arXiv subjects

Yaowei Hu

Publications and source records attributed to Yaowei Hu.

6 recordsLinked to original sources

arXiv2025

Ultrafast probing of isotope-induced explciit symmetry breaking in ethylene

Symmetry governs nature's law, yet many of the natural phenomena occur due to the breakdown of symmetry. Here, we show how isotope-induced inversion symmetry breaking influences ultrafast photoisomerization processes in ethylene. Using extreme ultraviolet pump-near infrared probe time-of-flight mass spectrometry, we find that replacing one of the carbon atoms in ethylene with a 13C isotope leads to twice-faster structural relaxation via ethylene-ethylidene isomerization in the photo-excited molecular cation. Advanced trajectory surface hopping calculations incorporating the nuclear symmetry of the molecular systems, reveal that it arises from the mixing of different normal modes in the isotope-substituted species, interactions otherwise forbidden by symmetry. Although the mixing does not alter the symmetry of the electronic Hamiltonian, it modifies that of the nuclear Hamiltonian, causing explicit symmetry breaking. This facilitates efficient intra-molecular vibrational energy redistribution, lowering the isomerization yield. Our findings offer opportunities to use isotope-induced nuclear symmetry breaking to control the outcome of light-molecule interactions across ultrafast timescales.

physics.chem-ph↗

arXiv2025

AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System

In recent years, recommendation systems have evolved from providing a single list of recommendations to offering a comprehensive suite of topic focused services. To better accomplish this task, conversational recommendation systems (CRS) have progressed from basic retrieval augmented LLM generation to agentic systems with advanced reasoning and self correction capabilities. However, agentic systems come with notable response latency, a longstanding challenge for conversational recommendation systems. To balance the trade off between handling complex queries and minimizing latency, we propose AdaptJobRec, the first conversational job recommendation system that leverages autonomous agent to integrate personalized recommendation algorithm tools. The system employs a user query complexity identification mechanism to minimize response latency. For straightforward queries, the agent directly selects the appropriate tool for rapid responses. For complex queries, the agent uses the memory processing module to filter chat history for relevant content, then passes the results to the intelligent task decomposition planner, and finally executes the tasks using personalized recommendation tools. Evaluation on Walmart's real world career recommendation scenarios demonstrates that AdaptJobRec reduces average response latency by up to 53.3% compared to competitive baselines, while significantly improving recommendation accuracy.

cs.IR↗

arXiv2025

Causally Fair Node Classification on Non-IID Graph Data

Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recent research has extended fairness to graph data, such as social networks, but many studies neglect the causal relationships among data instances. This paper addresses a prevalent challenge in many fair machine learning research, which typically assumes independent and identically distributed (IID) data, from the causal perspective. Specifically, this work targets the circumstance where nodes with different neighborhood structures follow different causal mechanisms, violating the invariance assumptions required for classical structural causal models and do-calculus. We base our research on the Network Structural Causal Model (NSCM) framework and develop a Message Passing Variational Autoencoder for Causal Inference (MPVA) to compute interventional distributions for causally fair node classification. We establish theoretical soundness under two conditions: Decomposability and Graph Independence. These conditions formalize when causal mechanism heterogeneity can be overcome by constructing a structural representation that restores invariance and facilitates the computation of interventional distributions using do-calculus in non-IID settings. Empirical evaluations on semi-synthetic and real-world datasets demonstrate that MPVA outperforms conventional methods by effectively approximating interventional distributions and mitigating bias. Our findings demonstrate the potential of causality-based fairness in complex ML applications and motivate future work on relaxing the classic assumptions in algorithmic fairness.

cs.LG↗

arXiv2024

Long-Term Fair Decision Making through Deep Generative Models

This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we leverage the temporal causal graph and use the 1-Wasserstein distance between the interventional distributions of different demographic groups at a sufficiently large time step as the quantitative metric. Then, we propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model. We formulate the optimization problem as a performative risk minimization and adopt the repeated gradient descent algorithm for learning. The empirical evaluation shows the efficacy of the proposed method using both synthetic and semi-synthetic datasets.

cs.LG↗

arXiv2024

Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning

While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static population. In this paper, we study fairness in dynamic systems where sequential decisions are made. Each decision may shift the underlying distribution of features or user behavior. We model the dynamic system through a Markov Decision Process (MDP). By acknowledging that traditional fairness notions and long-term fairness are distinct requirements that may not necessarily align with one another, we propose an algorithmic framework to integrate various fairness considerations with reinforcement learning using both pre-processing and in-processing approaches. Three case studies show that our method can strike a balance between traditional fairness notions, long-term fairness, and utility.

cs.LG↗

arXiv2022

Achieving Long-Term Fairness in Sequential Decision Making

In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets.

cs.LG↗