Publications (219)
Local Electrical Stress-Induced Doping and Formation of 2D Monolayer Graphene P-N Junction
Tianhua Yu, Chen-Wei Liang, Changdong Kim +1
We demonstrated doping in 2D monolayer graphene via local electrical stressing. The doping, confirmed by the resistance-voltage transfer characteristics of the graphene system, is…
MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models
Xinming Wang, Jian Xu, Bin Yu +9
Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation i…
CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents
Zhen Xiang, Aliyah R. Hsu, Austin V. Zane +6
Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decisi…
3D-Mix for VLA: A Plug-and-Play Module for Integrating VGGT-based 3D Information into Vision-Language-Action Models
Bin Yu, Shijie Lian, Xiaopeng Lin +8
Vision-Language-Action (VLA) models leverage Multimodal Large Language Models (MLLMs) for robotic control, but recent studies reveal that MLLMs exhibit limited spatial intelligence…
The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)
Brenda Y. Miao, Irene Y. Chen, Christopher YK Williams +15
Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language an…
Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric
Hao Chen, Cong Tian, Zixuan He +3
With the significant success achieved by large language models (LLMs) like LLaMA, edge computing-based LLM inference services for mobile and PC are in high demand for data privacy.…
On topological representation theory from quivers
Fang Li, Zhihao Wang, Jie Wu +1
In this work, we introduce {\em topological representations of a quiver} as a system consisting of topological spaces and its relationships determined by the quiver. Such a setting…
Nonsingular structural stable chaotic 3-flows of attractor-repeller type
Zhentao Lai, V. Medvedev, Bin Yu +1
We show that any orientable closed 3-manifold admits structurally stable non-singular flow whose non-wandering set consists of a 2-dimensional expanding attract…
Complexity Analysis of the Lasso Regularization Path
Julien Mairal, Bin Yu
The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popul…
Towards Practical Requirement Analysis and Verification: A Case Study on Software IP Components in Aerospace Embedded Systems
Zhi Ma, Cheng Wen, Jie Su +4
IP-based software design is a crucial research field that aims to improve efficiency and reliability by reusing complex software components known as intellectual property (IP) comp…
Fast MCMC sampling algorithms on polytopes
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright +1
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for generating samples from the uniform distribution over a polytope. Both random walks…
Anosov flows on Dehn surgeries on the figure-eight knot
Bin Yu
The purpose of this paper is to classify Anosov flows on the 3-manifolds obtained by Dehn surgeries on the figure-eight knot. This set of 3-manifolds is denoted by M(r) (r is a rat…
STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation
Yuxuan Tian, Yurun Jin, Bin Yu +5
Robotic manipulation requires reasoning about future spatial-temporal interactions and geometric constraints, yet existing Vision-Language-Action (VLA) policies often leave predict…
PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models
Soufiane Hayou, Nikhil Ghosh, Bin Yu
Low-Rank Adaptation (LoRA) is a widely used finetuning method for large models. Its small memory footprint allows practitioners to adapt large models to specific tasks at a fractio…
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data
Yueqing Wang, Xin Jiang, Bin Yu +1
Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current…
Comment: Monitoring Networked Applications With Incremental Quantile Estimation
Bin Yu
Comment: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]
Learning Using Privileged Information for Zero-Shot Action Recognition
Zhiyi Gao, Yonghong Hou, Wanqing Li +2
Zero-Shot Action Recognition (ZSAR) aims to recognize video actions that have never been seen during training. Most existing methods assume a shared semantic space between seen and…
Multicolor Graphene Nanoribbon/Semiconductor Nanowire Heterojunction Light-Emitting Diodes
Yu Ye, Lin Gan, Lun Dai +7
We report novel graphene nanoribbon (GNR)/semiconductor nanowire (SNW) heterojunction light-emitting diodes (LEDs) for the first time. The GNR and SNW have a face-to-face contact s…
Proton-mediated reversible switching of metastable ferroelectric phases with low operation voltages
Xin He, Yinchang Ma, Chenhui Zhang +8
The exploration of ferroelectric phase transitions enables an in-depth understanding of ferroelectric switching and promising applications in information storage. However, controll…
Affine Hirsch foliations on 3-manifolds
Bin Yu
This paper is devoted to discussing affine Hirsch foliations on -manifolds. First, we prove that up to isotopic leaf-conjugacy, every closed orientable -manifold admits $…
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha +1
Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that of the target data used to eva…
LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries
Shijie Lian, Bin Yu, Xiaopeng Lin +6
Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-task scenarios. We identify a cri…
