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ORCIDVaneet Aggarwal
Person information
- affiliation: Purdue University, West Lafayette, IN, USA
- affiliation: King Abdulaziz University, Jeddah, Saudi Arabia
- affiliation (2018 - 2019): IISc, Bangalore, India
- affiliation (2013 - 2014): Columbia University, New York, NY, USA
- affiliation (2010 - 2014): AT&T Labs-Research, Florham Park, NJ, USA
- affiliation (PhD 2010): Princeton University, NJ, USA
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2020 – today
- 2024
[j156]Vaneet Aggarwal
, Rakhi Pratihar:
Insdel codes from subspace and rank-metric codes. Discret. Math. 347(1): 113675 (2024)- [j155]Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai:
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms. Found. Trends Optim. 6(4): 193-298 (2024) - [j154]Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di Wang, Mohamed-Slim Alouini, Vaneet Aggarwal:
Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN. IEEE Netw. Lett. 6(2): 97-100 (2024) - [j153]Divija Swetha Gadiraju, Prasenjit Karmakar, Vijay K. Shah, Vaneet Aggarwal:
GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments. Inf. 15(8): 477 (2024) - [j152]Ruibo Wang, Washim Uddin Mondal, Mustafa A. Kishk, Vaneet Aggarwal, Mohamed-Slim Alouini:
Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation? IEEE Open J. Commun. Soc. 5: 633-648 (2024) - [j151]Ciyuan Zhang, Su Wang, Vaneet Aggarwal, Borja Peleato:
Coded Caching With Heterogeneous User Profiles. IEEE Trans. Inf. Theory 70(3): 1836-1847 (2024) - [j150]Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal:
Reinforced Sequential Decision-Making for Sepsis Treatment: The PosNegDM Framework With Mortality Classifier and Transformer. IEEE J. Biomed. Health Informatics 28(5): 3114-3122 (2024) - [j149]Seyyedali Hosseinalipour, Su Wang, Nicolò Michelusi, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Mung Chiang:
Parallel Successive Learning for Dynamic Distributed Model Training Over Heterogeneous Wireless Networks. IEEE/ACM Trans. Netw. 32(1): 222-237 (2024) - [j148]Bhargav Ganguly, Vaneet Aggarwal:
Online Federated Learning via Non-Stationary Detection and Adaptation Amidst Concept Drift. IEEE/ACM Trans. Netw. 32(1): 643-653 (2024) - [j147]Chenyi Liu, Vaneet Aggarwal, Tian Lan, Nan Geng, Yuan Yang, Mingwei Xu, Qing Li:
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design. IEEE/ACM Trans. Netw. 32(2): 1003-1018 (2024) - [j146]Chang-Lin Chen, Bharat K. Bhargava, Vaneet Aggarwal, Basavaraj Tonshal, Amrit Gopal:
A Hybrid Deep Reinforcement Learning Approach for Jointly Optimizing Offloading and Resource Management in Vehicular Networks. IEEE Trans. Veh. Technol. 73(2): 2456-2467 (2024) - [c141]Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal:
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes. AAAI 2024: 10980-10988 - [c140]Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini, Vaneet Aggarwal:
Combinatorial Stochastic-Greedy Bandit. AAAI 2024: 12052-12060 - [c139]Washim Uddin Mondal, Vaneet Aggarwal:
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes. AISTATS 2024: 3097-3105 - [c138]Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini:
FilFL: Client Filtering for Optimized Client Participation in Federated Learning. ECAI 2024: 2460-2467 - [c137]Mohammad Pedramfar, Yididiya Y. Nadew, Christopher John Quinn, Vaneet Aggarwal:
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization. ICLR 2024 - [c136]Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang:
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. ICLR 2024 - [c135]Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal:
Federated Combinatorial Multi-Agent Multi-Armed Bandits. ICML 2024 - [c134]Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini:
Stochastic Q-learning for Large Discrete Action Spaces. ICML 2024 - [c133]Mudit Gaur, Amrit S. Bedi, Di Wang, Vaneet Aggarwal:
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization. ICML 2024 - [c132]Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Dinesh Manocha, Amrit S. Bedi:
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles. ICML 2024 - [i199]Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal:
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm. CoRR abs/2402.02042 (2024) - [i198]Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di Wang, Mohamed-Slim Alouini, Vaneet Aggarwal:
Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN. CoRR abs/2402.06901 (2024) - [i197]Mohammad Pedramfar, Vaneet Aggarwal:
