Search Results for author:
Found 153 papers, 27 papers with code
Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models
Large-scale pre-trained language models have achieved great success on natural language generation tasks.
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism
Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.
Large-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents.
Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters
On the other hand, in distributionally robust optimization, we seek data-driven decisions which perform well under the most adverse distribution from a nominal distribution constructed from data samples within a certain discrepancy of probability distributions.
Learning to Infer Belief Embedded Communication
In multi-agent collaboration problems with communication, an agent's ability to encode their intention and interpret other agents' strategies is critical for planning their future actions.
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning
Our results also demonstrate the advantage of the switch transformer model for absorbing expert knowledge and the importance of value distribution in evaluating the trajectory.
Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data
Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
First, we devise a plug-and-play dynamic head and adopt a filter scaling strategy to improve the expressiveness of the network.
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.
Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks
Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance.
A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures
Epilepsy is the fourth most common neurological disorder and affects people of all ages worldwide.
Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years.
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems.
Learning Predictive, Online Approximations of Explanatory, Offline Algorithms
In this work, we introduce a general methodology for approximating offline algorithms in online settings.
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.
Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis
In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator.
Review Polarity-wise Recommender
To be specific, in this model, positive and negative reviews are separately gathered and utilized to model the user-preferred and user-rejected aspects, respectively.
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles.
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving
Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.
BLOCKEYE: Hunting For DeFi Attacks on Blockchain
We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before.
Cryptography and Security Computers and Society
Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue
We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue.
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance).
Morphology Matters: A Multilingual Language Modeling Analysis
We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features.
Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation
Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large.
Label-Wise Document Pre-Training for Multi-Label Text Classification
A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations.
Ranked #1 on
Multi-Label Text Classification
on AAPD
(Micro F1 metric)
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
User intent classification plays a vital role in dialogue systems.
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME).
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python
We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e. g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies.
The huge Package for High-dimensional Undirected Graph Estimation in R
We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data.
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
In this paper, we explore the slot tagging with only a few labeled support sentences (a. k. a.
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation
With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality.
Neural Polysynthetic Language Modelling
In the literature, languages like Finnish or Turkish are held up as extreme examples of complexity that challenge common modelling assumptions.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach
This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification.
EQL -- an extremely easy to learn knowledge graph query language, achieving highspeed and precise search
EQL, also named as Extremely Simple Query Language, can be widely used in the field of knowledge graph, precise search, strong artificial intelligence, database, smart speaker , patent search and other fields.
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans.
Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning
An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary.
Reconstructing Capsule Networks for Zero-shot Intent Classification
With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.
Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects.
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors.
Scalable Differentially Private Data Generation via Private Aggregation of Teacher Ensembles
We present a novel approach named G-PATE for training differentially private data generator.
Attributed Graph Learning with 2-D Graph Convolution
Graph convolutional neural networks have demonstrated promising performance in attributed graph learning, thanks to the use of graph convolution that effectively combines graph structures and node features for learning node representations.
AdvCodec: Towards A Unified Framework for Adversarial Text Generation
In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability $1- {\alpha}$.
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
Attributed Graph Clustering via Adaptive Graph Convolution
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes.
Ranked #3 on
Graph Clustering
on Cora
(NMI metric)
GLAD: Learning Sparse Graph Recovery
Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.
Label Efficient Semi-Supervised Learning via Graph Filtering
However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods.
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents
This work appears to be the first finite-sample analysis for batch MARL, a step towards rigorous theoretical understanding of general MARL algorithms in the finite-sample regime.
Exponentially Weighted Imitation Learning for Batched Historical Data
We consider deep policy learning with only batched historical trajectories.
Sketching Method for Large Scale Combinatorial Inference
In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.
Performance assessment of the deep learning technologies in grading glaucoma severity
Among these CNNs, the DenseNet had the highest classification accuracy (i. e., 75. 50%) based on pre-trained weights when using global ROIs, as compared to 65. 50% when using local ROIs.
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.
Super-pixel cloud detection using Hierarchical Fusion CNN
Secondly, super-pixel level database is used to train our cloud detection models based on CNN and deep forest.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.
Fully Implicit Online Learning
Regularized online learning is widely used in machine learning applications.
