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Found 122 papers, 21 papers with code
DiDi’s Machine Translation System for WMT2020
This paper describes the DiDi AI Labs’ submission to the WMT2020 news translation shared task.
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
Uncertainty-aware Unsupervised Multi-Object Tracking
The resulting accurate pseudo-tracklets boost learning the feature consistency.
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal
Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.
Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark
With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field.
Ranked #1 on
Semantic Segmentation
on INRIA Aerial Image Labeling
(mIOU metric)
Self-Learning Symmetric Multi-view Probabilistic Clustering
Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module
The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models.
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization in a non-stationary environment.
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization).
Stochastic Gradient Descent without Full Data Shuffle
In this paper, we first conduct a systematic empirical study on existing data shuffling strategies, which reveals that all existing strategies have room for improvement -- they all suffer in terms of I/O performance or convergence rate.
Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms
The aim of this paper is to establish causal relationship between ride-sharing platform's policies and outcomes of interest under complex temporal and/or spatial dependent experiments.
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.
Offline Model-based Adaptable Policy Learning
Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase.
A Deep Value-network Based Approach for Multi-Driver Order Dispatching
In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
In this paper we propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
Reinforcement Learning for Ridesharing: An Extended Survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms.
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.
Optimization and Control Applications
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems
Instead of increasing the fidelity of models for policy learning, we handle the distortion issue via learning to adapt to diverse simulators generated by the offline dataset.
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation
Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.
Domain Adaptation with Incomplete Target Domains
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
Recently the problem of cross-domain object detection has started drawing attention in the computer vision community.
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach
To address this issue, we propose a deep multi-task multi-graph learning approach, which combines two components: (1) multiple multi-graph convolutional (MGC) networks for predicting demands for different service modes, and (2) multi-task learning modules that enable knowledge sharing across multiple MGC networks.
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
DiDi's Machine Translation System for WMT2020
This paper describes DiDi AI Labs' submission to the WMT2020 news translation shared task.
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.
Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
Graph neural networks have become an important tool for modeling structured data.
A Survey on Machine Learning from Few Samples
The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning
Existing approaches for this problem are based on two-timescale or double-loop stochastic gradient algorithms, which may also require sampling large-batch data.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.
PropagationNet: Propagate Points to Curve to Learn Structure Information
Deep learning technique has dramatically boosted the performance of face alignment algorithms.
Ranked #3 on
Face Alignment
on WFLW
Road Network Metric Learning for Estimated Time of Arrival
To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML-ETA).
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
Fusion Recurrent Neural Network
Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN.
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.
Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots
In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem.
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting
Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies.
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
Mutual Learning Network for Multi-Source Domain Adaptation
However, in practice the labeled data can come from multiple source domains with different distributions.
Adaptive Object Detection with Dual Multi-Label Prediction
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation
At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy.
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode.
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Generative adversarial networks (GANs) are a hot research topic recently.
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution
How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs.
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-grained Attention Networks for Single Image Super-Resolution
In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
The key problem in graph node embedding lies in how to define the dependence to neighbors.
AHINE: Adaptive Heterogeneous Information Network Embedding
In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation
DEMER also derives a recommendation policy with a significantly improved performance in the test phase of the real application.
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem.
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Multiagent Systems
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment.
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.
P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.
Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure
As a regression model, it is competitive to the state-of-the-arts sparse models; as a variable selection method, SGLGG is promising for identifying Alzheimer's disease-related risk SNPs.
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
Firstly, we pre-train CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.
Efficient Approximate Solutions to Mutual Information Based Global Feature Selection
Estimating the MI for a subset of features is often intractable.
Coupled Support Vector Machines for Supervised Domain Adaptation
Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
Nonconvex One-bit Single-label Multi-label Learning
We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels.
The Second Order Linear Model
We show that the conventional gradient descent heuristic is biased by the skewness of the distribution therefore is no longer the best practice of learning the SLM.
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing
We develop an efficient alternating framework for learning a generalized version of Factorization Machine (gFM) on steaming data with provable guarantees.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the inactive features and samples that are guaranteed to be irrelevant to the outputs.
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
By a novel hierarchical projection algorithm, MLFre is able to test the nodes independently from any of their ancestor nodes.
HONOR: Hybrid Optimization for NOn-convex Regularized problems
(2) We establish a rigorous convergence analysis for HONOR, which shows that convergence is guaranteed even for non-convex problems, while it is typically challenging to analyze the convergence for non-convex problems.
