust in the face of noisy and clean data, handles class imbalance, and prevents overfitting to noisy labels. Our self-taught loss rescaling improves as the model trains, showing the ability to keep learning from the model's own signals. As training progresses, the impact of correctly labeled data is scaled up, while the impact of wrongly labeled data is suppressed.

Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling

Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailored to each subject. The personalization limits the broader applicability of brain visual decoding in real-world scenarios. To address this issue, we introduce Wills Aligner, a novel approach designed to achieve multi-subject collaborative brain visual decoding. Wills Aligner begins by aligning the fMRI data from different subjects at the anatomical level. It then employs delicate mixture-of-brain-expert adapters and a meta-learning strategy to account for individual fMRI pattern differences. Additionally, Wills Aligner leverages the semantic relation of visual stimuli to guide the learning of inter-subject commonality, enabling visual decoding for each subject to draw insights from other subjects' data. We rigorously evaluate our Wills Aligner across various visual decoding tasks, including classification, cross-modal retrieval, and image reconstruction. The experimental results demonstrate that Wills Aligner achieves promising performance.

Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods. We provide an anonymous implementation in the supplementary material.

Augmenting Sequential Recommendation with Balanced Relevance and Diversity

Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main challenges: i) How to  extract advanced semantics from available views? ii) How to build a refined classification framework with limited labeled space?
iii) How to provide more high-quality supervisory information? To address these problems, we propose a  Semi-Supervised Multi-View Multi-Label Learning Method with View-Specific Transformer and Enhanced Pseudo-Label named SMVTEP. Specifically, Generative Adversarial Networks are employed to extract informative shared and specific representations and their consistency and distinctiveness are ensured through the adversarial mechanism and information theory based contrastive learning. Then we build specific classifiers for each extracted feature and apply instance-level manifold  constraints to reduce bias across classifiers. Moreover, we design a transformer-style fusion approach that simultaneously captures the imbalance of expressive power among views, mapping effects on specific labels, and label dependencies by incorporating confidence scores and category semantics into the self-attention mechanism. Furthermore, after using Mixup for data augmentation, category-enhanced pseudo-labels are leveraged to improve  the reliability of additional annotations by aligning the label  distribution of unlabeled samples with the true distribution. Finally, extensive experimental results validate the effectiveness of SMVTEP against state-of-the-art methods.

Semi-Supervised Multi-View Multi-Label Learning with View-Specific Transformer and Enhanced Pseudo-Label

Although the complex spectrum-based speech enhancement (SE) methods have achieved significant performance, coupling amplitude and phase can lead to a compensation effect, where amplitude information is sacrificed to compensate for the phase that is harmful to SE. In addition, to further improve the performance of SE, many modules are stacked onto SE, resulting in increased model complexity that limits the application of SE. To address these problems, we proposed a dual-path network based on compressed frequency using Mamba. First, we extract amplitude and phase information through parallel dual branches. This approach leverages structured complex spectra to implicitly capture phase information and solves the compensation effect by decoupling amplitude and phase, and the network incorporates an interaction module to suppress unnecessary parts and recover missing components from the other branch. Second, to reduce network complexity, the network introduces a band-split strategy to compress the frequency dimension. To further reduce complexity while maintaining good performance, we designed a Mamba-based module that models the time and frequency dimensions under linear complexity. Finally, compared to baselines, our model achieves an average 8.3 times reduction in computational complexity while maintaining superior performance. Furthermore, it achieves a 25 times reduction in complexity compared to transformer-based models.

BSDB-Net: Band-Split Dual-Branch Network with Selective State Spaces Mechanism for Monaural Speech Enhancement

Recent concept-based interpretable models have succeeded in providing meaningful explanations by pre-defined concept sets. However, the dependency on the pre-defined concepts restricts the application because of the limited number of concepts for explanations. This paper proposes a novel interpretable deep neural network called explanation bottleneck models (XBMs). XBMs generate a text explanation from the input without pre-defined concepts and then predict a final task prediction based on the generated explanation by leveraging pre-trained vision-language encoder-decoder models. To achieve both the target task performance and the explanation quality, we train XBMs through the target task loss with the regularization penalizing the explanation decoder via the distillation from the frozen pre-trained decoder. Our experiments, including a comparison to state-of-the-art concept bottleneck models, confirm that XBMs provide accurate and fluent natural language explanations without pre-defined concept sets.

Explanation Bottleneck Models

In this paper, I review approaches for acquiring hierarchical knowledge
to improve the effectiveness of planning systems. First I note some
benefits of such hierarchical content and the advantages of learning
over manual construction. After this, I consider alternative paradigms
for encoding and acquiring plan expertise before turning to hierarchical
task networks. I specify the inputs to HTN learners and three subproblems
they must address: identifying hierarchical structure, unifying method
heads, and finding method conditions. Finally, I pose seven challenges
the community should pursue so that techniques for learning HTNs can
reach their full potential.

Learning Hierarchical Task Knowledge for Planning

Incentives for Early Arrival (I4EA) is a novel concept for online cooperative games introduced in an award-winning paper by Ge et al. (2024). The aim of I4EA is to encourage players to join a collaboration as soon as they become aware of it, a new study with significant real-world applications, including data collection and venture capital finance. This paper provides an in-depth discussion of I4EA and highlights its importance across various domains.

Incentives for Early Arrival

Robots are increasingly being used in different application domains due to rapid advancements in hardware and computational methods. However, state of the art methods for many problems in robotics are based on deep networks and similar data-driven models. These methods and models are resource-hungry and opaque, and they are known to provide arbitrary decisions in previously unseen situations, whereas practical robot application domains require transparent, multi-step, multi-level decision-making and ad hoc collaboration under resource constraints and open world uncertainty. In this talk, I argue that for widespread use of robots, we need to revisit principles such as refinement and adaptive satisficing, which can be traced back to the early pioneers of AI. We also need to make these principles the foundation of the architectures we develop for robots, with modern data-driven methods being just another tool in our toolbox. I then illustrate the potential benefits of this approach in the context of fundamental problems in robotics such as visual scene understanding, planning, changing-contact manipulation, and multiagent/human-agent collaboration.

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Constant-Factor Distortion Mechanisms for k-Committee Election

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