Search Results for author: Yuan Zhou

Found 101 papers, 19 papers with code

Allegro: Open the Black Box of Commercial-Level Video Generation Model

1 code implementation20 Oct 2024 Yuan Zhou, Qiuyue Wang, Yuxuan Cai, Huan Yang

Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models.

Video Generation

Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning

no code implementations7 Oct 2024 Chen Zhang, Huan Hu, Yuan Zhou, Qiyang Cao, Ruochen Liu, Wenya Wei, Elvis S. Liu

To address the challenges of navigation and combat in modern 3D FPS games, we introduce a method that combines navigation mesh (Navmesh) and shooting-rule with deep reinforcement learning (NSRL).

FPS Games

Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnostics

no code implementations2 Oct 2024 Yuan Zhou, Peng Zhang, Mengya Song, Alice Zheng, Yiwen Lu, Zhiheng Liu, Yong Chen, Zhaohan Xi

In this work, we introduce ZODIAC, an LLM-powered framework with cardiologist-level professionalism designed to engage LLMs in cardiological diagnostics.

Electrocardiography (ECG)

A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy

no code implementations29 Aug 2024 Jiameng Lyu, Jinxing Xie, Shilin Yuan, Yuan Zhou

Our meta-policy is flexible enough to be applied to a general inventory systems framework covering a wide range of inventory management problems with myopic clairvoyant optimal policy.

Computational Efficiency

DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion

no code implementations17 Jul 2024 Huiguo He, Huan Yang, Zixi Tuo, Yuan Zhou, Qiuyue Wang, Yuhang Zhang, Zeyu Liu, Wenhao Huang, Hongyang Chao, Jian Yin

DreamStory consists of (1) an LLM acting as a story director and (2) an innovative Multi-Subject consistent Diffusion model (MSD) for generating consistent multi-subject across the images.

Descriptive Story Visualization

A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints

no code implementations8 Jul 2024 Xi Chen, Mo Liu, Yining Wang, Yuan Zhou

In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints.

Assortment Optimization

Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems

no code implementations6 Jul 2024 Jiameng Lyu, Shilin Yuan, Bingkun Zhou, Yuan Zhou

Under the \alpha-global strong convexity condition, we demonstrate that the worst-case regret of any data-driven method is lower bounded by \Omega(\log T/\alpha), which is the first lower bound result that matches the existing upper bound with respect to both parameter \alpha and time horizon T. Along the way, we propose to analyze the SAA regret via a new gradient approximation technique, as well as a new class of smooth inverted-hat-shaped hard problem instances that might be of independent interest for the lower bounds of broader data-driven problems.

Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection

1 code implementation28 Jun 2024 Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang

STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning (DSN) module that learns the relative spatial relations between sequences in a feature space.

Anomaly Detection Time Series +1

Text-Animator: Controllable Visual Text Video Generation

no code implementations25 Jun 2024 Lin Liu, Quande Liu, Shengju Qian, Yuan Zhou, Wengang Zhou, Houqiang Li, Lingxi Xie, Qi Tian

Video generation is a challenging yet pivotal task in various industries, such as gaming, e-commerce, and advertising.

Text Generation Video Generation

Proof-of-Learning with Incentive Security

no code implementations13 Apr 2024 Zishuo Zhao, Zhixuan Fang, Xuechao Wang, Xi Chen, Yuan Zhou

Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance.

Computational Efficiency POS

Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning

no code implementations17 Mar 2024 Yuan Zhou, Richang Hong, Yanrong Guo, Lin Liu, Shijie Hao, Hanwang Zhang

In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i. e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories.

class-incremental learning Disentanglement +3

Evaluating Decision Optimality of Autonomous Driving via Metamorphic Testing

no code implementations28 Feb 2024 Mingfei Cheng, Yuan Zhou, Xiaofei Xie, Junjie Wang, Guozhu Meng, Kairui Yang

In this paper, we focus on evaluating the decision-making quality of an ADS and propose the first method for detecting non-optimal decision scenarios (NoDSs), where the ADS does not compute optimal paths for AVs.

Autonomous Driving Decision Making

SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism

no code implementations1 Feb 2024 Yuang Zhang, Haonan An, Zhengru Fang, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang

Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.

Autonomous Driving

Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking

1 code implementation17 Nov 2023 Hong Liu, Yucheng Cai, Yuan Zhou, Zhijian Ou, Yi Huang, Junlan Feng

Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys.

Continual Learning dialog state tracking

Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks

no code implementations13 Oct 2023 Harsh Patel, Yuan Zhou, Alexander P Lamb, Shu Wang, Jieliang Luo

By leveraging operational data as a foundation for the agent's actions, we enhance the explainability of the agent's actions, foster more robust recommendations, and minimize error.

