arXiv subjects

Bei Jiang

Publications and source records attributed to Bei Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Direct observation of quadruple spin-texture locking in a 2D d-wave altermagnet

Altermagnets combine vanishing net magnetization with nonrelativistic, momentum-dependent spin splitting, offering a new paradigm for spintronics. Spin-crystal symmetry coupling, namely spin-lattice locking, is the defining mechanism of altermagnetism, enforcing opposite spin sublattices in real space and spin-momentum-locked electronic structure in reciprocal space. Direct atomic-scale visualization of spin-lattice locking therefore constitutes a decisive benchmark of the altermagnetic state, yet such evidence has remained elusive despite extensive efforts. Here we show that the electronic states in RbV2Se2O exhibit a d-wave-like spin texture at the sublattice level, providing the first atomic-scale evidence of spin-lattice locking with a predominantly c-axis spin orientation. By employing an in-situ, field-switchable spin-polarized Cr tip, we realize spin-contrast mapping of quasiparticle interference at identical energies, overcoming a long-standing experimental barrier in altermagnets. The resulting interference patterns exhibit pronounced spin-dependent modulations, establishing spin scattering locking and spin momentum locking as the real and reciprocal space manifestations. Unexpectedly, we uncover that the spin-selective scattering response is organized by a long-period stripe modulation, giving rise to a previously unidentified form of spin-texture locking, spin-stripe locking. We attribute this behavior to the emergence of a spin-density-wave moir\'e pattern. Together, these results establish a unified picture of quadruple spin-texture locking phenomena in a d-wave altermagnet, and position altermagnets as a versatile platform for exploring many-body interactions among intertwined degrees of freedom, including spin, lattice, momentum, moir\'e potential and valley.

cond-mat.mtrl-sci↗

arXiv2026

Differentially Private Conformal Prediction

Conformal prediction (CP) has attracted broad attention as a simple and flexible framework for uncertainty quantification through prediction sets. In this work, we study how to deploy CP under differential privacy (DP) in a statistically efficient manner. We first introduce differential CP, a non-splitting conformal procedure that avoids the efficiency loss caused by data splitting and serves as a bridge between oracle CP and private conformal inference. By exploiting the stability properties of DP mechanisms, differential CP establishes a direct connection to oracle CP and inherits corresponding validity behavior. Building on this idea, we develop Differentially Private Conformal Prediction (DPCP), a fully private procedure that combines DP model training with a private quantile mechanism for calibration. We establish the end-to-end privacy guarantee of DPCP and investigate its coverage properties under additional regularity conditions. We further study the efficiency of both differential CP and DPCP under empirical risk minimization and general regression models, showing that DPCP can produce tighter prediction sets than existing private split conformal approaches under the same privacy budget. Numerical experiments on synthetic and real datasets demonstrate the practical effectiveness of the proposed methods.

stat.ML↗

arXiv2026

Observation of Resonance of Kagome Flat Band Doublet

The interplay between local and itinerant electrons underpins many correlated and topological quantum states. Kagome lattices provide an ideal platform by hosting both flat (localized states) and dispersive bands (itinerant states), yet direct spectroscopic evidence of their dynamical coupling has remained elusive. Here we report the long-sought flat band resonance in the quasi-two-dimensional kagome bilayer material CsCr6Sb6. Using angle-resolved photoemission spectroscopy, transport measurements, and combined density functional theory and dynamical mean-field theory, we identify coexisting flat band doublets and dispersive bands near the Fermi energy. Upon cooling, the flat and dispersive bands exhibit a pronounced enhancement of spectral weight and hybridization, directly evidencing flat band resonance. Crucially, this emergence coincides with the onset of short-range antiferromagnetic correlations, contrasting sharply with conventional Kondo lattice behavior. Our findings demonstrate not only the long-sought flat band resonance in kagome materials, but also its unconventional correlation with magnetism.

