Black-Box Prompt Learning for Pre-trained Language Models

Evaluation of Mycobacterium tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling

Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity

Hashtag-Guided Low-Resource Tweet Classification

Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game

Particle-based Variational Inference with Preconditioned Functional Gradient Flow

A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization

Convex Formulation of Overparameterized Deep Neural Networks

Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning

Weakly Supervised Disentangled Generative Causal Representation Learning

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games

A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization

Achieving Minimax Rates in Pool-Based Batch Active Learning

Bayesian Invariant Risk Minimization

Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint

Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums

Exploring Geometric Consistency for Monocular 3D Object Detection

HYPERDQN: A Randomized Exploration Method for Deep Reinforcement Learning

MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation

Minimax Regret Optimization for Robust Machine Learning under Distribution Shift

Model Agnostic Sample Reweighting for Out-of-Distribution Learning

Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity

Multilingual Word Sense Disambiguation with Unified Sense Representation

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets

Probabilistic Bilevel Coreset Selection

Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting

Semi-supervised Monocular 3D Object Detection by Multi-view Consistency

Sparse Invariant Risk Minimization

A Framework of Composite Functional Gradient Methods for Generative Adversarial Models

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate

Finite-sample analysis for decentralized batch multiagent reinforcement learning with networked agents

Local-global memory neural network for medication prediction

Mathematical Models of Overparameterized Neural Networks

A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning

DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

Effective Sparsification of Neural Networks with Global Sparsity Constraint

Efficient Neural Network Training via Forward and Backward Propagation Sparsification

Error Compensated Distributed SGD can be Accelerated

Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling

G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation

Involution: Inverting the inherence of convolution for visual recognition

Involution: Inverting the Inherence of Convolution for Visual Recognition

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks

Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search

Accelerated dual-averaging primal–dual method for composite convex minimization

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks

Local Uncertainty Sampling for Large-Scale Multiclass Logistic Regression

MAP Inference Via ℓ₂ -Sphere Linear Program Reformulation

Proximal Gradient Method for Nonsmooth Optimization Over the Stiefel Manifold

A generalized neural tangent kernel analysis for two-layer neural networks

Black-Box Adversarial Attack with Transferable Model-based Embedding

Bridging the gap between sample-based and one-shot neural architecture search with BONAS

CATCH: Context-Based Meta Reinforcement Learning for Transferrable Architecture Search

Decentralized accelerated proximal gradient descent

Finite-sample analysis for decentralized cooperative multi-agent reinforcement learning from batch data

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks

Improving Chinese word segmentation with wordhood memory networks

Joint Chinese word segmentation and part-of-speech tagging via two-way attentions of auto-analyzed knowledge

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation

Model Rubik’s cube: Twisting Resolution, Depth and Width for TinyNets

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts

Stable Learning via Differentiated Variable Decorrelation

Stable Learning via Sample Reweighting

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data

ZEN: Pre-Training Chinese Text Encoder Enhanced by N-gram Representations

Graph-guided multi-task sparse learning model: A method for identifying antigenic variants of influenza A(H3N2) virus

Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models

Robust Frequent Directions with Application in Online Learning

Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations

DHER: Hindsight experience replay for dynamic goals

Divergence-augmented policy optimization

Doublesqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression

Doublesqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression

Efficient Decision-based Black-box Adversarial Attacks on Face Recognition

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

N Attack: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

N Attack: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Reinforced Training Data Selection for Domain Adaptation

A convex formulation for high-dimensional sparse sliced inverse regression

Bayesian Model Averaging with Exponentiated Least Squares Loss

Gradient hard thresholding pursuit

I-LAMM for sparse learning: Simultaneous control of algorithmic complexity and statistical error

Learning to remember translation history with a continuous cache

Near-optimal stochastic approximation for online principal component estimation

Pathwise coordinate optimization for sparse learning: Algorithm and theory

Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow

An algorithmic framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-gradient method