Reinforcement Learning (RL) Scheduling

Dual Feature Augmentation Network for Generalized Zero-shot Learning

1 code implementation25 Sep 2023 Lei Xiang, Yuan Zhou, Haoran Duan, Yang Long

To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features.

Attribute Diversity +1

Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving

no code implementations15 Sep 2023 Senkang Hu, Zhengru Fang, Haonan An, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang

To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.

Autonomous Driving

End-to-end Hyperspectral Image Change Detection Network Based on Band Selection

no code implementations23 Jul 2023 Qingren Yao, Yuan Zhou, Chang Tang, Wei Xiang

For hyperspectral image change detection (HSI-CD), one key challenge is to reduce band redundancy, as only a few bands are crucial for change detection while other bands may be adverse to it.

Change Detection

ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression

no code implementations26 May 2023 Yixin Wan, Yuan Zhou, Xiulian Peng, Kai-Wei Chang, Yan Lu

To begin with, we are among the first to comprehensively investigate mainstream KD techniques on DNS models to resolve the two challenges.

Knowledge Distillation

Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation

no code implementations18 May 2023 Yuan Zhou, Xin Chen, Yanrong Guo, Shijie Hao, Richang Hong, Qi Tian

Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories.

Few-Shot Semantic Segmentation Incremental Learning +3

Medical visual question answering using joint self-supervised learning

no code implementations25 Feb 2023 Yuan Zhou, Jing Mei, Yiqin Yu, Tanveer Syeda-Mahmood

Visual Question Answering (VQA) becomes one of the most active research problems in the medical imaging domain.

Decoder Diversity +4

Real-time speech enhancement with dynamic attention span

no code implementations21 Feb 2023 Chengyu Zheng, Yuan Zhou, Xiulian Peng, Yuan Zhang, Yan Lu

For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge.

Acoustic echo cancellation Decoder +1

Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning

no code implementations15 Oct 2022 Zihan Zhang, Yuhang Jiang, Yuan Zhou, Xiangyang Ji

Meanwhile, we show that to achieve $\tilde{O}(\mathrm{poly}(S, A, H)\sqrt{K})$ regret, the number of batches is at least $\Omega\left(H/\log_A(K)+ \log_2\log_2(K) \right)$, which matches our upper bound up to logarithmic terms.

reinforcement-learning Reinforcement Learning +1

AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning

no code implementations3 Aug 2022 Wangyang Yue, Yuan Zhou, Xiaochuan Zhang, Yuchen Hua, Zhiyuan Wang, Guang Kou

Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment.

reinforcement-learning Reinforcement Learning +1

Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing

no code implementations22 Jul 2022 Xi Chen, Jiameng Lyu, Yining Wang, Yuan Zhou

We introduce the regularized revenue, i. e., the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal.

Cloud Computing Fairness +1

Imitation Learning from Observations under Transition Model Disparity

1 code implementation ICLR 2022 Tanmay Gangwani, Yuan Zhou, Jian Peng

In this work, we propose an algorithm that trains an intermediary policy in the learner environment and uses it as a surrogate expert for the learner.

Imitation Learning

Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving

no code implementations2 Mar 2022 Xingshuo Han, Guowen Xu, Yuan Zhou, Xuehuan Yang, Jiwei Li, Tianwei Zhang

However, DNN models are vulnerable to different types of adversarial attacks, which pose significant risks to the security and safety of the vehicles and passengers.

Autonomous Driving Backdoor Attack +1

Self-normalized Classification of Parkinson's Disease DaTscan Images

no code implementations27 Dec 2021 Yuan Zhou, Hemant D. Tagare

Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region.

Classification

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

no code implementations2 Dec 2021 Ziyuan Zhong, Yun Tang, Yuan Zhou, Vania de Oliveira Neves, Yang Liu, Baishakhi Ray

To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works.

Learning Long-Term Reward Redistribution via Randomized Return Decomposition

1 code implementation ICLR 2022 Zhizhou Ren, Ruihan Guo, Yuan Zhou, Jian Peng

Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning.

Attribute reinforcement-learning +2

Fairness-aware Online Price Discrimination with Nonparametric Demand Models

no code implementations16 Nov 2021 Xi Chen, Jiameng Lyu, Xuan Zhang, Yuan Zhou

To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret.

Fairness

Full-attention based Neural Architecture Search using Context Auto-regression

no code implementations13 Nov 2021 Yuan Zhou, Haiyang Wang, Shuwei Huo, Boyu Wang

Thus, it is appropriate to consider using NAS methods to discover a better self-attention architecture automatically.

Fine-Grained Image Recognition Image Classification +5

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

no code implementations13 Aug 2021 Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan

However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.