cond-mat.str-el↗

arXiv2026

Observation of spin-valley locked nodal lines in a quasi-2D altermagnet

The interplay among quantum degrees of freedom-spin, orbital and momentum-has emerged as a fertile ground for realizing magnetic quantum states with transformative potential for electronic and spintronic technologies. Prominent examples include ferromagnetic Weyl semimetals and antiferromagnetic axion insulators. Recently, altermagnets(AMs) have been identified as a distinct spin-splitting class of collinear antiferromagnets(AFMs), characterized by crystal symmetry that connects magnetic sublattices in real space and enforces C-paired spin-momentum locking in reciprocal space. These materials combine the advantages of nonrelativistic spin-polarization akin to FMs and vanished net-magnetization as AFMs, making them highly promising for spintronic applications. Furthermore, they introduce nontrivial spin-momentum locking spin texture as an additional degree of freedom for realizing novel quantum phases. In this work, we report the discovery of a new type of spin-valley-locked nodal line phase in the layered AM Rb-intercalated V{_2}Te{_2}O. By combining high-resolution spin and angle-resolved photoemission spectroscopy with first-principles calculations, we observe the coexistence of both spinless and spinful nodal lines near the Fermi level. Remarkably, the spinful nodal lines exhibit uniform spin polarization within each valley, while displaying opposite spin polarizations across symmetry-paired valleys-a unique feature we term spin-valley-locked nodal lines, which is exclusive to AMs. Direct measurements of out-of-plane band dispersion using a side-cleaving technique reveal the two-dimensional nature of these nodal lines. Our findings not only unveil a previously unexplored topological phase in AMs where valley-locked spin as an additional quantum character but also establish RbV{_2}Te{_2}O as a promising platform for spintronics, valleytronics, and moire-engineered quantum devices.

cond-mat.mtrl-sci↗

arXiv2025

Stoichiometry-Controlled Structural Order and Tunable Antiferromagnetism in $\mathrm{Fe}_{x}\mathrm{NbSe_2}$ ($0.05 \le x \le 0.38$)

Transition metal dichalcogenides (TMDs) enable magnetic property engineering via intercalation, but stoichiometry-structure-magnetism correlations remain poorly defined for Fe-intercalated $\mathrm{NbSe_2}$. Here, we report a systematic study of $\mathrm{Fe}_{x}\mathrm{NbSe_2}$ across an extended composition range $0.05 \le x \le 0.38$, synthesized via chemical vapor transport and verified by rigorous energy-dispersive x-ray spectroscopy (EDS) microanalysis. X-ray diffraction, magnetic, and transport measurements reveal an intrinsic correlation between Fe content, structural ordering, and magnetic ground states. With increasing $x$, the system undergoes a successive transition from paramagnetism to a spin-glass state, then to long-range antiferromagnetism (AFM), and ultimately to a reentrant spin-glass phase, with the transition temperatures exhibiting a nonmonotonic dependence on Fe content. The maximum N\'eel temperature ($T_{\mathrm{N}}$ = $\mathrm{175K}$) and strongest AFM coupling occur at $x=0.25$, where Fe atoms form a well-ordered $2a_0 \times 2a_0 $ superlattice within van der Waals gaps. Beyond $x = 0.25$, the superlattice transforms or disorders, weakening Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions and significantly reducing $T_{\mathrm{N}}$. Electrical transport exhibits distinct anomalies at magnetic transition temperatures, corroborating the magnetic state evolution. Our work extends the compositional boundary of Fe-intercalated $\mathrm{NbSe_2}$, establishes precise stoichiometry-structure-magnetism correlations, and identifies structural ordering as a key tuning parameter for AFM. These findings provide a quantitative framework for engineering altermagnetic or switchable antiferromagnetic states in van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2025

CBMA: Improving conformal prediction through Bayesian model averaging

Conformal prediction has emerged as a popular technique for facilitating valid predictive inference across a spectrum of machine learning models, under minimal assumption of exchangeability. Recently, Hoff (2023) showed that full conformal Bayes provides the most efficient prediction sets (smallest by expected volume) among all prediction sets that are valid at the $(1 - \alpha)$ level if the model is correctly specified. However, a critical issue arises when the Bayesian model itself may be mis-specified, resulting in prediction set that might be suboptimal, even though it still enjoys the frequentist coverage guarantee. To address this limitation, we propose an innovative solution that combines Bayesian model averaging (BMA) with conformal prediction. This hybrid not only leverages the strengths of Bayesian conformal prediction but also introduces a layer of robustness through model averaging. Theoretically, we prove that the resulting prediction set will converge to the optimal level of efficiency, if the true model is included among the candidate models. This assurance of optimality, even under potential model uncertainty, provides a significant improvement over existing methods, ensuring more reliable and precise uncertainty quantification.

