United States

In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra&#39;s magnitude and phase in two diffusion networks. Throughout the diffusion process, noise clips from real-world noise interferences are added gradually to the clean speech spectra and a noise-aware reverse process is proposed to learn how to generate both clean speech spectra and noise spectra. Furthermore, to fully leverage the intrinsic relationship between magnitude and phase, we introduce a complex-cycle-consistent (CCC) mechanism that uses the estimated magnitude to map the phase, and vice versa. We implement this algorithm within a phase-aware speech enhancement diffusion model (SEDM). We conduct extensive experiments on public datasets to demonstrate the effectiveness of our method, highlighting the significant benefits of exploiting the intrinsic relationship between phase and magnitude information to enhance speech. The comparison to conventional diffusion models demonstrates the superiority of SEDM. The code of this work is available at https://anonymous.4open.science/r/CCS-diffu-CA3C.

AAAI 2025

Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement

In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phase in two diffusion networks. Throughout the diffusion process, noise clips from real-world noise interferences are added gradually to the clean speech spectra and a noise-aware reverse process is proposed to learn how to generate both clean speech spectra and noise spectra. Furthermore, to fully leverage the intrinsic relationship between magnitude and phase, we introduce a complex-cycle-consistent (CCC) mechanism that uses the estimated magnitude to map the phase, and vice versa. We implement this algorithm within a phase-aware speech enhancement diffusion model (SEDM). We conduct extensive experiments on public datasets to demonstrate the effectiveness of our method, highlighting the significant benefits of exploiting the intrinsic relationship between phase and magnitude information to enhance speech. The comparison to conventional diffusion models demonstrates the superiority of SEDM. The code of this work is available at https://anonymous.4open.science/r/CCS-diffu-CA3C.

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.



Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by the human Cognitive Reflection, which promptly detects errors in our intuitive response and revises them by invoking the System 2 reasoning, we propose to improve NeSy systems by introducing Abductive Reflection (ABL-Refl) based on the Abductive Learning (ABL) framework. ABL-Refl leverages domain knowledge to abduce a reflection vector during training, which can then flag potential errors in the neural network outputs and invoke abduction to rectify them and generate consistent outputs during inference. ABL-Refl is highly efficient in contrast to previous ABL implementations. Experiments show that ABL-Refl outperforms state-of-the-art NeSy methods, achieving excellent accuracy with fewer training resources and enhanced efficiency.

Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection

An important - but very demanding - property in collective decision-making is strategyproofness, which requires that voters cannot benefit from submitting insincere preferences. Gibbard (1977) has shown that only rather unattractive rules are strategyproof, even when allowing for randomization. However, Gibbard's theorem is based on a rather strong interpretation of strategyproofness, which deems a manipulation successful if it increases the voter's expected utility for at least one utility function consistent with his ordinal preferences. In this paper, we study weak strategyproofness, which deems a manipulation successful if it increases the voter's expected utility for all utility functions consistent with his ordinal preferences. We show how to systematically design attractive, weakly strategyproof social decision schemes (SDSs) and explore their limitations for both strict and weak preferences. In particular, for strict preferences, we show that there are weakly strategyproof SDSs that are either ex post efficient or Condorcet-consistent, while neither even-chance SDSs nor pairwise SDSs satisfy both properties and weak strategyproofness at the same time. By contrast, for the case of weak preferences, we discuss two sweeping impossibility results that preclude the existence of appealing weakly strategyproof SDSs.

Weak Strategyproofness in Randomized Social Choice

Infectious diseases have historically had profound effects on global health, economies, and social structures. Effective tracing of infectious diseases is essential not only for immediate public health responses but also for shaping future prevention strategies. Traditional tracing methods often emphasize homogeneous networks, overlooking the diverse transmission characteristics of heterogeneous populations. This research addresses two critical challenges: the heterogeneity of transmission across various media and modes, and the significant yet underexplored influence of community structures on epidemic spread and tracing.We propose a Heterogeneous Hypergraph Attention Network (HHAN) modelthat accounts for multiple transmission pathways and patterns within heterogeneous networks.  HHAN integrates a heterogeneous graph neural network module to handle the complexity of communication among different populations, and an Agent-Based Modeling Module that combines agent-based ideas to model individual behaviors. This approach effectively captures complex interactions within community structures and addresses individual variability. Experimental results on three real-world datasets demonstrate that the HHAN model significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations.