An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method

Candidates vs. noises estimation for large multi-class classification problem

Candidates vs. noises estimation for large multi-class classification problem

Communication compression for decentralized training

Composite functional gradient learning of generative adversarial models

Composite Functional Gradient Learning of Generative Adversarial Models

End-to-end active object tracking via reinforcement learning

End-to-end active object tracking via reinforcement learning

Error compensated quantized sgd and its applications to large-scale distributed optimization

Error compensated quantized SGD and its applications to large-scale distributed optimization

Exponentially weighted imitation learning for batched historical data

Fully decentralized multi-agent reinforcement learning with networked agents

Fully decentralized multi-agent reinforcement learning with networked agents

Gradient sparsification for communication-efficient distributed optimization

Graphical nonconvex optimization via an adaptive convex relaxation

Graphical nonconvex optimization via an adaptive convex relaxation

Near-optimal non-convex optimization via stochastic path integrated differential estimator

Safe element screening for submodular function minimization

Safe Element Screening for Submodular Function Minimization

Stochastic expectation maximization with variance reduction

Stochastic primal-dual method for empirical risk minimization with O(1) per-iteration complexity

A general distributed dual coordinate optimization framework for regularized loss minimization

Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

Deep pyramid convolutional neural networks for text categorization

Diffusion approximations for online principal component estimation and global convergence

Efficient distributed learning with sparsity

Efficient distributed learning with sparsity

On quadratic convergence of DC proximal Newton algorithm in nonconvex sparse learning

Projection-free distributed online learning in networks

Projection-free distributed online learning in networks

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Towards more efficient SPSD matrix approximation and CUR matrix decomposition

Exact recovery of hard thresholding pursuit

Fast Component Pursuit for Large-scale Inverse Covariance Estimation

Generalized Hierarchical Sparse Model for Arbitrary-order Interactive Antigenic Sites Identification in Flu Virus Data

Learning Additive Exponential Family Graphical Models via ℓ_2,1-norm Regularized M-Estimation

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Supervised and semi-supervised text categorization using LSTM for region embeddings

Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

Learning sparse low-threshold linear classifiers

Adaptive stochastic alternating direction method of multipliers

Adaptive stochastic alternating direction method of multipliers

Crowd fraud detection in internet advertising

Effective use of word order for text categorization with convolutional neural networks

Local smoothness in variance reduced optimization

Quartz: Randomized dual coordinate ascent with arbitrary sampling

Semi-supervised convolutional neural networks for text categorization via region embedding

Stochastic optimization with importance sampling for regularized loss minimization

A proximal stochastic gradient method with progressive variance reduction

Learning nonlinear functions using regularized greedy forest

Optimal computational and statistical rates of convergence for sparse nonconvex learning problems

Partial Gaussian graphical model estimation

Random Design Analysis of Ridge Regression

A convergence rate analysis for LogitBoost, MART and their variant

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Communication-efficient distributed optimization using an approximate Newton-type method

Compressed counting meets compressed sensing

Efficient mini-batch training for stochastic optimization

Gradient hard thresholding pursuit for sparsity-constrained optimization

A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration

A proximal-gradient homotopy method for the sparse least-squares problem

Multi-stage convex relaxation for feature selection

Stochastic Dual Coordinate Ascent methods for regularized loss minimization

Truncated power method for sparse eigenvalue problems

Accelerated mini-batch stochastic dual coordinate ascent

Accelerating stochastic gradient descent using predictive variance reduction

High-dimensional joint sparsity random effects model for multi-task learning

Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes

A General theory of concave regularization for high-dimensional sparse estimation problems

A spectral algorithm for learning Hidden Markov Models

A tail inequality for quadratic forms of subgaussian random vectors

Deviation optimal learning using greedy q-aggregation

Identifying antigenicity-associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning

Tail inequalities for sums of random matrices that depend on the intrinsic dimension

A proximal-gradient homotopy method for the ℓ 1-regularized least-squares problem

Random design analysis of ridge regression

Selective labeling via error bound minimization

Adaptive forward-backward greedy algorithm for learning sparse representations

Concepts and applications for influenza antigenic cartography

Integrative analysis of many weighted Co-Expression networks using tensor computation

Learning with structured sparsity

Robust matrix decomposition with sparse corruptions

Sparse recovery with orthogonal matching pursuit under RIP

Efficient optimal learning for contextual bandits

Greedy model averaging

Learning to search efficiently in high dimensions

Spectral methods for learning multivariate latent tree structure

A computational framework for influenza antigenic cartography

Analysis of multi-stage convex relaxation for sparse regularization

The benefit of group sparsity

Trading accuracy for sparsity in optimization problems with sparsity constraints

Fundamental statistical techniques

Agnostic active learning without constraints

Deep coding network

Image classification using supervector coding of local image descriptors

Improved local coordinate coding using local tangents

Classifying search quries using the web as a source of knowledge.