math.ST↗

arXiv2025

Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we propose a principled framework that adjusts data representations to balance predictive utility and fairness. Using sufficient dimension reduction, we decompose the feature space into target-relevant, sensitive, and shared components, and control the fairness-utility trade-off by selectively removing sensitive information. We provide a theoretical analysis of how prediction error and fairness gaps evolve as shared subspaces are added, and employ influence functions to quantify their effects on the asymptotic behavior of parameter estimates. Experiments on both synthetic and real-world datasets validate our theoretical insights and show that the proposed method effectively improves fairness while preserving predictive performance.

stat.ML↗

arXiv2025

Conformal Inference For Missing Data under Multiple Robust Learning

We develop a novel approach to tackle the common but challenging problem of conformal inference for missing data in machine learning, focusing on Missing at Random (MAR) data. We propose a new procedure Conformal prediction for Missing data under Multiple Robust Learning (CM--MRL) that combines split conformal calibration with a multiple robust empirical-likelihood (EL) reweighting scheme. The method proceeds via a double calibration by reweighting the complete-case scores by EL so that their distribution matches the full calibration distribution implied by MAR, even when some working models are misspecified. We demonstrate the asymptotic behavior of our estimators through empirical process theory and provide reliable coverage for our prediction intervals, both marginally and conditionally and we further show an interval-length dominance result. We show the effectiveness of the proposed method by several numerical experiments in the presence of missing data.

stat.ME↗

arXiv2025

Budget-constrained Active Learning to Effectively De-censor Survival Data

Standard supervised learners attempt to learn a model from a labeled dataset. Given a small set of labeled instances, and a pool of unlabeled instances, a budgeted learner can use its given budget to pay to acquire the labels of some unlabeled instances, which it can then use to produce a model. Here, we explore budgeted learning in the context of survival datasets, which include (right) censored instances, where we know only a lower bound on an instance's time-to-event. Here, that learner can pay to (partially) label a censored instance -- e.g., to acquire the actual time for an instance [perhaps go from (3 yr, censored) to (7.2 yr, uncensored)], or other variants [e.g., learn about one more year, so go from (3 yr, censored) to either (4 yr, censored) or perhaps (3.2 yr, uncensored)]. This serves as a model of real world data collection, where follow-up with censored patients does not always lead to uncensoring, and how much information is given to the learner model during data collection is a function of the budget and the nature of the data itself. We provide both experimental and theoretical results for how to apply state-of-the-art budgeted learning algorithms to survival data and the respective limitations that exist in doing so. Our approach provides bounds and time complexity asymptotically equivalent to the standard active learning method BatchBALD. Moreover, empirical analysis on several survival tasks show that our model performs better than other potential approaches on several benchmarks.

cs.LG↗

arXiv2025

Toward Optimal Statistical Inference in Noisy Linear Quadratic Reinforcement Learning over a Finite Horizon

Recent developments in Reinforcement learning have significantly enhanced sequential decision-making in uncertain environments. Despite their strong performance guarantees, most existing work has focused primarily on improving the operational accuracy of learned control policies and the convergence rates of learning algorithms, with comparatively little attention to uncertainty quantification and statistical inference. Yet, these aspects are essential for assessing the reliability and variability of control policies, especially in high-stakes applications. In this paper, we study statistical inference for the policy gradient (PG) method for noisy Linear Quadratic Reinforcement learning (LQ RL) over a finite time horizon, where linear dynamics with both known and unknown drift parameters are controlled subject to a quadratic cost. We establish the theoretical foundations for statistical inference in LQ RL, deriving exact asymptotics for both the PG estimators and the corresponding objective loss. Furthermore, we introduce a principled inference framework that leverages online bootstrapping to construct confidence intervals for both the learned optimal policy and the corresponding objective losses. The method updates the PG estimates along with a set of randomly perturbed PG estimates as new observations arrive. We prove that the proposed bootstrapping procedure is distributionally consistent and that the resulting confidence intervals achieve both asymptotic and non-asymptotic validity. Notably, our results imply that the quantiles of the exact distribution can be approximated at a rate of $n^{-1/4}$, where $n$ is the number of samples used during the procedure. The proposed procedure is easy to implement and applicable to both offline and fully online settings. Numerical experiments illustrate the effectiveness of our approach across a range of noisy linear dynamical systems.

