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no code implementations • 30 May 2023 • Chenyi Liu, Vaneet Aggarwal, Tian Lan, Nan Geng, Yuan Yang, Mingwei Xu, Qing Li

By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.

no code implementations • 27 May 2023 • Jiayu Chen, Tian Lan, Vaneet Aggarwal

A notable advantage of HDCFR over previous research in this field is its ability to facilitate learning with predefined (human) expertise and foster the acquisition of transferable skills that can be applied to similar tasks.

no code implementations • 26 May 2023 • Mohammad Pedramfar, Christopher John Quinn, Vaneet Aggarwal

This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types.

1 code implementation • 22 May 2023 • Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots.

no code implementations • 4 May 2023 • Washim Uddin Mondal, Vaneet Aggarwal

We investigate an infinite-horizon average reward Markov Decision Process (MDP) with delayed, composite, and partially anonymous reward feedback.

no code implementations • 23 Mar 2023 • Mohammad Pedramfar, Vaneet Aggarwal

This paper investigates the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed feedback is assumed to be composite and anonymous.

no code implementations • 15 Mar 2023 • Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Weifeng Su, Mung Chiang

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.

no code implementations • 16 Feb 2023 • Bhargav Ganguly, Yulian Wu, Di Wang, Vaneet Aggarwal

This improvement is a key to the significant regret improvement in quantum reinforcement learning.

no code implementations • 13 Feb 2023 • Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini

Federated learning is an emerging machine learning paradigm that enables clients to train collaboratively without exchanging local data.

no code implementations • 2 Feb 2023 • Fares Fourati, Vaneet Aggarwal, Christopher John Quinn, Mohamed-Slim Alouini

We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization.

no code implementations • 30 Jan 2023 • Guanyu Nie, Yididiya Y Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn

The framework only requires the offline algorithms to be robust to small errors in function evaluation.

no code implementations • 23 Jan 2023 • Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang

In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle.

1 code implementation • 13 Jan 2023 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

We compute the approximation error as $\mathcal{O}(e)$ where $e=\frac{1}{\sqrt{N}}\left[\sqrt{|\mathcal{X}|} +\sqrt{|\mathcal{U}|}\right]$.

1 code implementation • 1 Dec 2022 • Jiayu Chen, Vaneet Aggarwal, Tian Lan

Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research.

no code implementations • 22 Nov 2022 • Bhargav Ganguly, Vaneet Aggarwal

Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research.

no code implementations • 14 Nov 2022 • Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal

Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood.

no code implementations • 7 Oct 2022 • Jiayu Chen, Marina Haliem, Tian Lan, Vaneet Aggarwal

In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

1 code implementation • 5 Oct 2022 • Jiayu Chen, Tian Lan, Vaneet Aggarwal

In this work, we develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning and adapt it with the Expectation-Maximization algorithm in order to directly recover a hierarchical policy from the unannotated demonstrations.

no code implementations • 15 Sep 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

In a special case where the reward, cost, and state transition functions are independent of the action distribution of the population, we prove that the error can be improved to $e=\mathcal{O}(\sqrt{|\mathcal{X}|}/\sqrt{N})$.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 7 Sep 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

We show that in a cooperative $N$-agent network, one can design locally executable policies for the agents such that the resulting discounted sum of average rewards (value) well approximates the optimal value computed over all (including non-local) policies.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

1 code implementation • 18 Jul 2022 • Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal

We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme.

1 code implementation • 22 Jun 2022 • Hanhan Zhou, Tian Lan, Vaneet Aggarwal

Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods.

Multi-agent Reinforcement Learning
reinforcement-learning
**+4**

1 code implementation • 17 Jun 2022 • Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal

Newton-type methods are popular in federated learning due to their fast convergence.

no code implementations • 12 Jun 2022 • Qinbo Bai, Amrit Singh Bedi, Vaneet Aggarwal

We propose a novel Conservative Natural Policy Gradient Primal-Dual Algorithm (C-NPG-PD) to achieve zero constraint violation while achieving state of the art convergence results for the objective value function.

no code implementations • 26 Mar 2022 • Bhargav Ganguly, Seyyedali Hosseinalipour, Kwang Taik Kim, Christopher G. Brinton, Vaneet Aggarwal, David J. Love, Mung Chiang

CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.

1 code implementation • 28 Feb 2022 • Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

We prove that, if the reward of each agent is an affine function of the mean-field seen by that agent, then one can approximate such a non-uniform MARL problem via its associated MFC problem within an error of $e=\mathcal{O}(\frac{1}{\sqrt{N}}[\sqrt{|\mathcal{X}|} + \sqrt{|\mathcal{U}|}])$ where $N$ is the population size and $|\mathcal{X}|$, $|\mathcal{U}|$ are the sizes of state and action spaces respectively.