Stability and Convergence Trade-off of Iterative Optimization Algorithms
Yuansi Chen, Chi Jin, Bin Yu
The overall performance or expected excess risk of an iterative machine learning algorithm can be decomposed into training error and generalization error. While the former is contr…
Three dimensional dust mapping of 12 supernovae remnants in the Galactic anticentre
Bin Yu, B. Q. Chen, B. W. Jiang +1
We present three dimensional (3D) dust mapping of 12 supernova remnants (SNRs) in the Galactic anti-center (Galactic longitude between 150\degr\ and 210\degr) based on a recent…
PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence
Xiaopeng Lin, Shijie Lian, Bin Yu +10
Robotic generalization relies on physical intelligence: the ability to reason about state changes, contact-rich interactions, and long-horizon planning under egocentric perception…
A path following algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE)
Guilherme V. Rocha, Peng Zhao, Bin Yu
Given n observations of a p-dimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g.…
Hard labels sampled from sparse targets mislead rotation invariant algorithms
Avrajit Ghosh, Bin Yu, Manfred Warmuth +1
One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final prediction is obtained by applying a logi…
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
Raaz Dwivedi, Nhat Ho, Koulik Khamaru +3
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on i.i.d. samples are known to have lower accuracy than t…
Electronic Transport in Monolayer Graphene with Extreme Physical Deformation: ab Initio Density Functional Calculation
Haiyuan Gao, Yang Xu, Meijiao Li +4
Electronic transport properties of monolayer graphene with extreme physical bending up to 90o angle are studied using ab Initio first-principle calculations. The importance of key…
SOFFLFM: Super-resolution optical fluctuation Fourier light-field microscopy
Haixin Huang, Haoyuan Qiu, Hanzhe Wu +5
Fourier light-field microscopy (FLFM) uses a micro-lens array (MLA) to segment the Fourier Plane of the microscopic objective lens to generate multiple two-dimensional perspective…
Error Rate Bounds in Crowdsourcing Models
Hongwei Li, Bin Yu, Dengyong Zhou
Crowdsourcing is an effective tool for human-powered computation on many tasks challenging for computers. In this paper, we provide finite-sample exponential bounds on the error ra…
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
Siyao Zhang, Daocheng Fu, Zhao Zhang +2
With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance…
Fast Kernelized Correlation Filters without Boundary Effect
Ming Tang, Linyu Zheng, Bin Yu +1
In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accur…
Scaling behavior of density gradient accelerated mixing rate in shock bubble interaction
Bin Yu, Haoyang Liu, Hong Liu
Variable-density mixing in shock bubble interaction, a canonical flow of Richtermyer-Meshkov instability, is studied by the high-resolution simulation. While the dissipation mainly…
Minimum-Norm Interpolation Under Covariate Shift
Neil Mallinar, Austin Zane, Spencer Frei +1
Transfer learning is a critical part of real-world machine learning deployments and has been extensively studied in experimental works with overparameterized neural networks. Howev…
Classifying expanding attractors on figure eight knot complement space and non-transitive Anosov flows on Franks-Williams manifold
Jiagang Yang, Bin Yu
The path closure of figure eight knot complement space, , supports a natural DA (derived from Anosov) expanding attractor. Using this attractor, Franks-Williams constructed th…
Statistics at a Crossroads; Who is for the Challenge?
Xuming He, David Madigan, Bin Yu +1
This project was sponsored by the National Science Foundation and organized by a steering committee and a group of theme leaders. The six-member steering committee, consisting of J…
Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings
Rong Ma, Xi Li, Jingyuan Hu +1
Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet…
Structural Compression of Convolutional Neural Networks
Reza Abbasi-Asl, Bin Yu
Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CN…
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch +1
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too of…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
The Impact of Initialization on LoRA Finetuning Dynamics
Soufiane Hayou, Nikhil Ghosh, Bin Yu
In this paper, we study the role of initialization in Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021). Essentially, to start from the pretrained model as in…
A spectral-like decomposition for transitive Anosov flows in dimension three
François Béguin, Christian Bonatti, Bin Yu
Given a (transitive or non-transitive) Anosov vector field on a closed three-dimensional manifold , one may try to decompose by cutting along two-tori transverse…
Chemical Vapor Deposition-Assembled Graphene Field-Effect Transistor on Hexagonal Boron Nitride
Edwin Kim, Tianhua Yu, Eui Sang Song +1
We investigate key electrical properties of monolayer graphene assembled by chemical-vapor-deposition (CVD) as impacted by supporting substrate material. Graphene field-effect tran…
Does a Global Perspective Help Prune Sparse MoEs Elegantly?