Data Augmentation Image Generation +6

Few-shot Learning with Global Relatedness Decoupled-Distillation

no code implementations12 Jul 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, ZhengJun Zha, Meng Wang

To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.

Few-Shot Learning Metric Learning

Coordinate-wise Control Variates for Deep Policy Gradients

no code implementations11 Jul 2021 Yuanyi Zhong, Yuan Zhou, Jian Peng

The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice.

continuous-control Continuous Control

Few-shot Partial Multi-view Learning

no code implementations5 May 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, Jiebo Luo

The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn.

Few-Shot Learning MULTI-VIEW LEARNING

Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records

no code implementations13 Jan 2021 Yiqin Yu, Pin-Yu Chen, Yuan Zhou, Jing Mei

With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems.

Data Augmentation Domain Adaptation

Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition

no code implementations NeurIPS 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

We study the reinforcement learning problem in the setting of finite-horizon1episodic Markov Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a model-free algorithm UCB-ADVANTAGE and prove that it achieves \tilde{O}(\sqrt{H^2 SAT}) regret where T=KH and K is the number of episodes to play.

reinforcement-learning Reinforcement Learning +1

Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity

1 code implementation5 Nov 2020 Tanmay Gangwani, Jian Peng, Yuan Zhou

Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning.

Diversity Off-policy evaluation

Learning Guidance Rewards with Trajectory-space Smoothing

2 code implementations NeurIPS 2020 Tanmay Gangwani, Yuan Zhou, Jian Peng

To make credit assignment easier, recent works have proposed algorithms to learn dense "guidance" rewards that could be used in place of the sparse or delayed environmental rewards.

Attribute Q-Learning +1

Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection

no code implementations15 Oct 2020 Yuan Zhou, Xiangrui Li

Then two set of pseudo labels are used to jointly train a student network with the same structure as the teacher.

Change Detection Earth Observation

Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis

1 code implementation10 Oct 2020 Yicheng Luo, Antonio Filieri, Yuan Zhou

Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itself using probabilistic programming constructs.

Probabilistic Programming

Probabilistic Programs with Stochastic Conditioning

1 code implementation1 Oct 2020 David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang

We tackle the problem of conditioning probabilistic programs on distributions of observable variables.

Probabilistic Programming

Bayesian Policy Search for Stochastic Domains

no code implementations1 Oct 2020 David Tolpin, Yuan Zhou, Hongseok Yang

In this work, we cast policy search in stochastic domains as a Bayesian inference problem and provide a scheme for encoding such problems as nested probabilistic programs.

Probabilistic Programming Variational Inference

Near-Optimal MNL Bandits Under Risk Criteria

no code implementations26 Sep 2020 Guangyu Xi, Chao Tao, Yuan Zhou

We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria.

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

no code implementations29 Jul 2020 Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan

We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.

Graph Neural Network

Graph Neural Network for Video Relocalization

no code implementations20 Jul 2020 Yuan Zhou, Mingfei Wang, Ruolin Wang, Shuwei Huo

In this paper, we focus on video relocalization task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video.

Graph Neural Network Moment Retrieval

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 $O(N \log^2 T)$.

Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design

no code implementations4 Jul 2020 Yufei Ruan, Jiaqi Yang, Yuan Zhou

Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning.

Active Learning Multi-Armed Bandits

Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

no code implementations6 Jun 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP).

reinforcement-learning Reinforcement Learning (RL)

Adaptive Double-Exploration Tradeoff for Outlier Detection

no code implementations13 May 2020 Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, Yuan Zhou

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold.

Outlier Detection

Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

no code implementations21 Apr 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$.

reinforcement-learning Reinforcement Learning +1

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.

Reinforcement Learning

Stochastically Differentiable Probabilistic Programs

no code implementations2 Mar 2020 David Tolpin, Yuan Zhou, Hongseok Yang

Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs).

Probabilistic Programming

Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks

no code implementations22 Feb 2020 Yuan Zhou, Tao Cao, Wei Xiang

As a promising technology in the Internet of Underwater Things, underwater sensor networks have drawn a widespread attention from both academia and industry.

Q-Learning

Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement

no code implementations20 Feb 2020 Yuan Zhou, Kangming Yan

Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion.

Domain Adaptation Image Enhancement

Exploiting Operation Importance for Differentiable Neural Architecture Search

no code implementations24 Nov 2019 Xukai Xie, Yuan Zhou, Sun-Yuan Kung

All the existing methods determine the importance of each operation directly by architecture weights.

Neural Architecture Search

Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset

no code implementations21 Nov 2019 Bingyuan Liu, Jiantao Zhang, Xiaoting Zhang, Wei zhang, Chuanhui Yu, Yuan Zhou

However, few works focus on the understanding of furniture within the scenes and a large-scale dataset is also lacked to advance the field.