2 code implementations • 13 Feb 2022 • Washim Uddin Mondal, Praful D. Mankar, Goutam Das, Vaneet Aggarwal, Satish V. Ukkusuri

This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology.

no code implementations • 7 Feb 2022 • Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Mung Chiang

PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.

no code implementations • 21 Jan 2022 • Dheeraj Peddireddy, Vipul Bansal, Vaneet Aggarwal

This manuscript proposes an algorithm that compresses the quantum state within a circuit using a tensor ring representation which allows for the implementation of VQC based algorithms on a classical simulator at a fraction of the usual storage and computational complexity.

no code implementations • 20 Jan 2022 • Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph.

1 code implementation • 4 Jan 2022 • Hanhan Zhou, Tian Lan, Vaneet Aggarwal

To this end, we present LSF-SAC, a novel framework that features a variational inference-based information-sharing mechanism as extra state information to assist individual agents in the value function factorization.

no code implementations • 29 Oct 2021 • Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob

In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case).

no code implementations • 22 Oct 2021 • Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal

We establish that the sample complexity to obtain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with respect to the network in accordance with multi-agent optimization.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

no code implementations • 13 Sep 2021 • Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal

To achieve that, we advocate the use of randomized primal-dual approach to solve the CMDP problems and propose a conservative stochastic primal-dual algorithm (CSPDA) which is shown to exhibit $\tilde{\mathcal{O}}\left(1/\epsilon^2\right)$ sample complexity to achieve $\epsilon$-optimal cumulative reward with zero constraint violations.

no code implementations • 12 Sep 2021 • Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal

We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints.

no code implementations • 9 Sep 2021 • Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, Satish V. Ukkusuri

We show that, in these cases, the $K$-class MARL problem can be approximated by MFC with errors given as $e_1=\mathcal{O}(\frac{\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}}{N_{\mathrm{pop}}}\sum_{k}\sqrt{N_k})$, $e_2=\mathcal{O}(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\sum_{k}\frac{1}{\sqrt{N_k}})$ and $e_3=\mathcal{O}\left(\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]\left[\frac{A}{N_{\mathrm{pop}}}\sum_{k\in[K]}\sqrt{N_k}+\frac{B}{\sqrt{N_{\mathrm{pop}}}}\right]\right)$, respectively, where $A, B$ are some constants and $|\mathcal{X}|,|\mathcal{U}|$ are the sizes of state and action spaces of each agent.

1 code implementation • 9 Sep 2021 • Xinwu Qian, Shuocheng Guo, Vaneet Aggarwal

This study proposes the deep relocating option policy (DROP) that supervises vehicle agents to escape from oversupply areas and effectively relocate to potentially underserved areas.

1 code implementation • 30 Aug 2021 • Xingyu Fu, Fengfeng Zhou, Dheeraj Peddireddy, Zhengyang Kang, Martin Byung-Guk Jun, Vaneet Aggarwal

In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems.

no code implementations • 12 Jun 2021 • Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal

We consider the problem of constrained Markov Decision Process (CMDP) where an agent interacts with a unichain Markov Decision Process.

no code implementations • 31 May 2021 • Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal

In this paper, we propose an energy-efficient federated meta-learning framework.

no code implementations • 28 May 2021 • Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal

Many engineering problems have multiple objectives, and the overall aim is to optimize a non-linear function of these objectives.

Multi-Objective Reinforcement Learning reinforcement-learning

no code implementations • 24 Apr 2021 • Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob

We also demonstrate that the proposed approach outperforms the results of classical causal inference for the Tubingen database when the variables are classical by exploiting quantum dependence between variables through density matrices rather than joint probability distributions.

no code implementations • 1 Apr 2021 • Marina Haliem, Vaneet Aggarwal, Bharat Bhargava

To mitigate this problem in highly dynamic environments, we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to detect the changes in the distribution of experiences, (2) develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment.

1 code implementation • 5 Mar 2021 • Jiayu Chen, Abhishek K. Umrawal, Tian Lan, Vaneet Aggarwal

Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 1 Mar 2021 • Trevor Bonjour, Marina Haliem, Aala Alsalem, Shilpa Thomas, Hongyu Li, Vaneet Aggarwal, Mayank Kejriwal, Bharat Bhargava

Monopoly is a popular strategic board game that requires players to make multiple decisions during the game.

no code implementations • 23 Feb 2021 • Mohammad Ali Javidian, Vaneet Aggarwal, Fanglin Bao, Zubin Jacob

This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multi-node quantum networks as well as quantum error correction.

no code implementations • 22 Feb 2021 • Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal

We provide \NAM\ which runs at each agent and prove that the total cumulative regret of $M$ agents is upper bounded as $\Tilde{O}(DS\sqrt{MAT})$ for a Markov Decision Process with diameter $D$, number of states $S$, and number of actions $A$.

no code implementations • 10 Feb 2021 • Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli

With our algorithm, LCC-UCB, each agent enjoys a regret of $\tilde{O}\left(\sqrt{({K/N}+ N)T}\right)$, communicates for $O(\log T)$ steps and broadcasts $O(\log K)$ bits in each communication step.

no code implementations • 30 Jan 2021 • Mayank Gupta, Lingjun Chen, Denny Yu, Vaneet Aggarwal

Non-contact methods can have additional advantages since they are scalable with any environment where video can be captured, can be used for continuous measurements, and can be used on patients with varying levels of dexterity and independence, from people with physical impairments to infants (e. g., baby camera).

no code implementations • 31 Dec 2020 • Arnob Ghosh, Vaneet Aggarwal

We consider a multi-agent Markov strategic interaction over an infinite horizon where agents can be of multiple types.

no code implementations • 7 Dec 2020 • Souvik Das, Anirudh Shankar, Vaneet Aggarwal

Rules of the framework select certain bots and their SNNs for reproduction and others for elimination based on their efficacy in capturing food in a competitive environment.

no code implementations • 17 Nov 2020 • Kaushik Manchella, Marina Haliem, Vaneet Aggarwal, Bharat Bhargava

The ubiquitous growth of mobility-on-demand services for passenger and goods delivery has brought various challenges and opportunities within the realm of transportation systems.

no code implementations • 16 Nov 2020 • Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn, Abhishek Umrawal

Additionally, our algorithm works on correlated rewards of individual arms.

no code implementations • 16 Nov 2020 • Mridul Agarwal, Vaneet Aggarwal

This paper proposes an approach, where the delay in the knowledge of the state can be used, and the decisions are made based on the available information which may not include the current state information.

no code implementations • 12 Nov 2020 • Mounssif Krouka, Anis Elgabli, Mohammed S. Elbamby, Cristina Perfecto, Mehdi Bennis, Vaneet Aggarwal

Wirelessly streaming high quality 360 degree videos is still a challenging problem.

no code implementations • 5 Oct 2020 • Marina Haliem, Ganapathy Mani, Vaneet Aggarwal, Bharat Bhargava

In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations.

no code implementations • 27 Sep 2020 • Ramkumar Raghu, Mahadesh Panju, Vaneet Aggarwal, Vinod Sharma

In this paper, we use deep reinforcement learning where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network.

no code implementations • 27 Jul 2020 • Kaushik Manchella, Abhishek K. Umrawal, Vaneet Aggarwal

Through simulations on a realistic multi-agent urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers & goods.

1 code implementation • 18 Jul 2020 • Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet Aggarwal, David J. Love, Huaiyu Dai

We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).

no code implementations • 10 Jun 2020 • Qinbo Bai, Vaneet Aggarwal, Ather Gattami

This paper uses concepts from constrained optimization and Q-learning to propose an algorithm for CMDP with long-term constraints.

no code implementations • L4DC 2020 • Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel

Experimentally, we observe state of the art accuracy and complexity tradeoffs for GP bandit algorithms on various hyper-parameter tuning tasks, suggesting the merits of managing the complexity of GPs in bandit settings

no code implementations • 7 Jun 2020 • Seyyedali Hosseinalipour, Christopher G. Brinton, Vaneet Aggarwal, Huaiyu Dai, Mung Chiang

There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.

no code implementations • 23 Mar 2020 • Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler, Alec Koppel

Doing so permits us to precisely characterize the trade-off between regret bounds of GP bandit algorithms and complexity of the posterior distributions depending on the compression parameter $\epsilon$ for both discrete and continuous action sets.

no code implementations • 11 Mar 2020 • Qinbo Bai, Vaneet Aggarwal, Ather Gattami

The proposed algorithm is proved to achieve an $(\epsilon, p)$-PAC policy when the episode $K\geq\Omega(\frac{I^2H^6SA\ell}{\epsilon^2})$, where $S$ and $A$ are the number of states and actions, respectively.

no code implementations • 30 Oct 2019 • Ashutosh Singh, Abubakr Alabbasi, Vaneet Aggarwal

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services.

no code implementations • 23 Oct 2019 • Anis Elgabli, Jihong Park, Amrit S. Bedi, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal

In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).

no code implementations • 3 Oct 2019 • Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal

Gradient descent and its variants are widely used in machine learning.

no code implementations • 27 Sep 2019 • Ramkumar Raghu, Pratheek Upadhyaya, Mahadesh Panju, Vaneet Aggarwal, Vinod Sharma

