Search Results for author:
Found 73 papers, 7 papers with code
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg).
UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing
When doing private domain marketing with cloud services, the merchants usually have to purchase different machine learning models for the multiple marketing purposes, leading to a very high cost.
SimHaze: game engine simulated data for real-world dehazing
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems
Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling
However, existing researches have little ability to model universal user representation based on lifelong behavior sequences since user registration.
Interest-oriented Universal User Representation via Contrastive Learning
Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application.
Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach
In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization.
Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training
We revisit the concept of "adversary" in online learning, motivated by solving robust optimization and adversarial training using online learning methods.
Multi-Agent Coverage in Urban Environments
We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments.
Robotics
Understanding and Resolving Performance Degradation in Graph Convolutional Networks
In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works.
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
To maximize cumulative user engagement (e. g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e. g., click-through rate) and encouraging user browsing (i. e., more items exposured).
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data.
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
Efficient Meta Learning via Minibatch Proximal Update
We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks.
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs
Finding the Nash Equilibrium (NE) of a two player zero-sum game is core to many problems in statistics, optimization, and economics, and for a fixed game matrix this can be easily reduced to solving a linear program.
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data.
Bayesian Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Large Scale Markov Decision Processes with Changing Rewards
We consider Markov Decision Processes (MDPs) where the rewards are unknown and may change in an adversarial manner.
A Unified Framework for Marketing Budget Allocation
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
Risk-Averse Stochastic Convex Bandit
Motivated by applications in clinical trials and finance, we study the problem of online convex optimization (with bandit feedback) where the decision maker is risk-averse.
The Online Saddle Point Problem and Online Convex Optimization with Knapsacks
We relate this problem to the online saddle point problem and establish $O(\sqrt{T})$ regret using a primal-dual algorithm.
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
We develop a novel computationally efficient and general framework for robust hypothesis testing.
Nonlinear Distributional Gradient Temporal-Difference Learning
We devise a distributional variant of gradient temporal-difference (TD) learning.
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.
Projection-Free Algorithms in Statistical Estimation
Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation.
Fast Global Convergence via Landscape of Empirical Loss
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.
Adaptive Recurrent Neural Network Based on Mixture Layer
By leveraging the mixture layer, the proposed method can adaptively update states according to the similarities between encoded inputs and prototype vectors, leading to a stronger capacity in assimilating sequences with multiple patterns.
Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
Reinforcement Learning under Model Mismatch
We study reinforcement learning under model misspecification, where we do not have access to the true environment but only to a reasonably close approximation to it.
Bayesian Pool-based Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate.
Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates
Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm.
Fake News Mitigation via Point Process Based Intervention
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.
SAGA and Restricted Strong Convexity
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.
Linear convergence of SDCA in statistical estimation
In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption.
Outlier Robust Online Learning
We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine.
Linear Convergence of SVRG in Statistical Estimation
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.
Online Nonnegative Matrix Factorization with General Divergences
We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences.
Online Collaborative Learning for Open-Vocabulary Visual Classifiers
Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Avoiding this devise, we propose an accelerated randomized mirror descent method for solving this problem without the strongly convex assumption.
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning.
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
As most deep learning algorithms are stochastic (e. g., Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness arguments of Xu & Mannor, and introduce a new approach, ensemble robustness, that concerns the robustness of a population of hypotheses.
Online Gradient Descent in Function Space
In many problems in machine learning and operations research, we need to optimize a function whose input is a random variable or a probability density function, i. e. to solve optimization problems in an infinite dimensional space.
Online Crowdsourcing
With the success of modern internet based platform, such as Amazon Mechanical Turk, it is now normal to collect a large number of hand labeled samples from non-experts.
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
This paper considers the subspace clustering problem where the data contains irrelevant or corrupted features.
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion
Social trust prediction addresses the significant problem of exploring interactions among users in social networks.
Efficient Online Minimization for Low-Rank Subspace Clustering
Low-rank representation~(LRR) has been a significant method for segmenting data that are generated from a union of subspaces.
Online Optimization for Max-Norm Regularization
The key technique in our algorithm is to reformulate the max-norm into a matrix factorization form, consisting of a basis component and a coefficients one.
Robust Logistic Regression and Classification
We consider logistic regression with arbitrary outliers in the covariate matrix.
Convex Optimization Procedure for Clustering: Theoretical Revisit
In this paper, we present theoretical analysis of SON~--~a convex optimization procedure for clustering using a sum-of-norms (SON) regularization recently proposed in \cite{ICML2011Hocking_419, SON, Lindsten650707, pelckmans2005convex}.
Clustering from Labels and Time-Varying Graphs
Our theoretical results cover and subsume a wide range of existing graph clustering results including planted partition, weighted clustering and partially observed graphs.
Distributed Robust Learning
We propose a framework for distributed robust statistical learning on {\em big contaminated data}.
Online Optimization for Large-Scale Max-Norm Regularization
Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low-rank estimation for the underlying data.
Robust Subspace Segmentation with Block-diagonal Prior
Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix.
Online PCA for Contaminated Data
We consider the online Principal Component Analysis (PCA) for contaminated samples (containing outliers) which are revealed sequentially to the Principal Components (PCs) estimator.
Provable Subspace Clustering: When LRR meets SSC
Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both considered as the state-of-the-art methods for {\em subspace clustering}.
Reinforcement Learning in Robust Markov Decision Processes
An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system.
Online Robust PCA via Stochastic Optimization
Robust PCA methods are typically based on batch optimization and have to load all the samples into memory.
Learning Multiple Models via Regularized Weighting
We demonstrate the robustness benefits of our approach with some experimental results and prove for the important case of clustering that our approach has a non-trivial breakdown point, i. e., is guaranteed to be robust to a fixed percentage of adversarial unbounded outliers.
Noisy Sparse Subspace Clustering
This paper considers the problem of subspace clustering under noise.
Scaling Up Robust MDPs by Reinforcement Learning
We consider large-scale Markov decision processes (MDPs) with parameter uncertainty, under the robust MDP paradigm.
Clustering Sparse Graphs
We develop a new algorithm to cluster sparse unweighted graphs -- i. e. partition the nodes into disjoint clusters so that there is higher density within clusters, and low across clusters.
Improved Graph Clustering
We show that, in the classic stochastic block model setting, it outperforms existing methods by polynomial factors when the cluster size is allowed to have general scalings.
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation
In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of multiple subspaces and the other part consists of outliers.
Clustering Partially Observed Graphs via Convex Optimization
This paper considers the problem of clustering a partially observed unweighted graph---i. e., one where for some node pairs we know there is an edge between them, for some others we know there is no edge, and for the remaining we do not know whether or not there is an edge.
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs
Moreover, we show by an information-theoretic argument that our guarantees are nearly optimal in terms of the fraction of sampled entries on the authentic columns, the fraction of corrupted columns, and the rank of the underlying matrix.
Distributionally Robust Markov Decision Processes
We consider Markov decision processes where the values of the parameters are uncertain.
Robust PCA via Outlier Pursuit
Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers.
Robust Regression and Lasso
We generalize this robust formulation to consider more general uncertainty sets, which all lead to tractable convex optimization problems.