High-Temperature Structure Detection in Ferromagnets
The goal is to distinguish whether the underlying graph is empty, i. e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.
A convex formulation for high-dimensional sparse sliced inverse regression
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates.
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decision making tasks.
Multiagent Systems
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis.
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory.
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
Compared to the naive interior point method, our method reduces the computational complexity of solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to $\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an $\epsilon$-optimal solution.
Feedback-Based Tree Search for Reinforcement Learning
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process.
Discrete Factorization Machines for Fast Feature-based Recommendation
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.
The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated viathe World Wide Web.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
We consider estimating the parametric components of semiparametric multi-index models in high dimensions.
Parametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting.
Property Testing in High Dimensional Ising models
In terms of methodological development, we propose two types of correlation based tests: computationally efficient screening for ferromagnets, and score type tests for general models, including a fast cycle presence test.
Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs
Our framework is based on the Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of the inter-subject precision matrix.
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
We consider estimating the parametric component of single index models in high dimensions.
Adaptive Inferential Method for Monotone Graph Invariants
In this paper, we propose a new inferential framework for testing nested multiple hypotheses and constructing confidence intervals of the unknown graph invariants under undirected graphical models.
CANE: Context-Aware Network Embedding for Relation Modeling
Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors.
Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.
Continual Learning in Generative Adversarial Nets
Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions.
Homotopy Parametric Simplex Method for Sparse Learning
High dimensional sparse learning has imposed a great computational challenge to large scale data analysis.
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization
We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e. g., low-rank matrix factorization, phase retrieval, and deep linear neural networks)}.
Agnostic Estimation for Misspecified Phase Retrieval Models
The goal of noisy high-dimensional phase retrieval is to estimate an $s$-sparse parameter $\boldsymbol{\beta}^*\in \mathbb{R}^d$ from $n$ realizations of the model $Y = (\boldsymbol{X}^{\top} \boldsymbol{\beta}^*)^2 + \varepsilon$.
Blind Attacks on Machine Learners
We study the potential of a “blind attacker” to provably limit a learner’s performance by data injection attack without observing the learner’s training set or any parameter of the distribution from which it is drawn.
Max-Norm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary sampling schemes.
Tensor Graphical Model: Non-convex Optimization and Statistical Inference
We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data.
Combinatorial Inference for Graphical Models
We propose a new family of combinatorial inference problems for graphical models.
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
In particular, we first show that for a family of quadratic minimization problems, the iteration complexity $\mathcal{O}(\log^2(p)\cdot\log(1/\epsilon))$ of the CBCD-type methods matches that of the GD methods in term of dependency on $p$, up to a $\log^2 p$ factor.
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility.
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints.
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large family of high-dimensional statistical models, including sparse Fisher's discriminant analysis, canonical correlation analysis, and sufficient dimension reduction.
Near-Optimal Stochastic Approximation for Online Principal Component Estimation
We prove for the first time a nearly optimal finite-sample error bound for the online PCA algorithm.
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
Based upon an oracle model of computation, which captures the interactions between algorithms and data, we establish a general lower bound that explicitly connects the minimum testing risk under computational budget constraints with the intrinsic probabilistic and combinatorial structures of statistical problems.
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
The testing procedures are based on a high dimensional, debiasing-free moment estimator, which uses a novel kernel smoothed Kendall's tau correlation matrix as an input statistic.
Robust Portfolio Optimization
We propose a robust portfolio optimization approach based on quantile statistics.
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
Local Smoothness in Variance Reduced Optimization
Abstract We propose a family of non-uniform sampling strategies to provably speed up a class of stochastic optimization algorithms with linear convergence including Stochastic Variance Reduced Gradient (SVRG) and Stochastic Dual Coordinate Ascent (SDCA).
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data.
A Nonconvex Optimization Framework for Low Rank Matrix Estimation
We study the estimation of low rank matrices via nonconvex optimization.
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.
A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
Our main theoretical contribution is to establish a unified Z-estimation theory of confidence regions for high dimensional problems.
Optimal linear estimation under unknown nonlinear transform
This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing.
Graphical Fermat's Principle and Triangle-Free Graph Estimation
We consider the problem of estimating undirected triangle-free graphs of high dimensional distributions.