However for this system, obtaining optimal power control is intractable because of a very large state space.

no code implementations • 6 Sep 2019 • Sathwik Chadaga, Mridul Agarwal, Vaneet Aggarwal

However, large-scale design of quantum encoders and decoders have to depend on the channel characteristics and require look-up tables which require memory that is exponential in the number of qubits.

no code implementations • 6 Sep 2019 • Mridul Agarwal, Vaneet Aggarwal

Finding optimal policies which maximize long term rewards of Markov Decision Processes requires the use of dynamic programming and backward induction to solve the Bellman optimality equation.

no code implementations • 30 Aug 2019 • Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.

no code implementations • 30 May 2019 • Mridul Agarwal, Vaneet Aggarwal, Arnob Ghosh, Nilay Tiwari

This paper focuses on finding a mean-field equilibrium (MFE) in an action coupled stochastic game setting in an episodic framework.

no code implementations • 9 Mar 2019 • Abubakr Alabbasi, Arnob Ghosh, Vaneet Aggarwal

The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed.

1 code implementation • 2 Mar 2019 • Anis Elgabli, Ali Elghariani, Vaneet Aggarwal, *Mehdi Bennis, Mark R. Bell

We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors.

Information Theory Information Theory

no code implementations • 29 Nov 2018 • Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn, Abhishek K. Umrawal

Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $K$ out of $N$ options at a given time instant.

no code implementations • 25 Sep 2018 • Vaneet Aggarwal, Hamed Asadi, Mayank Gupta, Jae Joong Lee, Denny Yu

We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos.

1 code implementation • 7 Jun 2018 • Anis Elgabli, Vaneet Aggarwal

For example, on an experiment conducted over 100 real cellular bandwidth traces of a public dataset that spans different bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to the original dash. js rate adaptation scheme, Festive, BBA, RB, and FastMPC algorithms.

Networking and Internet Architecture Multimedia

no code implementations • 30 Apr 2018 • Anis Elgabli, Vaneet Aggarwal, Shuai Hao, Feng Qian, Subhabrata Sen

The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches.

Networking and Internet Architecture Multimedia

no code implementations • 13 Mar 2018 • Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Tensor train is a hierarchical tensor network structure that helps alleviate the curse of dimensionality by parameterizing large-scale multidimensional data via a set of network of low-rank tensors.

no code implementations • CVPR 2018 • Wenqi Wang, Yifan Sun, Brian Eriksson, Wenlin Wang, Vaneet Aggarwal

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.

no code implementations • 5 Dec 2017 • Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang

In this letter, we study the deterministic sampling patterns for the completion of low rank matrix, when corrupted with a sparse noise, also known as robust matrix completion.

no code implementations • 3 Dec 2017 • Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace.

no code implementations • ICCV 2017 • Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation.

no code implementations • 3 Jul 2017 • Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal

Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given.

no code implementations • 3 Jan 2017 • Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal

We provide a deterministic necessary and sufficient condition on the sampling pattern for finite completability.

no code implementations • 6 Dec 2016 • Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang

We investigate the fundamental conditions on the sampling pattern, i. e., locations of the sampled entries, for finite completability of a low-rank tensor given some components of its Tucker rank.

no code implementations • 15 Oct 2016 • Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Similar to the Union of Subspaces (UOS) model where each data from each subspace is generated from a (unknown) basis, in the UOPC model each data from each cone is assumed to be generated from a finite number of (unknown) \emph{extreme rays}. To cluster data under this model, we consider several algorithms - (a) Sparse Subspace Clustering by Non-negative constraints Lasso (NCL), (b) Least squares approximation (LSA), and (c) K-nearest neighbor (KNN) algorithm to arrive at affinity between data points.

no code implementations • 5 Oct 2016 • Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang

The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data.

no code implementations • 19 Sep 2016 • Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron

Using the matrix product state (MPS) representation of tensor train decompositions, in this paper we propose a tensor completion algorithm which alternates over the matrices (tensors) in the MPS representation.

no code implementations • 11 Jul 2016 • Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal

In this paper we present deterministic conditions for success of sparse subspace clustering (SSC) under missing data, when data is assumed to come from a Union of Subspaces (UoS) model.

no code implementations • 15 Apr 2016 • Wenqi Wang, Shuchin Aeron, Vaneet Aggarwal

We provide extensive set of simulation results for clustering as well as completion of data under missing entries, under the UoS model.

no code implementations • 14 Aug 2015 • Vaneet Aggarwal, Shuchin Aeron

In this short note we extend some of the recent results on matrix completion under the assumption that the columns of the matrix can be grouped (clustered) into subspaces (not necessarily disjoint or independent).

no code implementations • 10 Aug 2015 • Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu

In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy.

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