Search Results for author: Qin Zhang

Found 23 papers, 2 papers with code

Semantic-enhanced Image Clustering

no code implementations21 Aug 2022 Shaotian Cai, Liping Qiu, Xiaojun Chen, Qin Zhang, Longteng Chen

Theoretical result on convergence analysis shows that our proposed method can converge in sublinear speed.

Image Clustering Self-Supervised Learning

Communication-Efficient Collaborative Best Arm Identification

no code implementations18 Aug 2022 Nikolai Karpov, Qin Zhang

We investigate top-$m$ arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function.

Collaborative Best Arm Identification with Limited Communication on Non-IID Data

no code implementations16 Jul 2022 Nikolai Karpov, Qin Zhang

We use a basic problem in bandit theory called best arm identification in multi-armed bandits as a vehicle to deliver the following conceptual message: Collaborative learning on non-IID data is provably more difficult than that on IID data.

Multi-Armed Bandits

SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving

1 code implementation17 Jun 2022 Linrui Zhang, Qin Zhang, Li Shen, Bo Yuan, Xueqian Wang

Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well.

Autonomous Driving reinforcement-learning +1

Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis

no code implementations7 Apr 2022 Yutian Wang, Yuankun Xie, Kun Zhao, Hui Wang, Qin Zhang

In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner.

Quantization Speech Synthesis

Deep Unsupervised Hashing with Latent Semantic Components

no code implementations17 Mar 2022 Qinghong Lin, Xiaojun Chen, Qin Zhang, Shaotian Cai, Wenzhe Zhao, Hongfa Wang

Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited.

Common Sense Reasoning Image Retrieval

A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement

no code implementations5 Sep 2021 Guochen Yu, Yutian Wang, Hui Wang, Qin Zhang, Chengshi Zheng

After that, the second stage is applied to further suppress the residual noise components and estimate the clean phase by a complex spectral mapping network, which is a pure complex-valued network composed of complex 2D convolution/deconvolution and complex temporal-frequency attention blocks.

Denoising Speech Enhancement

Learning to Cluster via Same-Cluster Queries

no code implementations17 Aug 2021 Yi Li, Yan Song, Qin Zhang

We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries.

Deep Self-Adaptive Hashing for Image Retrieval

no code implementations16 Aug 2021 Qinghong Lin, Xiaojun Chen, Qin Zhang, Shangxuan Tian, Yudong Chen

Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning.

Image Retrieval

Batched Thompson Sampling for Multi-Armed Bandits

no code implementations15 Aug 2021 Nikolai Karpov, Qin Zhang

We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small number of policy changes (or, batches).

Multi-Armed Bandits

Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit

no code implementations2 Dec 2020 Nikolai Karpov, Qin Zhang

Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade.

online learning

Batched Coarse Ranking in Multi-Armed Bandits

no code implementations NeurIPS 2020 Nikolai Karpov, Qin Zhang

We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we have a set of arms each of which is associated with an unknown distribution.

Multi-Armed Bandits

A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases

no code implementations6 Nov 2020 Hao Nie, Qin Zhang

In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation.

Multinomial Logit Bandit with Low Switching Cost

no code implementations ICML 2020 Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou

We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.

Collaborative Top Distribution Identifications with Limited Interaction

no code implementations20 Apr 2020 Nikolai Karpov, Qin Zhang, Yuan Zhou

We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants.

Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits

no code implementations5 Apr 2019 Chao Tao, Qin Zhang, Yuan Zhou

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning.

Multi-Armed Bandits

Distinct Sampling on Streaming Data with Near-Duplicates

no code implementations29 Oct 2018 Jiecao Chen, Qin Zhang

In this paper we study how to perform distinct sampling in the streaming model where data contain near-duplicates.

Data Structures and Algorithms

A Practical Algorithm for Distributed Clustering and Outlier Detection

no code implementations NeurIPS 2018 Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang

We study the classic $k$-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers.

Outlier Detection

Tight Bounds for Collaborative PAC Learning via Multiplicative Weights

no code implementations NeurIPS 2018 Jiecao Chen, Qin Zhang, Yuan Zhou

We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously.

PAC learning

Adaptive Multiple-Arm Identification

no code implementations ICML 2017 Jiecao Chen, Xi Chen, Qin Zhang, Yuan Zhou

We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game.

Communication-Optimal Distributed Clustering

no code implementations NeurIPS 2016 Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang

We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site.

EmbedJoin: Efficient Edit Similarity Joins via Embeddings

1 code implementation1 Feb 2017 Haoyu Zhang, Qin Zhang

Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems.


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