HHAN: Comprehensive Infectious Disease Source Tracing via Heterogeneous Hypergraph Neural Network

Motion planning is a crucial component in autonomous vehicles. State-of-the-art motion planners are trained on meticulously curated datasets, which are not only expensive to annotate but also insufficiently containing rarely-seen critical scenarios. Failing to capture such scenarios inevitably risks motion planners and potentially causes incidents during testing. One intuitive solution is to compose such scenarios by programming and executing a simulator (e.g., CARLA). However, this requires huge human cost. I'm motivated to develop a cheap method to generate diverse critical traffic scenarios for training better motion planners. First, I represent traffic scenarios by scripts, using which the simulator produces traffic scenarios. Then, I develop a method which accepts user-specified text description, which a Large Language Model (LLM) translates into scripts using in-context learning.
The output scripts are sent to the simulator which produces the corresponding traffic scenarios. As my method can generate abundant safety-critical traffic scenarios, I use them as synthetic training data for motion planners. To demonstrate the value of the generated scenarios, I train existing motion planners on my synthetic data, existing real-world datasets, and the mix of my data with the existing datasets.
My experiments show that motion planners trained with my data significantly outperforms the counterparts trained only on real-world data, showing the usefulness of my synthetic data and the effectiveness of my data generation method.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

Weakly-Supervised Dense Video Captioning (WSDVC) aims to localize and describe all events of interest in a video without requiring annotations of event boundaries. This setting poses a great challenge in accurately locating the temporal location of event, as the relevant supervision is unavailable. Existing methods rely on explicit alignment constraints between event locations and captions, which involve complex event proposal procedures during both training and inference. To tackle this problem, we propose a novel implicit location-caption alignment paradigm by complementary masking, which simplifies the complex event proposal and localization process while maintaining effectiveness. Specifically, our model comprises two components: a dual-mode video captioning module and a mask generation module. The dual-mode video captioning module captures global event information and generates descriptive captions,
while the mask generation module generates differentiable positive and negative masks for localizing the events. These masks enable the implicit alignment of event locations and captions by ensuring that captions generated from positively and negatively masked videos are complementary, thereby forming a complete video description. In this way, even under weak supervision, the event location and event caption can be aligned implicitly. Extensive experiments on the public datasets demonstrate that our method outperforms existing weakly-supervised methods and achieves competitive results compared to fully-supervised methods. Code will be released.

Implicit Location-Caption Alignment via Complementary Masking for Weakly-Supervised Dense Video Captioning

Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by fine-tuning LLM, aligning collaborative features with LLM's input space, and concatenating collaborative features with text tokens to form a unified sequence input. Although effective, in this work, we identify two limitations when adapting LLMs to recommendation tasks, which hinder the integration of general knowledge and collaborative information, resulting in sub-optimal recommendation performance. (1) Fine-tuning LLM with recommendation data can undermine its inherent world knowledge and fundamental competencies, which are crucial for interpreting and inferring recommendation text. (2) Incorporating collaborative features into textual prompts disrupts the semantics of the original prompts, preventing LLM from generating appropriate outputs. In this paper, we propose a new paradigm, CoRA (an acronym for Collaborative LoRA), with a collaborative weights generator. Rather than input space alignment, this method aligns collaborative information with LLM's parameter space, representing them as incremental weights to update LLM's output. This way, LLM perceives collaborative information without altering its general knowledge and text inference capabilities. 
For each user-item pair, we employ a collaborative filtering model to extract user and item embeddings, converting them into collaborative weights with low-rank properties through the collaborative weights generator. We then merge the collaborative weights into LLM's weights, enabling LLM to perceive the collaborative signals and generate personalized recommendations without fine-tuning or extra collaborative tokens in prompts. Extensive experiments confirm that CoRA effectively integrates collaborative information into LLM, enhancing recommendation performance.