On the consistency of feature selection using greedy least squares regression

Some sharp performance bounds for least squares regression with L₁ regularization

Sparse Online Learning via Truncated Gradient

A spectral algorithm for learning hidden Markov models

Learning nonlinear dynamic models

Learning with structured sparsity

Multi-label prediction via compressed sensing

Nonlinear learning using local coordinate coding

An online relevant set algorithm for statistical machine translation

Graph-based semi-supervised learning and spectral kernel design

Statistical analysis of Bayes optimal subset ranking

Adaptive forward-backward greedy algorithm for sparse learning with linear models

Multi-stage convex relaxation for learning with sparse regularization

Sparse online learning via truncated gradient

A block bigram prediction model for statistical machine translation

On the effectiveness of Laplacian normalization for graph semi-supervised learning

A general boosting method and its application to learning ranking functions for Web search

Epoch-Greedy algorithm for multi-armed bandits with side information

Margin based active learning

Robust classification of rare queries using web knowledge

Two-view feature generation model for semi-supervised learning

From ε-entropy to KL-entropy: Analysis of minimum information complexity density estimation

Information theoretical upper and lower bounds for statistical estimation

A discriminative global training algorithm for statistical MT

Learning on graph with Laplacian regularization

Linear prediction models with graph regularization for Web-page categorization

Subset ranking using regression

A framework for learning predictive structures from multiple tasks and unlabeled data

Boosting with early stopping: Convergence and consistency

Learning bounds for kernel regression using effective data dimensionality

Text mining: Predictive methods for analyzing unstructured information

A high-performance semi-supervised learning method for text chunking

A localized prediction model for statistical machine translation

Analysis of spectral kernel design based semi-supervised learning

Data dependent concentration bounds for sequential prediction algorithms

Localized upper and lower bounds for some estimation problems

TREC 2005 genomics track experiments at IBM watson

Greedy algorithms for classification - Consistency, convergence rates, and adaptivity

Statistical analysis of some multi-category large margin classification methods

Statistical behavior and consistency of classification methods based on convex risk minimization

Text categorization for a comprehensive time-dependent benchmark

An evaluation of over-the-counter medication sales for syndromic surveillance

An infinity-sample theory for multi-category large margin classification

Class-size independent generalization analsysis of some discriminative multi-category classification methods

Column-generation boosting methods for mixture of kernels

Focused named entity recognition using machine learning

Generalization error bounds for Bayesian mixture algorithms

Learning bounds for a generalized family of Bayesian posterior distributions

On the convergence of MDL density estimation

Solving large scale linear prediction problems using stochastic gradient descent algorithms

Support vector classification with input data uncertainty

Leave-one-out bounds for kernel methods

Sequential greedy approximation for certain convex optimization problems

Performance analysis and evaluation

A robust risk minimization based named entity recognition system

Data-dependent bounds for Bayesian mixture methods

Howtogetachinesename (entity): Segmentation and combination issues.

Named entity recogintion through classifier combination

On the Convergence of Boosting Procedures

Updating an nlp system to fit new domains: an empirical study on the sentence segmentation problem

A decision-tree-based symbolic rule induction system for text categorization

Approximation bounds for some sparse kernel regression algorithms

Covering Number Bounds of Certain Regularized Linear Function Classes

On the consistency of instantaneous rigid motion estimation

On the dual formulation of regularized linear systems

Recommender Systems Using Linear Classifiers

Text Chunking based on a Generalization of Winnow

Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms

Effective dimension and generalization of kernel learning

Generalization performance of some learning problems in hilbert functional spaces

Statistical behavior and consistency of support vector machines, boosting, and beyond

