Search Results for author:
Found 121 papers, 37 papers with code
LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform
The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN).
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
First, we train a degradation-aware prompt extractor, which can generate accurate soft and hard semantic prompts even under strong degradation.
Adversarial Attacks on Cooperative Multi-agent Bandits
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game.
Pick Planning Strategies for Large-Scale Package Manipulation
Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations.
Spatial-Temporal Transformer based Video Compression Framework
Similar as the traditional video coding, LVC inherits motion estimation/compensation, residual coding and other modules, all of which are implemented with neural networks (NNs).
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices
Extensive experiments demonstrate that our approach decreases runtime by up to 13% and reduces the number of parameters by up to 23%, while increasing PSNR and SSIM on several image restoration datasets.
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL).
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation
In this paper, we propose a structural causal model-based method to address the popularity bias issue for sequential recommendation model learning.
Player-optimal Stable Regret for Bandit Learning in Matching Markets
Most previous works in this line are only able to derive theoretical guarantees for player-pessimal stable regret, which is defined compared with the players' least-preferred stable matching.
Future-conditioned Unsupervised Pretraining for Decision Transformer
While promising, return conditioning is limited to training data labeled with rewards and therefore faces challenges in learning from unsupervised data.
Online Influence Maximization under Decreasing Cascade Model
The effect is neglected by previous OIM works under IC and linear threshold models.
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success
This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which utilizes a pick success predictor trained on real production data.
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related tasks.
Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera
Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps.
Nearest-Neighbor Sampling Based Conditional Independence Testing
The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data.
MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos
The proposed MDQE is the first VIS method with per-clip input that achieves state-of-the-art results on challenging videos and competitive performance on simple videos.
One-to-Few Label Assignment for End-to-End Dense Detection
The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to ``representation learning'' in the early training stage, and contribute more to ``duplicated prediction removal'' in the later stage.
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame.
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention.
DynaMask: Dynamic Mask Selection for Instance Segmentation
The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e. g., 28*28 grid.
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm
Follow-the-regularized-leader (FTRL) is another type of popular algorithm that can adapt to different environments.
Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits
To tackle these issues, we first propose ``ConLinUCB", a general framework for conversational bandits with better information incorporation, combining arm-level and key-term-level feedback to estimate user preference in one step at each time.
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem.
FSI: Frequency and Spatial Interactive Learning for Image Restoration in Under-Display Cameras
In this paper, we introduce a new perspective to handle various diffraction in UDC images by jointly exploring the feature restoration in the frequency and spatial domains, and present a Frequency and Spatial Interactive Learning Network (FSI).
Sequential Texts Driven Cohesive Motions Synthesis with Natural Transitions
At the technical level, we explore the local-to-global semantic features of previous and current texts to extract relevant information.
FPR: False Positive Rectification for Weakly Supervised Semantic Segmentation
Without introducing any external supervision and human priors, the proposed FPR effectively suppresses wrong activations from the background objects.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Pretraining in Deep Reinforcement Learning: A Survey
The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning.
Salient Object Detection via Dynamic Scale Routing
First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w. r. t.
Hierarchical Conversational Preference Elicitation with Bandit Feedback
To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories.
Federated Online Clustering of Bandits
Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems.
Comparison-based Conversational Recommender System with Relative Bandit Feedback
To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system.
Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
A novel meta-heuristic algorithm, Egret Swarm Optimization Algorithm (ESOA), is proposed in this paper, which is inspired by two egret species' (Great Egret and Snowy Egret) hunting behavior.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction
Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions.
Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model
This work studies the OLTR problem in both stochastic and adversarial environments under the position-based model (PBM).
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback
With a general feedback graph, the observation of an arm may not be available when this arm is pulled, which makes the exploration more expensive and the algorithms more challenging to perform optimally in both environments.
Thompson Sampling for Bandit Learning in Matching Markets
The problem of two-sided matching markets has a wide range of real-world applications and has been extensively studied in the literature.
