arXiv subjects

Yanchen Wu

Publications and source records attributed to Yanchen Wu.

5 recordsLinked to original sources

arXiv2026

A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization

Data-driven inverse optimization seeks to estimate unknown parameters in an optimization model from observations of optimization solutions. Many existing methods are ineffective in handling noisy and suboptimal solution observations and also suffer from computational challenges. In this paper, we build a connection between inverse optimization and the Fenchel-Young (FY) loss originally designed for structured prediction, proposing a FY loss approach to data-driven inverse optimization. This new approach is amenable to efficient gradient-based optimization, hence much more efficient than existing methods. We provide theoretical guarantees for the proposed method and use extensive simulation and real-data experiments to demonstrate its significant advantage in parameter estimation accuracy, decision error and computational speed.

math.OC↗

arXiv2026

Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework

Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self-evolution. A number of memory methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework that covers existing representative agent memory methods from a high-level perspective. We then extensively compare representative agent memory methods on two long-term conversational benchmarks and an agentic memory benchmark, and examine the effectiveness of representative methods, providing a thorough analysis of those methods. As a byproduct of our experimental analysis, we also design a new memory method by exploiting modules in the existing methods, which outperforms the state-of-the-art methods. Finally, based on these findings, we offer promising future research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide valuable new insights for future research.

cs.CL↗

arXiv2025

Contextual Linear Optimization with Partial Feedback

Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients in the objective and thereby improve decision-making performance. A canonical example is the stochastic shortest path problem with random edge costs (e.g., travel time) and contextual features (e.g., lagged traffic, weather). While existing work on CLO assumes fully observed cost coefficient vectors, in many applications the decision maker observes only partial feedback corresponding to each chosen decision in the history. In this paper, we study both a bandit-feedback setting (e.g., only the overall travel time of each historical path is observed) and a semi-bandit-feedback setting (e.g., travel times of the individual segments on each chosen path are additionally observed). We propose a unified class of offline learning algorithms for CLO with different types of feedback, following a powerful induced empirical risk minimization (IERM) framework that integrates estimation and optimization. We provide a novel fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of estimation methods. To solve the partial-feedback IERM, we also tailor computationally tractable surrogate losses. A byproduct of our theory of independent interest is the fast-rate regret bound for IERM with full feedback and a misspecified policy class. We compare the performance of different methods numerically using stochastic shortest path examples on simulated and real data and provide practical insights from the empirical results.

stat.ML↗

arXiv2025

Line Tension Reshapes Nucleation at Surface Edges: A Generalized Theory for Nanopore Activation

Heterogeneous nucleation at surface edges is pervasive across nature and industry, yet the role of line tension, arising from asymmetric capillary interactions at geometric singularities, remains poorly understood. Herein we develop a generalized nucleation theory that explicitly incorporates line tension induced by edge pinning, thereby extending classical frameworks to account for nanoscale confinement and interfacial asymmetry. Through analytical treatment of droplet formation within geometrically defined nanopores, we derive a closed-form expression for the edge-pinned line tension as a function of Laplace pressure, pore geometry, and wettability. This formulation reveals that line tension can significantly reshape the nucleation energy landscape, introducing nontrivial dependencies on contact angle and pore morphology. Our results uncover a tunable, geometry-mediated mechanism for controlling nucleation barriers, offering predictive insight into phase transitions in confined environments and suggesting new strategies for design in applications ranging from nanofluidics to crystallization control.

cond-mat.soft↗

arXiv2025

Mechanistic Modeling of Lipid Nanoparticle Formation for the Delivery of Nucleic Acid Therapeutics

Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and process insights. Lastly, the use of the models to guide development of advanced process control and optimization strategies is discussed.

cond-mat.soft↗