The consistency of greedy algorithms for classification

Rank-one approximation to high order tensors

Text Categorization Based on Regularized Linear Classification Methods

A general greedy approximation algorithm with applications

A leave-one-out cross validation bound for kernel methods with applications in learning

A sequential approximation bound for some sample-dependent convex optimization problems with applications in learning

Convergence of large margin separable linear classification

Empirical study of recommender systems using linear classifiers

Regularized winnow methods

Some sparse approximation bounds for regression problems

Text chunking using regularized Winnow

A Method for Reduced-Order Modeling and Simulation of Large Interconnect Circuits and its Application to PEEC Models with Retardation

A probability analysis on the value of unlabeled data for classification problems

Active learning using adaptive resampling

Large margin Winnow methods for text categorization

Subspace iterative methods for eigenvalue problems

Fast, robust, and consistent camera motion estimation

Some theoretical results concerning the convergence of composition of regularizated linear functions

Theoretical analysis of a class of randomized regularization methods

Eigenvalue perturbation and the generalized Krylov subspace method

On the Homotopy Method for Perturbed Symmetric Generalized Eigenvalue Problems

A linear algorithm for optimal context clustering with application to bi-level image coding

Compression by model combination

Model reduction for peec models including retardation

A progressive ziv-lempel algorithm for image compression

Progressive Ziv-Lempel Encoding of Synthetic Images

Optimal surface smoothing as filter design

Densities of self-similar measures on the line

A general distributed dual coordinate optimization framework for regularized loss minimization

Article

Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

Article

Deep pyramid convolutional neural networks for text categorization

Conference paper

Diffusion approximations for online principal component estimation and global convergence

Conference paper

Efficient distributed learning with sparsity

Conference paper

Efficient distributed learning with sparsity

Conference paper

On quadratic convergence of DC proximal Newton algorithm in nonconvex sparse learning

Conference paper

Projection-free distributed online learning in networks

Conference paper

Projection-free distributed online learning in networks

Conference paper

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Article

Towards more efficient SPSD matrix approximation and CUR matrix decomposition

Article

Exact recovery of hard thresholding pursuit

Conference paper

Fast Component Pursuit for Large-scale Inverse Covariance Estimation

Conference paper

Generalized Hierarchical Sparse Model for Arbitrary-order Interactive Antigenic Sites Identification in Flu Virus Data

Conference paper

Learning Additive Exponential Family Graphical Models via ℓ_2,1-norm Regularized M-Estimation

Conference paper

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Conference paper

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Conference paper

Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

Conference paper

Supervised and semi-supervised text categorization using LSTM for region embeddings

Conference paper

Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

Conference paper

Learning sparse low-threshold linear classifiers

Article

Adaptive stochastic alternating direction method of multipliers

Conference paper

Adaptive stochastic alternating direction method of multipliers

Conference paper

Crowd fraud detection in internet advertising

Conference paper

Effective use of word order for text categorization with convolutional neural networks

Conference paper

Local smoothness in variance reduced optimization

Conference paper

Quartz: Randomized dual coordinate ascent with arbitrary sampling

Conference paper

Semi-supervised convolutional neural networks for text categorization via region embedding

Conference paper

Stochastic optimization with importance sampling for regularized loss minimization

Conference paper

A proximal stochastic gradient method with progressive variance reduction

Article

Learning nonlinear functions using regularized greedy forest

Article

Optimal computational and statistical rates of convergence for sparse nonconvex learning problems

Article

Partial Gaussian graphical model estimation

Article

Random Design Analysis of Ridge Regression

Article

A convergence rate analysis for LogitBoost, MART and their variant

Conference paper

Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Conference paper

Communication-efficient distributed optimization using an approximate Newton-type method

Conference paper

Compressed counting meets compressed sensing

Conference paper

Efficient mini-batch training for stochastic optimization

Conference paper

Gradient hard thresholding pursuit for sparsity-constrained optimization

Conference paper

A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration

Article

A proximal-gradient homotopy method for the sparse least-squares problem

Article

Multi-stage convex relaxation for feature selection

Article

Stochastic Dual Coordinate Ascent methods for regularized loss minimization

Article

Truncated power method for sparse eigenvalue problems

Article

Accelerated mini-batch stochastic dual coordinate ascent

Conference paper

Accelerating stochastic gradient descent using predictive variance reduction

Conference paper

High-dimensional joint sparsity random effects model for multi-task learning

Conference paper

Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes

Conference paper

A General theory of concave regularization for high-dimensional sparse estimation problems