Learning of Global Objective for Network Flow in Multi-Object Tracking
This paper concerns the problem of multi-object tracking based on the min-cost flow (MCF) formulation, which is conventionally studied as an instance of linear program.
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds
VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training.
Ranked #9 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
A Dual Weighting Label Assignment Scheme for Object Detection
Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight.
Towards Robust 2D Convolution for Reliable Visual Recognition
2D convolution (Conv2d), which is responsible for extracting features from the input image, is one of the key modules of a convolutional neural network (CNN).
A density peaks clustering algorithm with sparse search and K-d tree
It is proved that the algorithm can effectively reduce the computational complexity of the original DPC from $O(n^2K)$ to $O(n(n^{1-1/K}+k))$.
Pursuit-evasion differential games of players with different speeds in spaces of different dimensions
We study pursuit-evasion differential games between a faster pursuer moving in 3D space and an evader moving in a plane.
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning.
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Unlike conventional methods that learn knowledge embedding and regular patterns from encoded visual information, we propose to suppress the misunderstandings caused by appearance similarities and other perceptual confusion.
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle
These are the first theoretical results for TS to solve CMAB with a common approximation oracle and break the misconception that TS cannot work with approximation oracles.
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes
Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval.
Incentivizing an Unknown Crowd
Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a heterogeneous and unknown crowd.
Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography
The lensless pinhole camera is perhaps the earliest and simplest form of an imaging system using only a pinhole-sized aperture in place of a lens.
A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding
To the best of our knowledge, we are the first one that clearly characterizes the video filtering process from the above global appearance and local coding distortion restoration aspects with experimental verification, providing a clear pathway to developing filter techniques.
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions
Our algorithm not only achieves near-optimal regret in the stochastic setting, but also obtains a regret with an additive term of corruption in the corrupted setting, while maintaining efficient communication.
On Learning to Rank Long Sequences with Contextual Bandits
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses.
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Especially, state-of-the-art SSL methods significantly outperform existing UDA methods on the challenging UDA benchmark of DomainNet, and state-of-the-art UDA methods could be further enhanced with SSL techniques.
Understanding Bandits with Graph Feedback
We propose the notions of the fractional weak domination number $\delta^*$ and the $k$-packing independence number capturing upper bound and lower bound for the regret respectively.
Cascading Bandit under Differential Privacy
This paper studies \emph{differential privacy (DP)} and \emph{local differential privacy (LDP)} in cascading bandits.
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network
Recent development of Under-Display Camera (UDC) systems provides a true bezel-less and notch-free viewing experience on smartphones (and TV, laptops, tablets), while allowing images to be captured from the selfie camera embedded underneath.
Conservative Contextual Combinatorial Cascading Bandit
We propose the novel \emph{conservative contextual combinatorial cascading bandit ($C^4$-bandit)}, a cascading online learning game which incorporates the conservative mechanism.
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our framework to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses.
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances.
Combinatorial Bandits under Strategic Manipulations
Strategic behavior against sequential learning methods, such as "click framing" in real recommendation systems, have been widely observed.
A Two-stream Neural Network for Pose-based Hand Gesture Recognition
The self-attention based graph convolutional network has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCN.
A robust and generalizable immune-relatedsignature for sepsis diagnostics
High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death.
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
However, it is observed that such methods would lead to standard performance degradation, i. e., the degradation on natural examples.
Online Influence Maximization under Linear Threshold Model
Based on the linear structure in node activations, we incorporate ideas from linear bandits and design an algorithm LT-LinUCB that is consistent with the observed feedback.
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones.
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.
Deforming the Loss Surface to Affect the Behaviour of the Optimizer
With visualizations of loss landscapes, we evaluate the flatnesses of minima obtained by both the original optimizer and optimizers enhanced by VDMs on CIFAR-100.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.
A Plug-and-play Scheme to Adapt Image Saliency Deep Model for Video Data
With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.
Deforming the Loss Surface
Furthermore, the flatter minima could be obtained by exploiting the proposed deformation functions, which is verified on CIFAR-100, with visualizations of loss landscapes near the critical points obtained by both the original optimizer and optimizer enhanced by deformation functions.