The Knowledge Gradient Policy Using A Sparse Additive Belief Model
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power.
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
We develop a novel procedure for constructing confidence bands for components of a sparse additive model.
Statistical Limits of Convex Relaxations
Many high dimensional sparse learning problems are formulated as nonconvex optimization.
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Theoretically, we develop a theory for testing the equality of U-statistic based correlation matrices.
Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Due to the presence of unknown marginal transformations, we propose a pseudo likelihood based inferential approach.
Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor Truncated Power (TTP) method, that incorporates variable selection into the estimation of decomposition components.
A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression
Our de-biasing procedure does not require solving the $L_1$-penalized composite quantile regression.
On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters.
High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models.
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
This is the first result on the computational and statistical guarantees of the pathwise coordinate optimization framework in high dimensions.
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models.
A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
(i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
In this paper, we propose a two-stage sparse PCA procedure that attains the optimal principal subspace estimator in polynomial time.
Accelerated Mini-batch Randomized Block Coordinate Descent Method
When the regularization function is block separable, we can solve the minimization problems in a randomized block coordinate descent (RBCD) manner.
Sparse PCA with Oracle Property
In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank-$k$, and attains a $\sqrt{s/n}$ statistical rate of convergence with $s$ being the subspace sparsity level and $n$ the sample size.
Multivariate Regression with Calibration
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models.
Mode Estimation for High Dimensional Discrete Tree Graphical Models
Though the mode finding problem is generally intractable in high dimensions, this paper unveils that, if the distribution can be approximated well by a tree graphical model, mode characterization is significantly easier.
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions
For smooth convex problems, the SCGD can be accelerated to converge at a rate of $O(k^{-2/7})$ in the general case and $O(k^{-4/5})$ in the strongly convex case.
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time
To optimally estimate sparse principal subspaces, we propose a two-stage computational framework named "tighten after relax": Within the 'relax' stage, we approximately solve a convex relaxation of sparse PCA with early stopping to obtain a desired initial estimator; For the 'tighten' stage, we propose a novel algorithm called sparse orthogonal iteration pursuit (SOAP), which iteratively refines the initial estimator by directly solving the underlying nonconvex problem.
High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Theoretically, the proposed methods achieve the same rates of convergence for both precision matrix estimation and eigenvector estimation, as if the latent variables were observed.
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA).
Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
For the first approach, we note that the zero vector can be taken as the initial basic (infeasible) solution for the linear programming problem and therefore, if the true signal is very sparse, some variants of the simplex method can be expected to take only a small number of pivots to arrive at a solution.
Sparse Inverse Covariance Estimation with Calibration
We propose a semiparametric procedure for estimating high dimensional sparse inverse covariance matrix.
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
In this paper we focus on the principal component regression and its application to high dimension non-Gaussian data.
Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions.
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
In the non-sparse setting, we show that ECA's performance is highly related to the effective rank of the covariance matrix.
Challenges of Big Data Analysis
Big Data bring new opportunities to modern society and challenges to data scientists.
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
In addition, we provide thorough experiments on both synthetic and real-world equity data to show that there are empirical advantages of our method over the lasso-type estimators in both parameter estimation and forecasting.
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$.
Optimal Feature Selection in High-Dimensional Discriminant Analysis
Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size n, dimensionality p, and sparsity level s in the high-dimensional setting.
Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
In particular, our analysis improves upon existing results by providing a more refined sample complexity bound as well as an exact support recovery result for the final estimator.
Statistical analysis of latent generalized correlation matrix estimation in transelliptical distribution
The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix.
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models.
Exponential Concentration for Mutual Information Estimation with Application to Forests
We prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information.
Graph Estimation From Multi-attribute Data
Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes.
Graph-Valued Regression
In this paper, we propose a semiparametric method for estimating $G(x)$ that builds a tree on the $X$ space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph.
Multivariate Dyadic Regression Trees for Sparse Learning Problems
We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs).
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
In this paper, we present StARS: a new stability-based method for choosing the regularization parameter in high dimensional inference for undirected graphs.
Nonparametric Greedy Algorithms for the Sparse Learning Problem
This paper studies the forward greedy strategy in sparse nonparametric regression.
Nonparametric regression and classification with joint sparsity constraints
We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints.