math.ST↗

arXiv2025

Non-Asymptotic Analysis of Online Local Private Learning with SGD

Differentially Private Stochastic Gradient Descent (DP-SGD) has been widely used for solving optimization problems with privacy guarantees in machine learning and statistics. Despite this, a systematic non-asymptotic convergence analysis for DP-SGD, particularly in the context of online problems and local differential privacy (LDP) models, remains largely elusive. Existing non-asymptotic analyses have focused on non-private optimization methods, and hence are not applicable to privacy-preserving optimization problems. This work initiates the analysis to bridge this gap and opens the door to non-asymptotic convergence analysis of private optimization problems. A general framework is investigated for the online LDP model in stochastic optimization problems. We assume that sensitive information from individuals is collected sequentially and aim to estimate, in real-time, a static parameter that pertains to the population of interest. Most importantly, we conduct a comprehensive non-asymptotic convergence analysis of the proposed estimators in finite-sample situations, which gives their users practical guidelines regarding the effect of various hyperparameters, such as step size, parameter dimensions, and privacy budgets, on convergence rates. Our proposed estimators are validated in the theoretical and practical realms by rigorous mathematical derivations and carefully constructed numerical experiments.

stat.ME↗

arXiv2025

Online differentially private inference in stochastic gradient descent

We propose a general privacy-preserving optimization-based framework for real-time environments without requiring trusted data curators. In particular, we introduce a noisy stochastic gradient descent algorithm for online statistical inference with streaming data under local differential privacy constraints. Unlike existing methods that either disregard privacy protection or require full access to the entire dataset, our proposed algorithm provides rigorous local privacy guarantees for individual-level data. It operates as a one-pass algorithm without re-accessing the historical data, thereby significantly reducing both time and space complexity. We also introduce online private statistical inference by conducting two construction procedures of valid private confidence intervals. We formally establish the convergence rates for the proposed estimators and present a functional central limit theorem to show the averaged solution path of these estimators weakly converges to a rescaled Brownian motion, providing a theoretical foundation for our online inference tool. Numerical simulation experiments demonstrate the finite-sample performance of our proposed procedure, underscoring its efficacy and reliability. Furthermore, we illustrate our method with an analysis of two datasets: the ride-sharing data and the US insurance data, showcasing its practical utility.

stat.ME↗

arXiv2025

Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to construct fair and informative representations. By introducing a novel penalty term during fine-tuning, our method enforces conditional independence between sensitive attributes and learned representations, addressing bias at its source while preserving predictive performance. Unlike prior methods, it supports diverse sensitive attributes, including continuous, discrete, binary, or multi-group types. Experiments on various types of data structure show that our approach achieves a superior balance between fairness and utility, significantly outperforming state-of-the-art baselines.

stat.ML↗

arXiv2025

A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation

Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI estimator; however, methods based on the empirical distribution function (EDF) can introduce sharp fluctuations in the MI loss due to poor out-of-sample performance, destabilizing convergence. We present a Bayesian nonparametric (BNP) solution for training an MI estimator by constructing the MI loss with a finite representation of the Dirichlet process posterior to incorporate regularization in the training process. With this regularization, the MI loss integrates both prior knowledge and empirical data to reduce the loss sensitivity to fluctuations and outliers in the sample data, especially in small sample settings like mini-batches. This approach addresses the challenge of balancing accuracy and low variance by effectively reducing variance, leading to stabilized and robust MI loss gradients during training and enhancing the convergence of the MI approximation while offering stronger theoretical guarantees for convergence. We explore the application of our estimator in maximizing MI between the data space and the latent space of a variational autoencoder. Experimental results demonstrate significant improvements in convergence over EDF-based methods, with applications across synthetic and real datasets, notably in 3D CT image generation, yielding enhanced structure discovery and reduced overfitting in data synthesis. While this paper focuses on generative models in application, the proposed estimator is not restricted to this setting and can be applied more broadly in various BNP learning procedures.

stat.ML↗

arXiv2024

Fast Online $L_0$ Elastic Net Subspace Clustering via A Novel Dictionary Update Strategy