Retrieval

Temporal Action Localization using Long Short-Term Dependency

no code implementations4 Nov 2019 Yuan Zhou, Hongru Li, Sun-Yuan Kung

In the present study, we developed a novel method, referred to as Gemini Network, for effective modeling of temporal structures and achieving high-performance temporal action localization.

Temporal Action Localization

Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking

no code implementations4 Nov 2019 Yuan Zhou, Xiaoting Du, Yeda Zhang, Sun-Yuan Kung

To this end, we propose the cross-scale residual network to exploit scale-related features and the inter-task correlations among the three tasks.

Denoising Image Restoration +1

Comb Convolution for Efficient Convolutional Architecture

no code implementations1 Nov 2019 Dandan Li, Yuan Zhou, Shuwei Huo, Sun-Yuan Kung

Convolutional neural networks (CNNs) are inherently suffering from massively redundant computation (FLOPs) due to the dense connection pattern between feature maps and convolution kernels.

$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank

no code implementations5 Sep 2019 Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou

Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE.

Efficient Exploration Reinforcement Learning

Dual-reference Age Synthesis

no code implementations7 Aug 2019 Yuan Zhou, Bingzhang Hu, and Jun He, Yu Guan, Ling Shao

Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image.

Generative Adversarial Network

Exploration via Hindsight Goal Generation

1 code implementation NeurIPS 2019 Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng

Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space.

reinforcement-learning Reinforcement Learning +1

HGC: Hierarchical Group Convolution for Highly Efficient Neural Network

no code implementations9 Jun 2019 Xukai Xie, Yuan Zhou, Sun-Yuan Kung

Using this operation, feature maps of different group cannot communicate, which restricts their representation capability.

Efficient Neural Network

Thresholding Bandit with Optimal Aggregate Regret

no code implementations NeurIPS 2019 Chao Tao, Saùl Blanco, Jian Peng, Yuan Zhou

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials.

Tight Regret Bounds for Infinite-armed Linear Contextual Bandits

no code implementations4 May 2019 Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou

Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.

Decision Making Multi-Armed Bandits +1

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

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

1 code implementation6 Mar 2019 Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables.

Probabilistic Programming

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

no code implementations14 Dec 2018 Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan

Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.

Deep Learning Feature Importance

Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models

no code implementations NeurIPS 2018 Yining Wang, Xi Chen, Yuan Zhou

In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model.

On Exploration, Exploitation and Learning in Adaptive Importance Sampling

no code implementations31 Oct 2018 Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation.

Dynamic Assortment Optimization with Changing Contextual Information

no code implementations31 Oct 2018 Xi Chen, Yining Wang, Yuan Zhou

To this end, we develop an upper confidence bound (UCB) based policy and establish the regret bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of the feature and $\widetilde O$ suppresses logarithmic dependence.

Assortment Optimization Combinatorial Optimization

Best Arm Identification in Linear Bandits with Linear Dimension Dependency

no code implementations ICML 2018 Chao Tao, Saúl Blanco, Yuan Zhou

We study the best arm identification problem in linear bandits, where the mean reward of each arm depends linearly on an unknown $d$-dimensional parameter vector $\theta$, and the goal is to identify the arm with the largest expected reward.

Dynamic Assortment Selection under the Nested Logit Models

no code implementations27 Jun 2018 Xi Chen, Chao Shi, Yining Wang, Yuan Zhou

One key challenge is that utilities of products are unknown to the seller and need to be learned.

Management

Inference Trees: Adaptive Inference with Exploration

no code implementations25 Jun 2018 Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent

We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods.

Channel Gating Neural Networks

1 code implementation NeurIPS 2019 Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh

Combining our method with knowledge distillation reduces the compute cost of ResNet-18 by 2. 6$\times$ without accuracy drop on ImageNet.

Knowledge Distillation Network Pruning

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

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities

1 code implementation7 Apr 2018 Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood

Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).

Probabilistic Programming

Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability

no code implementations25 Jan 2018 Yuan Zhou, Erin B. Wetherley, Paul D. Gader

Spectral libraries were built by manually identifying and extracting pure spectra from both resolution images, resulting in 3, 287 spectra at 16 m and 15, 426 spectra at 4 m. We then unmixed ROIs of each resolution using the following unmixing algorithms: the set-based algorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, and BCM.

A Gaussian mixture model representation of endmember variability in hyperspectral unmixing

1 code implementation29 Sep 2017 Yuan Zhou, Anand Rangarajan, Paul D. Gader

We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives).

Hyperspectral Unmixing

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.

A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation

no code implementations30 Sep 2015 Yuan Zhou, Anand Rangarajan, Paul Gader

In this paper, we show that NCM can be used for calculating the uncertainty of the estimated endmembers with spatial priors incorporated for better unmixing.

Hyperspectral Unmixing

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