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09/11/2019
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Distance metric learning (DML) is to learn the embeddings where examples...
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07/23/2019
DR Loss: Improving Object Detection by Distributional Ranking
Most of object detection algorithms can be categorized into two classes:...
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06/19/2019
XNAS: Neural Architecture Search with Expert Advice
This paper introduces a novel optimization method for differential neura...
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06/03/2019
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Satellite-based positioning system such as GPS often suffers from large ...
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05/10/2019
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
For SGD based distributed stochastic optimization, computation complexit...
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05/09/2019
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Recent developments on large-scale distributed machine learning applicat...
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03/19/2019
Conservative Exploration for Semi-Bandits with Linear Generalization: A Product Selection Problem for Urban Warehouses
The recent rising popularity of ultra-fast delivery services on retail p...
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01/30/2019
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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12/10/2018
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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11/28/2018
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Difference of convex (DC) functions cover a broad family of non-convex a...
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05/25/2018
Large-scale Distance Metric Learning with Uncertainty
Distance metric learning (DML) has been studied extensively in the past ...
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05/21/2018
Learning with Non-Convex Truncated Losses by SGD
Learning with a convex loss function has been a dominating paradigm for...
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05/19/2018
Robust Optimization over Multiple Domains
Recently, machine learning becomes important for the cloud computing ser...
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05/11/2018
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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05/08/2018
Multinomial Logit Bandit with Linear Utility Functions
Multinomial logit bandit is a sequential subset selection problem which ...
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12/04/2017
NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization
Accelerated gradient (AG) methods are breakthroughs in convex optimizati...
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07/24/2017
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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05/16/2016
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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05/04/2015
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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04/26/2015
Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion
In this paper, we provide a theoretical analysis of the nuclear-norm reg...
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04/15/2015
Theory of Dual-sparse Regularized Randomized Reduction
In this paper, we study randomized reduction methods, which reduce high-...
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10/06/2014
Top Rank Optimization in Linear Time
Bipartite ranking aims to learn a real-valued ranking function that orde...
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03/22/2014
CUR Algorithm with Incomplete Matrix Observation
CUR matrix decomposition is a randomized algorithm that can efficiently ...
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02/07/2014
Binary Excess Risk for Smooth Convex Surrogates
In statistical learning theory, convex surrogates of the 0-1 loss are hi...
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02/03/2014
Fine-Grained Visual Categorization via Multi-stage Metric Learning
Fine-grained visual categorization (FGVC) is to categorize objects into ...
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01/18/2014
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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11/19/2013
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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02/01/2013
Sparse Multiple Kernel Learning with Geometric Convergence Rate
In this paper, we study the problem of sparse multiple kernel learning (...
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08/30/2012
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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07/11/2012
A Bayesian Approach toward Active Learning for Collaborative Filtering
Collaborative filtering is a useful technique for exploiting the prefere...
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06/27/2012
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
In this work, we develop a simple algorithm for semi-supervised regressi...
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06/20/2012
Bayesian Active Distance Metric Learning
Distance metric learning is an important component for many tasks, such ...
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05/09/2012
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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03/15/2012