Fast Distributed Bandits for Online Recommendation Systems
This algorithm lazily creates clusters in a distributed manner, and dramatically reduces the network data sharing requirement, achieving high scalability.
Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator.
Learning Various Length Dependence by Dual Recurrent Neural Networks
Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences.
On the interplay between physical and content priors in deep learning for computational imaging
That is, the higher the entropy of the training images, the weaker the regularization effect can be imposed.
A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images
Experimental results achieve competitive performance in detection with the AUC of 0. 9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.
Fractional order graph neural network
This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs.
The Gambler's Problem and Beyond
We analyze the Gambler's problem, a simple reinforcement learning problem where the gambler has the chance to double or lose the bets until the target is reached.
Contextual Combinatorial Conservative Bandits
The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios.
Deep Independently Recurrent Neural Network (IndRNN)
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks.
Dynamic Anchor Feature Selection for Single-Shot Object Detection
In this paper, we present a dynamic feature selection operation to select new pixels in a feature map for each refined anchor received from the ARM.
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase.
Orthogonal Deep Neural Networks
To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices.
Convergence analysis of beetle antennae search algorithm and its applications
The beetle antennae search algorithm was recently proposed and investigated for solving global optimization problems.
Stochastic Online Learning with Probabilistic Graph Feedback
We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$.
Improved Algorithm on Online Clustering of Bandits
We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies.
Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks
We present a formal measure-theoretical theory of neural networks (NN) built on probability coupling theory.
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery
Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands.
Online Learning to Rank with Features
We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter.
S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks
We present a formal measure-theoretical theory of neural networks (NN) built on {\it probability coupling theory}.
Gear Training: A new way to implement high-performance model-parallel training
The training of Deep Neural Networks usually needs tremendous computing resources.
TopRank: A practical algorithm for online stochastic ranking
Online learning to rank is a sequential decision-making problem where in each round the learning agent chooses a list of items and receives feedback in the form of clicks from the user.
Offline Evaluation of Ranking Policies with Click Models
We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds.
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.
Ranked #10 on
Language Modelling
on Penn Treebank (Character Level)
Hole Filling with Multiple Reference Views in DIBR View Synthesis
In this paper, we first examine the view interpolation with multiple reference views, demonstrating that the problem of emerging holes in a target virtual view can be greatly alleviated by making good use of other neighboring complementary views in addition to its two (commonly used) most neighboring primary views.
Online Clustering of Contextual Cascading Bandits
We consider a new setting of online clustering of contextual cascading bandits, an online learning problem where the underlying cluster structure over users is unknown and needs to be learned from a random prefix feedback.
Beetle Antennae Search without Parameter Tuning (BAS-WPT) for Multi-objective Optimization
Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles.
BAS: Beetle Antennae Search Algorithm for Optimization Problems
Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem.
Porcellio scaber algorithm (PSA) for solving constrained optimization problems
In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem.
PSA: A novel optimization algorithm based on survival rules of porcellio scaber
Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies.
Foreground Detection in Camouflaged Scenes
This paper proposes a texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes.
A Fully Trainable Network with RNN-based Pooling
Such a network with learnable pooling function is referred to as a fully trainable network (FTN).
Deep LSTM for Large Vocabulary Continuous Speech Recognition
It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach.
Lensless computational imaging through deep learning
Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks.
Akid: A Library for Neural Network Research and Production from a Dataism Approach
The distributed computing stack handles the concurrency and communication, thus letting models be trained or deployed to a single GPU, multiple GPUs, or a distributed environment without affecting how a model is specified in the programming paradigm stack.
Automatic Discoveries of Physical and Semantic Concepts via Association Priors of Neuron Groups
The recent successful deep neural networks are largely trained in a supervised manner.
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.
Graph Clustering Bandits for Recommendation
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users.
Distributed Clustering of Linear Bandits in Peer to Peer Networks
We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities.
Context-Aware Bandits
We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects.
Collaborative Filtering Bandits
Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users.
Online Clustering of Bandits
We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies.