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices
One of the appealing features of DPC is that: it is safe in the sense that the detected inactive features are guaranteed to have zero coefficients in the solution vectors across all tasks.
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\ell_1$ and $\ell_2$ norms.
Persistent Homology in Sparse Regression and Its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
Linear Convergence of Variance-Reduced Stochastic Gradient without Strong Convexity
Under the strongly convex condition, these variance-reduced stochastic gradient algorithms achieve a linear convergence rate.
Geodesic Distance Function Learning via Heat Flow on Vector Fields
Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself.
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
Generalization Bounds for Representative Domain Adaptation
Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation.
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint
Our new bounds are consistent with the bounds of a special case (least squares) and fills a previously existing theoretical gap for general convex smooth functions; 3) We show that the restricted strong convexity condition is satisfied if the number of independent samples is more than $\bar{k}\log d$ where $\bar{k}$ is the sparsity number and $d$ is the dimension of the variable; 4) We apply FoBa-gdt (with the conditional random field objective) to the sensor selection problem for human indoor activity recognition and our results show that FoBa-gdt outperforms other methods (including the ones based on forward greedy selection and L1-regularization).
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
Some appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3) DVI is independent of the solvers and can be integrated with any existing efficient solvers.
Safe Screening With Variational Inequalities and Its Application to LASSO
Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model.
Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
One key building block of the proposed algorithm is the l1q-regularized Euclidean projection (EP_1q).
A Safe Screening Rule for Sparse Logistic Regression
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection.
Dictionary LASSO: Guaranteed Sparse Recovery under Linear Transformation
Specifically, we show 1) in the noiseless case, if the condition number of $D$ is bounded and the measurement number $n\geq \Omega(s\log(p))$ where $s$ is the sparsity number, then the true solution can be recovered with high probability; and 2) in the noisy case, if the condition number of $D$ is bounded and the measurement increases faster than $s\log(p)$, that is, $s\log(p)=o(n)$, the estimate error converges to zero with probability 1 when $p$ and $s$ go to infinity.
Generalization Bounds for Domain Adaptation
Afterwards, we analyze the asymptotic convergence and the rate of convergence of the learning process for such kind of domain adaptation.
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems.
Multi-task Vector Field Learning
MTVFL has the following key properties: (1) the vector fields we learned are close to the gradient fields of the prediction functions; (2) within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace; (3) the vector fields from all tasks share a low dimensional subspace.
Multi-Stage Multi-Task Feature Learning
In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer.
Lasso Screening Rules via Dual Polytope Projection
To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i. e., predictors that have $0$ components in the solution vector.
Fused Multiple Graphical Lasso
We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD.
Clustered Multi-Task Learning Via Alternating Structure Optimization
We further establish the equivalence relationship between the proposed convex relaxation of CMTL and an existing convex relaxation of ASO, and show that the proposed convex CMTL formulation is significantly more efficient especially for high-dimensional data.
Efficient Methods for Overlapping Group Lasso
There have been several recent attempts to study a more general formulation, where groups of features are given, potentially with overlaps between the groups.
Projection onto A Nonnegative Max-Heap
In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called \emph{maximal root-tree} of the subtree rooted at each node.
A Two-Stage Weighting Framework for Multi-Source Domain Adaptation
In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data.
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis
This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.
Multi-Stage Dantzig Selector
We show that if $X$ obeys a certain condition, then with a large probability the difference between the solution $\hat\beta$ estimated by the proposed method and the true solution $\beta^*$ measured in terms of the $l_p$ norm ($p\geq 1$) is bounded as \begin{equation*} \|\hat\beta-\beta^*\|_p\leq \left(C(s-N)^{1/p}\sqrt{\log m}+\Delta\right)\sigma, \end{equation*} $C$ is a constant, $s$ is the number of nonzero entries in $\beta^*$, $\Delta$ is independent of $m$ and is much smaller than the first term, and $N$ is the number of entries of $\beta^*$ larger than a certain value in the order of $\mathcal{O}(\sigma\sqrt{\log m})$.
Moreau-Yosida Regularization for Grouped Tree Structure Learning
The structured regularization with a pre-defined tree structure is based on a group-Lasso penalty, where one group is defined for each node in the tree.
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
Efficient Recovery of Jointly Sparse Vectors
MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).
Multi-label Multiple Kernel Learning
We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.
Discriminative K-means for Clustering
The connection between DisKmeans and several other clustering algorithms is also analyzed.
