
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Distance metric learning (DML) is to learn the embeddings where examples...
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Stochastic Optimization for DC Functions and Nonsmooth Nonconvex Regularizers with Nonasymptotic Convergence
Difference of convex (DC) functions cover a broad family of nonconvex a...
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Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
Factorization machine (FM) is a popular machine learning model to captur...
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Robust Gaussian Process Regression for RealTime High Precision GPS Signal Enhancement
Satellitebased positioning system such as GPS often suffers from large ...
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XNAS: Neural Architecture Search with Expert Advice
This paper introduces a novel optimization method for differential neura...
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DR Loss: Improving Object Detection by Distributional Ranking
Most of object detection algorithms can be categorized into two classes:...
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Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Although deep learning models are highly effective for various learning ...
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Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient
This work focuses on dynamic regret of online convex optimization that c...
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Top Rank Optimization in Linear Time
Bipartite ranking aims to learn a realvalued ranking function that orde...
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An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
In this paper, we consider the problem of column subset selection. We pr...
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Analysis of Nuclear Norm Regularization for Fullrank Matrix Completion
In this paper, we provide a theoretical analysis of the nuclearnorm reg...
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Theory of Dualsparse Regularized Randomized Reduction
In this paper, we study randomized reduction methods, which reduce high...
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A Bayesian Framework for Community Detection Integrating Content and Link
This paper addresses the problem of community detection in networked dat...
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CUR Algorithm with Incomplete Matrix Observation
CUR matrix decomposition is a randomized algorithm that can efficiently ...
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Binary Excess Risk for Smooth Convex Surrogates
In statistical learning theory, convex surrogates of the 01 loss are hi...
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Excess Risk Bounds for Exponentially Concave Losses
The overarching goal of this paper is to derive excess risk bounds for l...
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Beating the Minimax Rate of Active Learning with Prior Knowledge
Active learning refers to the learning protocol where the learner is all...
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Sparse Multiple Kernel Learning with Geometric Convergence Rate
In this paper, we study the problem of sparse multiple kernel learning (...
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An Improved Bound for the Nystrom Method for Large Eigengap
We develop an improved bound for the approximation error of the Nyström ...
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A Bayesian Approach toward Active Learning for Collaborative Filtering
Collaborative filtering is a useful technique for exploiting the prefere...
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A Simple Algorithm for Semisupervised Learning with Improved Generalization Error Bound
In this work, we develop a simple algorithm for semisupervised regressi...
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Bayesian Active Distance Metric Learning
Distance metric learning is an important component for many tasks, such ...
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Robust Metric Learning by Smooth Optimization
Most existing distance metric learning methods assume perfect side infor...
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FineGrained Visual Categorization via Multistage Metric Learning
Finegrained visual categorization (FGVC) is to categorize objects into ...
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NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for NonConvex Optimization
Accelerated gradient (AG) methods are breakthroughs in convex optimizati...
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Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Error bound conditions (EBC) are properties that characterize the growth...
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Learning with NonConvex Truncated Losses by SGD
Learning with a convex loss function has been a dominating paradigm for...
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Robust Optimization over Multiple Domains
Recently, machine learning becomes important for the cloud computing ser...
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Multinomial Logit Bandit with Linear Utility Functions
Multinomial logit bandit is a sequential subset selection problem which ...
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Largescale Distance Metric Learning with Uncertainty
Distance metric learning (DML) has been studied extensively in the past ...
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Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD?
Stagewise training strategy is commonly used for learning neural network...
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Conservative Exploration for SemiBandits with Linear Generalization: A Product Selection Problem for Urban Warehouses
The recent rising popularity of ultrafast delivery services on retail p...
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On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed NonConvex Optimization
Recent developments on largescale distributed machine learning applicat...
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On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic NonConvex Optimization
For SGD based distributed stochastic optimization, computation complexit...
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Rong Jin
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Research Scientist at Facebook, Research Assistant at Indiana University Bloomington from 20102017, Assistant Instructor at Indiana University Bloomington from 20102014, Data Analyst at Megaputer Intelligence Inc. 2013