Zeliang Zhang, Nikhil Ghosh, Jiani Liu +2
Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoE…
LPM: Industrial-Scale Generative Video Restoration
Bichuan Zhu, Fulin Li, Jiachao Gong +14
The paper introduces LPM, a diffusion‑based generative model for restoring user‑generated videos at industrial scale, achieving high visual quality, temporal consistency, and up to…
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
Andy Zhou, Xiaojun Xu, Ramesh Raghunathan +4
Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard app…
A Novel Scattered Pilot Design for FBMC/OQAM Systems
Pengfei Sun, Fang Yuan, Bin Yu +1
Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) has been heavily studied as an alternative waveform for 5G systems. Its advantages of higher spect…
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
Aaron J. Li, Robin Netzorg, Zhihan Cheng +2
In recent years, work has gone into developing deep interpretable methods for image classification that clearly attributes a model's output to specific features of the data. One su…
Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization
Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade +2
Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regres…
Towards the Standardization of Non-orthogonal Multiple Access for Next Generation Wireless Networks
Yan Chen, Alireza Bayesteh, Yiqun Wu +13
Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing can root back to the network information theory. For generations of wireless communication syste…
Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing
Hongwei Li, Bin Yu
Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to…
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright +1
Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte Carlo sampling algorithm for drawing samples from smooth probability densities over continuous spaces. We stu…
A Debiased MDI Feature Importance Measure for Random Forests
Xiao Li, Yu Wang, Sumanta Basu +2
Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people rout…
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Changti Wu, Bin Yu, Zhaolong Shen +6
Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policie…
LLMBoost: Make Large Language Models Stronger with Boosting
Zehao Chen, Tianxiang Ai, Yifei Li +11
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or fina…
Localizing axial dense emitters based on single-helix point spread function and deep learning
Yihong Ji, Danni Chen, Hanzhe Wu +4
Stimulated Emission Depletion Microscopy (STED) can achieve a spatial resolution as high as several nanometers. As a point scanning imaging method, it requires 3D scanning to compl…
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
Abhineet Agarwal, Fange Xiao, Rebecca Barter +3
As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on th…
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu +4
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the rea…
Interpreting and improving deep-learning models with reality checks
Chandan Singh, Wooseok Ha, Bin Yu
Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter cov…
Hierarchical interpretations for neural network predictions
Chandan Singh, W. James Murdoch, Bin Yu
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inabilit…
Radio stars of the SKA
Bin Yu, Albert Zijlstra, Biwei Jiang
Radio emission from stars can be used, e.g., to study ionized winds or stellar flares. The radio emission is faint and studies have been limited to few objects. The Square Kilomete…
Highly Conductive 3D Nano-Carbon: Stacked Multilayer Graphene System with Interlayer Decoupling
Tianhua Yu, Changdong Kim, Bin Yu
We investigate electrical conduction and breakdown behavior of 3D nano-carbon-stacked multilayer graphene (s-MLG) system with complete interlayer decoupling. The s-MLG is prepared…
Charge Crowding in Graphene-Silicon Diodes
Muhammad Abid Anwar, Munir Ali, Dong Pu +12
The performance of nanoscale electronic devices based on a two-three dimensional (2D-3D) interface is significantly affected by the electrical contacts that interconnect these mate…
Local MDI+: Local Feature Importances for Tree-Based Models
Zhongyuan Liang, Zachary T. Rewolinski, Abhineet Agarwal +2
Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning models due to their prediction performance and computational efficiency. These adva…
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi, Yan Shuo Tan, Briton Park +4
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC…
Genus two Smale-Williams solenoids in 3-manifolds
Jiming Ma, Bin Yu
Using alternating Heegaard diagrams, we construct some 3-manifolds which admit diffeomorphisms such that the non-wandering sets of the diffeomorphisms are composed of Smale-William…
Localizing axial dense emitters based onsingle-helix point spread function andcompressed sensing
Hanzhe Wu, Danni Chen, YiHong Jiand Gan Xiang +3
Among the approaches in three-dimensional (3D) single molecule localization microscopy, there are several point spread function (PSF) engineering approaches, in which depth informa…
Bridging Discrete and Backpropagation: Straight-Through and Beyond
Liyuan Liu, Chengyu Dong, Xiaodong Liu +2
Backpropagation, the cornerstone of deep learning, is limited to computing gradients for continuous variables. This limitation poses challenges for problems involving discrete late…
Local identifiability of -minimization dictionary learning: a sufficient and almost necessary condition
Siqi Wu, Bin Yu
We study the theoretical properties of learning a dictionary from signals for via -minimization. We assume that 's ar…
A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers
Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright +1
High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible t…
Asymptotic distribution and sparsistency for l1-penalized parametric M-estimators with applications to linear SVM and logistic regression
Guilherme V. Rocha, Xing Wang, Bin Yu
Since its early use in least squares regression problems, the l1-penalization framework for variable selection has been employed in conjunction with a wide range of loss functions…
Euclid's Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks
Shijie Lian, Changti Wu, Laurence Tianruo Yang +4
Spatial intelligence spans a rich suite of abilities, including visualising and transforming shapes, mentally rotating objects, judging relational positions and containment, and es…
Existence of arbitrary large numbers of non--covered Anosov flows on hyperbolic -manifolds
Francois Béguin, Bin Yu
The purpose of this paper is to prove that, for every , there exists a closed hyperbolic -manifold which carries at least non--covered Anosov…
Adaptive wavelet distillation from neural networks through interpretations
Wooseok Ha, Chandan Singh, Francois Lanusse +2
Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many…
Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data
Rebecca L Barter, Bin Yu
The technological advancements of the modern era have enabled the collection of huge amounts of data in science and beyond. Extracting useful information from such massive datasets…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Yangbo He, Jinzhu Jia, Bin Yu
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversib…
Adaptive Test-Time Intervention for Concept Bottleneck Models
Matthew Shen, Aliyah Hsu, Abhineet Agarwal +1
Concept bottleneck models (CBM) aim to improve model interpretability by predicting human level "concepts" in a bottleneck within a deep learning model architecture. However, how t…
Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?