Article

A spectral algorithm for learning Hidden Markov Models

Article

A tail inequality for quadratic forms of subgaussian random vectors

Article

Deviation optimal learning using greedy q-aggregation

Article

Identifying antigenicity-associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning

Article

Tail inequalities for sums of random matrices that depend on the intrinsic dimension

Article

A proximal-gradient homotopy method for the ℓ 1-regularized least-squares problem

Conference paper

Random design analysis of ridge regression

Conference paper

Selective labeling via error bound minimization

Conference paper

Adaptive forward-backward greedy algorithm for learning sparse representations

Article

Concepts and applications for influenza antigenic cartography

Article

Integrative analysis of many weighted Co-Expression networks using tensor computation

Article

Learning with structured sparsity

Article

Robust matrix decomposition with sparse corruptions

Article

Sparse recovery with orthogonal matching pursuit under RIP

Article

Efficient optimal learning for contextual bandits

Conference paper

Greedy model averaging

Conference paper

Learning to search efficiently in high dimensions

Conference paper

Spectral methods for learning multivariate latent tree structure

Conference paper

A computational framework for influenza antigenic cartography

Article

Analysis of multi-stage convex relaxation for sparse regularization

Article

The benefit of group sparsity

Article

Trading accuracy for sparsity in optimization problems with sparsity constraints

Article

Fundamental statistical techniques

Book chapter

Agnostic active learning without constraints

Conference paper

Deep coding network

Conference paper

Image classification using supervector coding of local image descriptors

Conference paper

Improved local coordinate coding using local tangents

Conference paper

Classifying search quries using the web as a source of knowledge.

Article

On the consistency of feature selection using greedy least squares regression

Article

Some sharp performance bounds for least squares regression with L₁ regularization

Article

Sparse Online Learning via Truncated Gradient

Article

A spectral algorithm for learning hidden Markov models

Conference paper

Learning nonlinear dynamic models

Conference paper

Learning with structured sparsity

Conference paper

Multi-label prediction via compressed sensing

Conference paper

Nonlinear learning using local coordinate coding

Conference paper

An online relevant set algorithm for statistical machine translation

Article

Graph-based semi-supervised learning and spectral kernel design

Article

Statistical analysis of Bayes optimal subset ranking

Article

Adaptive forward-backward greedy algorithm for sparse learning with linear models

Conference paper

Multi-stage convex relaxation for learning with sparse regularization

Conference paper

Sparse online learning via truncated gradient

Conference paper

A block bigram prediction model for statistical machine translation

Article

On the effectiveness of Laplacian normalization for graph semi-supervised learning

Article

A general boosting method and its application to learning ranking functions for Web search

Conference paper

Epoch-Greedy algorithm for multi-armed bandits with side information

Conference paper

Margin based active learning

Conference paper

Robust classification of rare queries using web knowledge

Conference paper

Two-view feature generation model for semi-supervised learning

Conference paper

From ε-entropy to KL-entropy: Analysis of minimum information complexity density estimation

Article

Information theoretical upper and lower bounds for statistical estimation

Article

A discriminative global training algorithm for statistical MT

Conference paper

Learning on graph with Laplacian regularization

Conference paper

Linear prediction models with graph regularization for Web-page categorization

Conference paper

Subset ranking using regression

Conference paper

A framework for learning predictive structures from multiple tasks and unlabeled data

Article

Boosting with early stopping: Convergence and consistency

Article

Learning bounds for kernel regression using effective data dimensionality

Article

Text mining: Predictive methods for analyzing unstructured information

Book

A high-performance semi-supervised learning method for text chunking

Conference paper

A localized prediction model for statistical machine translation

Conference paper

Analysis of spectral kernel design based semi-supervised learning

Conference paper

Data dependent concentration bounds for sequential prediction algorithms

Conference paper

Localized upper and lower bounds for some estimation problems

Conference paper

TREC 2005 genomics track experiments at IBM watson

Conference paper

Greedy algorithms for classification - Consistency, convergence rates, and adaptivity

