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Yikai Chen

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Artificial Intelligence cond-mat.quant-gas Machine Learning physics.optics

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preprint2026arXiv

Beyond Rational Illusion: Behaviorally Realistic Strategic Classification

Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes. Existing SC frameworks typically rely on the idealized assumption that agents are strictly rational. However, evidence from behavioral economics and psychology consistently shows that real-world decision-making is often shaped by cognitive biases, deviating from pure rationality. To formalize this limitation, we identify and define a new problem setting, termed the behaviorally realistic strategic classification problem, where agents' strategic manipulations deviate from full rationality due to psychological biases. Motivated by the identified limitation, we propose the Prospect-Guided Strategic Framework (Pro-SF) to address the problem, a principled framework grounded in prospect theory to model and learn under behaviorally realistic strategic responses. Specifically, to capture behaviorally realistic strategic manipulations, our framework reformulates the Stackelberg-style interaction between agents and the decision-maker by incorporating three key mechanisms inspired by prospect theory, including the asymmetry between benefits and costs, different subjective reference points, and non-rational probability distortion. Experiments on synthetic and real-world datasets establish Pro-SF as a behaviorally grounded approach to strategic classification, bridging machine learning and behavioral economics for more reliable deployment in the real world.

preprint2026arXiv

Detecting Unobserved Confounders: A Kernelized Regression Approach

Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder Detection (KRCD), a novel method for detecting unobserved confounding in nonlinear observational data under single-environment conditions. KRCD leverages reproducing kernel Hilbert spaces to model complex dependencies. By comparing standard and higherorder kernel regressions, we derive a test statistic whose significant deviation from zero indicates unobserved confounding. Theoretically, we prove two key results: First, in infinite samples, regression coefficients coincide if and only if no unobserved confounders exist. Second, finite-sample differences converge to zero-mean Gaussian distributions with tractable variance. Extensive experiments on synthetic benchmarks and the Twins dataset demonstrate that KRCD not only outperforms existing baselines but also achieves superior computational efficiency.

preprint2026arXiv

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach

Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph{non-strategic} settings where data distributions are independent of deployed classifiers. In many real-world decision scenarios, however, individuals may strategically modify their features after deployment to obtain favorable outcomes, inducing a post-deployment distribution shift. This paper studies whether PFN-style tabular foundation models can generalize to such \emph{strategic} tabular data. We show that strategic manipulation creates a mismatch between the non-strategic prior learned during pretraining and the post-manipulation strategic prior, which leads to systematic prediction bias. To address this issue, we propose \textbf{Strategic Prior-data Fitted Network}~\textit{(SPN)}, an inference-time strategy-aware framework that adapts tabular foundation models to strategic environments without retraining. SPN constructs strategic in-context examples to approximate post-manipulation inputs and aligns PFN predictions with the induced strategic distribution. Experiments on real-world and synthetic tabular datasets show that SPN consistently improves robustness and predictive performance under strategic manipulation compared with both tabular foundation models and classical tabular methods.

preprint2019arXiv

Persistent oscillations versus thermalization in the quench dynamics of quantum gases with long-range interactions

Searching for nonthermalized dynamics in interacting quantum systems is not only of fundamental theoretical interest in nonequilibrium quantum physics, but also of immense practical significance in quantum information processing. In this paper, we study quantum quench dynamics in an hard-core bosonic model with infinite-range interactions, which have been realized in recent high-finesse cavity experiments. We show the long-time dynamics of this model can exhibit either undamped oscillations or thermalization depending on the choice of initial states. The long-range nature of the interactions rather than conserved quantities are responsible for such nonergodic dynamical behaviors.

preprint2013arXiv

Plasmonic dark field microscopy with a polymer substrate

In this letter, a plasmonic dark field microscopy taking advantages of the polymer loaded surface plasmon polariton waveguide (PLSPPW) is experimentally demonstrated. The dye molecules (Rhodamine 6G, Rh6G) are doped in the PLSPPW to launch the plasmonic waves or guided waves. Due to the localized property of these waves, the near-field optical energy on the surface of PLSPPW can be scattered into the far-field only in the presence of the objects. The scattering signals then form the dark-field image of the objects. The proposed technique just utilizes a chip-scale integrated plasmonic multilayered structure which is highly compatible with the conventional optical microscopy. The polymer film involved in the PLSPPW also brings about the merits of small roughness, good stability and bio-compatibility

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