Reza Abbasi-Asl, Cengiz Pehlevan, Bin Yu +1
Several theories of early sensory processing suggest that it whitens sensory stimuli. Here, we test three key predictions of the whitening theory using recordings from 152 ganglion…
Signed iterative random forests to identify enhancer-associated transcription factor binding
Karl Kumbier, Sumanta Basu, Erwin Frise +4
Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alo…
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Yan Shuo Tan, Abhineet Agarwal, Bin Yu
Decision trees are important both as interpretable models amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient bo…
Two-stage growth mode for lift-off mechanism in oblique shock-wave/jet interaction
Bin Yu, Miaosheng He, Bin Zhang +1
The lift-off characteristics of supersonic streamwise vortex in oblique shock-wave/jet interaction (OS/JI for short), extracted from a wall-mount ramp injector in scramjet, is stud…
Artificial Intelligence and Statistics
Bin Yu, Karl Kumbier
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, devel…
Green Shielding: A User-Centric Approach Towards Trustworthy AI
Aaron J. Li, Nicolas Sanchez, Hao Huang +8
Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users phrase queries, a gap not well…
Scaling Analysis of Nanowire Phase Change Memory
Jie Liu, Bin Yu, M. P. Anantram
This letter analyzes the scaling property of nanowire (NW) phase change memory (PCM) using analytic and numerical methods. The scaling scenarios of the three widely-used NW PCM per…
Improved OpenCL-based Implementation of Social Field Pedestrian Model
Bin Yu, Ke Zhu, Kaiteng Wu +1
Two aspects of improvements are proposed for the OpenCL-based implementation of the social field pedestrian model. In the aspect of algorithm, a method based on the idea of divide-…
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Abhineet Agarwal, Yan Shuo Tan, Omer Ronen +2
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice. To mitigate overfitting, trees are typically regularized by…
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Yangbo He, Jinzhu Jia, Bin Yu
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are sai…
On the algebraic stretching dynamics of variable-density mixing in shock-bubble interaction
Xu Han, Bin Yu, Hong Liu
The mixing mechanism within a single-vortex has been a theoretical focus for decades, while remains unclear especially under variable-density (VD) scenario. This study investigates…
Data spectroscopy: Eigenspaces of convolution operators and clustering
Tao Shi, Mikhail Belkin, Bin Yu
This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information con…
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Yangbo He, Bin Yu
The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class c…
Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
Juraj Bodik, Yaxuan Huang, Bin Yu
While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty a…
Instability, Computational Efficiency and Statistical Accuracy
Nhat Ho, Koulik Khamaru, Raaz Dwivedi +3
Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case. The li…
A Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease (COPD)
Varghese Kurian, Navid Ghadipasha, Michelle Gee +7
Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing…
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks
Jakob Heiss, Sören Lambrecht, Jakob Weissteiner +4
We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most…
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Jonathan Peck, Claire Nie, Raaghavi Sivaguru +5
Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names which can be used for command and control (C&C) purposes. Approaches based on…
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods
Nikhil Ghosh, Song Mei, Bin Yu
To understand how deep learning works, it is crucial to understand the training dynamics of neural networks. Several interesting hypotheses about these dynamics have been made base…
Stability
Bin Yu
Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, rep…
On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis
Yan Shuo Tan, Omer Ronen, Theo Saarinen +1
Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression model that is commonly used in causal inference and beyond. Its strong predictive performa…