Article

Statistical analysis of some multi-category large margin classification methods

Article

Statistical behavior and consistency of classification methods based on convex risk minimization

Article

Text categorization for a comprehensive time-dependent benchmark

Article

An evaluation of over-the-counter medication sales for syndromic surveillance

Conference paper

An infinity-sample theory for multi-category large margin classification

Conference paper

Class-size independent generalization analsysis of some discriminative multi-category classification methods

Conference paper

Column-generation boosting methods for mixture of kernels

Conference paper

Focused named entity recognition using machine learning

Conference paper

Generalization error bounds for Bayesian mixture algorithms

Conference paper

Learning bounds for a generalized family of Bayesian posterior distributions

Conference paper

On the convergence of MDL density estimation

Conference paper

Solving large scale linear prediction problems using stochastic gradient descent algorithms

Conference paper

Support vector classification with input data uncertainty

Conference paper

Leave-one-out bounds for kernel methods

Article

Sequential greedy approximation for certain convex optimization problems

Article

Performance analysis and evaluation

Book chapter

A robust risk minimization based named entity recognition system

Conference paper

Data-dependent bounds for Bayesian mixture methods

Conference paper

Howtogetachinesename (entity): Segmentation and combination issues.

Conference paper

Named entity recogintion through classifier combination

Conference paper

On the Convergence of Boosting Procedures

Conference paper

Updating an nlp system to fit new domains: an empirical study on the sentence segmentation problem

Conference paper

A decision-tree-based symbolic rule induction system for text categorization

Article

Approximation bounds for some sparse kernel regression algorithms

Article

Covering Number Bounds of Certain Regularized Linear Function Classes

Article

On the consistency of instantaneous rigid motion estimation

Article

On the dual formulation of regularized linear systems

Article

Recommender Systems Using Linear Classifiers

Article

Text Chunking based on a Generalization of Winnow

Article

Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms

Article

Effective dimension and generalization of kernel learning

Conference paper

Generalization performance of some learning problems in hilbert functional spaces

Conference paper

Statistical behavior and consistency of support vector machines, boosting, and beyond

Conference paper

The consistency of greedy algorithms for classification

Conference paper

Rank-one approximation to high order tensors

Article

Text Categorization Based on Regularized Linear Classification Methods

Article

A general greedy approximation algorithm with applications

Conference paper

A leave-one-out cross validation bound for kernel methods with applications in learning

Conference paper

A sequential approximation bound for some sample-dependent convex optimization problems with applications in learning

Conference paper

Convergence of large margin separable linear classification

Conference paper

Empirical study of recommender systems using linear classifiers

Conference paper

Regularized winnow methods

Conference paper

Some sparse approximation bounds for regression problems

Conference paper

Text chunking using regularized Winnow

Conference paper

A Method for Reduced-Order Modeling and Simulation of Large Interconnect Circuits and its Application to PEEC Models with Retardation

Article

A probability analysis on the value of unlabeled data for classification problems

Conference paper

Active learning using adaptive resampling

Conference paper

Large margin Winnow methods for text categorization

Conference paper

Subspace iterative methods for eigenvalue problems

Article

Fast, robust, and consistent camera motion estimation

Conference paper

Some theoretical results concerning the convergence of composition of regularizated linear functions

Conference paper

Theoretical analysis of a class of randomized regularization methods

Conference paper

Eigenvalue perturbation and the generalized Krylov subspace method

Article

On the Homotopy Method for Perturbed Symmetric Generalized Eigenvalue Problems

Article

A linear algorithm for optimal context clustering with application to bi-level image coding

Conference paper

Compression by model combination

Conference paper

Model reduction for peec models including retardation

Conference paper

A progressive ziv-lempel algorithm for image compression

Conference paper

Progressive Ziv-Lempel Encoding of Synthetic Images

Conference paper

Optimal surface smoothing as filter design

Conference paper

Densities of self-similar measures on the line

Article