CoRA: Collaborative Information Perception by Large Language Model’s Weights for Recommendation

The map of elections framework is a methodology for visualizing and analyzing election datasets. So far, the framework was restricted to elections that have equal numbers of candidates, equal numbers of voters, and where all the (ordinal) votes rank all the candidates. We extend it to the case of elections of different sizes, where the votes can be top-truncated. We use our results to present a visualization of a large fragment of the Preflib database.

Distances Between Top-Truncated Elections of Different Sizes

Multi-agent path finding (MAPF) is a safety-critical scenario where the goal is to secure collision-free trajectories from initial to desired locations.
However, due to system complexity and uncertainty, integrating learning-based controllers with MAPF is challenging and cannot theoretically guarantee the safety of the learned controllers. 
In response, our study proposes a verified safe multi-agent neural control (VSMANC) approach for MAPF, focusing on the unified training of Decentralized Control Barrier Functions (DCBF) and controllers to enhence safety. 
VSMANC enables all agents to concurrently learn controllers and DCBFs using a unified loss function designed to maximize safety, adhere to standard control policies, and incorporate path-finding-related heuristics.  
We also propose a formal verification-guided retraining process to both verify the properties of the learned DCBFs and generate counterexamples for retraining, thereby providing a verified safety guarantee.
We validate our approach through shape formation experiments and UAV simulations, demonstrating significant improvements in safety and effectiveness in complex multi-agent environments.

Learning Verified Safe Neural Network Controllers for Multi-Agent Path Finding

This talk focuses on the use of foundation models to study problems in labor economics, in particular problems relating to the progression of worker careers and wages. In Vafa et al. (2024a), we introduced a transformer-based predictive model, CAREER, that predicts a worker’s next job as a function of career history (an “occupation model”). CAREER was initially estimated (“pre-trained”) using a large, unrepresentative resume dataset, which served as a “foundation model,” and parameter estimation was continued (“fine-tuned”) using data from a representative survey. CAREER had better predictive performance than benchmarks. Athey et al (2024) considers an alternative where the resume-based foundation model is replaced by a large language model (LLM). We convert tabular data from the survey into text files that resemble resumes and fine-tune the LLMs using these text files with the objective to predict the next token (word). The resulting fine-tuned LLM is used as an input to an occupation model. Its predictive performance surpasses all prior models. We demonstrate the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs surpasses the performance of fine-tuning larger models. In Vafa et al. (2024b), we apply and adapt this framework to the problem of gender wage decompositions, which require estimating the portion of the gender wage gap explained by career histories of workers. Classical methods for decomposing the wage gap employ simple linear models, and the resulting decompositions thus suffer from omitted variable bias (OVB), where covariates that are correlated with both gender and wages are not included in the model. We explore an alternative methodology for wage gap decomposition that employs CAREER as a foundation model. We prove that the way foundation models are usually trained might still lead to OVB, but develop fine-tuning algorithms that empirically mitigate this issue. We first provide a novel set of conditions under which an estimator of the wage gap based on a fine-tuned foundation model is root-n-consistent. Building on the theory, we then propose methods for fine-tuning foundation models that minimize OVB. Using data from the Panel Study of Income Dynamics, we find that history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of history that are important for reducing OVB.

Predicting Career Transitions and Estimating Wage Disparities Using Foundation Models

In this keynote, Andrew will explore how current technologies, particularly agentic workflow, are revolutionizing the development of AI products. He will also delve into the role of AI in prototyping, showcasing how AI lowers the cost of software development and accelerates the prototyping process.

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Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection

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