With the rapid growth of data volume and the increasing demand for real-time analysis, online subspace clustering has emerged as an effective tool for processing dynamic data streams. However, existing online subspace clustering methods often struggle to capture the complex and evolving distribution of such data due to their reliance on rigid dictionary learning mechanisms. In this paper, we propose a novel $\ell_0$ elastic net subspace clustering model by integrating the $\ell_0$ norm and the Frobenius norm, which owns the desirable block diagonal property. To address the challenges posed by the evolving data distributions in online data, we design a fast online alternating direction method of multipliers with an innovative dictionary update strategy based on support points, which are a set of data points to capture the underlying distribution of the data. By selectively updating dictionary atoms according to the support points, the proposed method can dynamically adapt to the evolving data characteristics, thereby enhancing both adaptability and computational efficiency. Moreover, we rigorously prove the convergence of the algorithm. Finally, extensive numerical experiments demonstrate that the proposed method improves clustering performance and computational efficiency, making it well-suited for real-time and large-scale data processing tasks.

math.OC↗

arXiv2024

Discovery of a metallic room-temperature d-wave altermagnet KV2Se2O

Beyond conventional ferromagnetism and antiferromagnetism, altermagnetism is a recently discovered unconventional magnetic phase characterized by time-reversal symmetry breaking and spin-split band structures in materials with zero net magnetization. This distinct magnetic phase not only enriches the understanding of fundamental physical concepts but also has profound impacts on condense-matter physics research and practical device applications. Spin-polarized band structures have been recently observed in semiconductors MnTe and MnTe2 with vanishing net magnetization, confirming the existence of this unconventional magnetic order. Metallic altermagnets have unique advantages for exploring novel physical phenomena related to low-energy quasiparticle excitations and for applications in spintronics as electrical conductivity in metals allows the direct manipulation of spin current through electric field. Here, through comprehensive characterization and analysis of the magnetic and electronic structures of KV2Se2O, we have unambiguously demonstrated a metallic room-temperature altermaget with d-wave spin-momentum locking. The highly anisotropic spin-polarized Fermi surfaces and the spin-density-wave order emerging in the altermagnetic phase make it an extraordinary platform for designing high-performance spintronic devices and studying many-body effects coupled with the unconventional magnetism.

cond-mat.mtrl-sci↗

arXiv2024

Oblivious subspace embeddings for compressed Tucker decompositions

Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension reduction) for the canonical polyadic (CP) tensor decomposition has left analogous results for the more expressive Tucker decomposition comparatively lacking. This work establishes general Johnson-Lindenstrauss (JL) type guarantees for the estimation of Tucker decompositions when an oblivious random embedding is applied along each mode. When these embeddings are drawn from a JL-optimal family, the decomposition can be estimated within $\varepsilon$ relative error under restrictions on the embedding dimension that are in line with recent CP results. We implement a higher-order orthogonal iteration (HOOI) decomposition algorithm with random embeddings to demonstrate the practical benefits of this approach and its potential to improve the accessibility of otherwise prohibitive tensor analyses. On moderately large face image and fMRI neuroimaging datasets, empirical results show that substantial dimension reduction is possible with minimal increase in reconstruction error relative to traditional HOOI ($\leq$5% larger error, 50%-60% lower computation time for large models with 50% dimension reduction along each mode). Especially for large tensors, our method outperforms traditional higher-order singular value decomposition (HOSVD) and recently proposed TensorSketch methods.

stat.ML↗

arXiv2024

Nonvolatile optical control of interlayer stacking order in 1T-TaS2

Nonvolatile optical manipulation of material properties on demand is a highly sought-after feature in the advancement of future optoelectronic applications. While the discovery of such metastable transition in various materials holds good promise for achieving this goal, their practical implementation is still in the nascent stage. Here, we unravel the nature of the ultrafast laser-induced hidden state in 1T-TaS2 by systematically characterizing the electronic structure evolution throughout the reversible transition cycle. We identify it as a mixed-stacking state involving two similarly low-energy interlayer orders, which is manifested as the charge density wave phase disruption. Furthermore, our comparative experiments utilizing the single-pulse writing, pulse-train erasing and pulse-pair control explicitly reveal the distinct mechanism of the bidirectional transformations -- the ultrafast formation of the hidden state is initiated by a coherent phonon which triggers a competition of interlayer stacking orders, while its recovery to the initial state is governed by the progressive domain coarsening. Our work highlights the deterministic role of the competing interlayer orders in the nonvolatile phase transition in the layered material 1T-TaS2, and promises the coherent control of the phase transition and switching speed. More importantly, these results establish all-optical engineering of stacking orders in low-dimensional materials as a viable strategy for achieving desirable nonvolatile electronic devices.

cond-mat.mtrl-sci↗