A Generalized Approach to Online Convex Optimization. CoRR abs/2402.08621 (2024) - [i196]Aditya Malusare, Vaneet Aggarwal:
Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model. CoRR abs/2402.08790 (2024) - [i195]Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei, Tian Lan, Vaneet Aggarwal:
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions. CoRR abs/2402.13777 (2024) - [i194]Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal:
Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer. CoRR abs/2403.07309 (2024) - [i193]Swetha Ganesh, Jiayu Chen, Gugan Thoppe, Vaneet Aggarwal:
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries. CoRR abs/2403.09940 (2024) - [i192]Mohammad Pedramfar, Yididiya Y. Nadew, Christopher J. Quinn, Vaneet Aggarwal:
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization. CoRR abs/2403.10063 (2024) - [i191]Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Amrit Singh Bedi, Dinesh Manocha:
Global Optimality without Mixing Time Oracles in Average-reward RL via Multi-level Actor-Critic. CoRR abs/2403.11925 (2024) - [i190]Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal:
Variance-Reduced Policy Gradient Approaches for Infinite Horizon Average Reward Markov Decision Processes. CoRR abs/2404.02108 (2024) - [i189]Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal, Christopher G. Brinton:
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis. CoRR abs/2404.08003 (2024) - [i188]Debanjan Konar, Zain Hafeez, Vaneet Aggarwal:
A Bi-directional Quantum Search Algorithm. CoRR abs/2404.15616 (2024) - [i187]Mohammad Pedramfar, Vaneet Aggarwal:
From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization. CoRR abs/2405.00065 (2024) - [i186]Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal:
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization. CoRR abs/2405.01843 (2024) - [i185]Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal:
Federated Combinatorial Multi-Agent Multi-Armed Bandits. CoRR abs/2405.05950 (2024) - [i184]Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini:
Stochastic Q-learning for Large Discrete Action Spaces. CoRR abs/2405.10310 (2024) - [i183]Washim Uddin Mondal, Vaneet Aggarwal:
Sample-Efficient Constrained Reinforcement Learning with General Parameterization. CoRR abs/2405.10624 (2024) - [i182]Jiayu Chen, Bhargav Ganguly, Tian Lan, Vaneet Aggarwal:
Variational Offline Multi-agent Skill Discovery. CoRR abs/2405.16386 (2024) - [i181]Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai:
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms. CoRR abs/2406.11481 (2024) - [i180]Swetha Ganesh, Vaneet Aggarwal:
An Accelerated Multi-level Monte Carlo Approach for Average Reward Reinforcement Learning with General Policy Parametrization. CoRR abs/2407.18878 (2024) - [i179]Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal:
A Scalable Quantum Non-local Neural Network for Image Classification. CoRR abs/2407.18906 (2024) - [i178]Washim Uddin Mondal, Vaneet Aggarwal:
Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs. CoRR abs/2408.11513 (2024) - [i177]Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang, Vaneet Aggarwal, Damon Conover, Aniket Bera:
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion. CoRR abs/2409.16950 (2024) - [i176]Mudit Gaur, Amrit Singh Bedi, Raghu Pasupathy, Vaneet Aggarwal:
On The Global Convergence Of Online RLHF With Neural Parametrization. CoRR abs/2410.15610 (2024) - 2023
- [j145]Debanjan Konar, Aditya Das Sarma, Soham Bhandary, Siddhartha Bhattacharyya, Attila Cangi, Vaneet Aggarwal:
A shallow hybrid classical-quantum spiking feedforward neural network for noise-robust image classification. Appl. Soft Comput. 136: 110099 (2023) - [j144]Dheeraj Peddireddy, Vipul Bansal, Vaneet Aggarwal:
Classical simulation of variational quantum classifiers using tensor rings. Appl. Soft Comput. 141: 110308 (2023) - [j143]Chenyi Liu, Vaneet Aggarwal, Tian Lan, Nan Geng, Yuan Yang, Mingwei Xu:
Machine Learning for Robust Network Design: A New Perspective. IEEE Commun. Mag. 61(10): 86-92 (2023) - [j142]Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Weifeng Su, Mung Chiang:
Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks. IEEE Commun. Mag. 61(12): 54-60 (2023) - [j141]Xingyu Fu, Fengfeng Zhou, Dheeraj Peddireddy, Zhengyang Kang, Martin Byung-Guk Jun, Vaneet Aggarwal:
An finite element analysis surrogate model with boundary oriented graph embedding approach for rapid design. J. Comput. Des. Eng. 10(3): 1026-1046 (2023) - [j140]Mridul Agarwal, Vaneet Aggarwal:
Reinforcement Learning for Joint Optimization of Multiple Rewards. J. Mach. Learn. Res. 24: 49:1-49:41 (2023) - [j139]Qinbo Bai, Vaneet Aggarwal, Ather Gattami:
Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints. J. Mach. Learn. Res. 24: 60:1-60:25 (2023) - [j138]Fanglin Bao, Xueji Wang, Shree Hari Sureshbabu, Gautam Sreekumar, Liping Yang, Vaneet Aggarwal, Vishnu Naresh Boddeti, Zubin Jacob:
Heat-assisted detection and ranging. Nat. 619(7971): 743-748 (2023) - [j137]Rooji Jinan, Gaurav Gautam, Parimal Parag, Vaneet Aggarwal:
Asymptotic Analysis of Probabilistic Scheduling for Erasure-Coded Heterogeneous Systems. SIGMETRICS Perform. Evaluation Rev. 50(4): 8-10 (2023) - [j136]Abhishek K. Umrawal, Vaneet Aggarwal:
Leveraging the Community Structure of a Social Network for Maximizing the Spread of Influence. SIGMETRICS Perform. Evaluation Rev. 50(4): 17-19 (2023) - [j135]Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal:
Learning Multiagent Options for Tabular Reinforcement Learning using Factor Graphs. IEEE Trans. Artif. Intell. 4(5): 1141-1153 (2023) - [j134]Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal:
A Community-Aware Framework for Social Influence Maximization. IEEE Trans. Emerg. Top. Comput. Intell. 7(4): 1253-1262 (2023) - [j133]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
Value Functions Factorization With Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients. IEEE Trans. Emerg. Top. Comput. Intell. 7(5): 1351-1361 (2023) - [j132]Guanghui Zhang, Ke Liu, Haibo Hu, Vaneet Aggarwal, Jack Y. B. Lee:
Post-Streaming Wastage Analysis - A Data Wastage Aware Framework in Mobile Video Streaming. IEEE Trans. Mob. Comput. 22(1): 389-401 (2023) - [j131]Chang-Lin Chen, Christopher G. Brinton, Vaneet Aggarwal:
Latency Minimization for Mobile Edge Computing Networks. IEEE Trans. Mob. Comput. 22(4): 2233-2247 (2023) - [j130]Guanghui Zhang, Jie Zhang, Yan Liu, Haibo Hu, Jack Y. B. Lee, Vaneet Aggarwal:
Adaptive Video Streaming With Automatic Quality-of-Experience Optimization. IEEE Trans. Mob. Comput. 22(8): 4456-4470 (2023) - [j129]Washim Uddin Mondal, Vaneet Aggarwal:
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward. Trans. Mach. Learn. Res. 2023 (2023) - [j128]Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri:
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State. Trans. Mach. Learn. Res. 2023 (2023) - [j127]Bhargav Ganguly, Seyyedali Hosseinalipour, Kwang Taik Kim, Christopher G. Brinton, Vaneet Aggarwal, David J. Love, Mung Chiang:
Multi-Edge Server-Assisted Dynamic Federated Learning With an Optimized Floating Aggregation Point. IEEE/ACM Trans. Netw. 31(6): 2682-2697 (2023) - [j126]Guanghui Zhang, Jie Zhang, Ke Liu, Jing Guo, Jack Y. B. Lee, Haibo Hu, Vaneet Aggarwal:
DUASVS: A Mobile Data Saving Strategy in Short-Form Video Streaming. IEEE Trans. Serv. Comput. 16(2): 1066-1078 (2023) - [j125]Divija Swetha Gadiraju, V. Lalitha, Vaneet Aggarwal:
An Optimization Framework Based on Deep Reinforcement Learning Approaches for Prism Blockchain. IEEE Trans. Serv. Comput. 16(4): 2451-2461 (2023) - [j124]Nan Geng, Qinbo Bai, Chenyi Liu, Tian Lan, Vaneet Aggarwal, Yuan Yang, Mingwei Xu:
A Reinforcement Learning Framework for Vehicular Network Routing Under Peak and Average Constraints. IEEE Trans. Veh. Technol. 72(5): 6753-6764 (2023) - [c131]Qinbo Bai, Amrit Singh Bedi, Vaneet Aggarwal:
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm. AAAI 2023: 6737-6744 - [c130]Fares Fourati, Vaneet Aggarwal, Christopher J. Quinn, Mohamed-Slim Alouini:
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback. AISTATS 2023: 7455-7471 - [c129]Abhishek K. Umrawal, Vaneet Aggarwal, Christopher J. Quinn:
Fractional Budget Allocation for Influence Maximization. CDC 2023: 4327-4332 - [c128]Debabrata Pal, Deeptej More, Sai Bhargav, Dipesh Tamboli, Vaneet Aggarwal, Biplab Banerjee:
Domain Adaptive Few-Shot Open-Set Learning. ICCV 2023: 18785-18794 - [c127]Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal:
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. ICML 2023: 4895-4920 - [c126]Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal:
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization. ICML 2023: 11013-11049 - [c125]Guanyu Nie, Yididiya Y. Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn:
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback. ICML 2023: 26166-26198 - [c124]Jiayu Chen, Tian Lan, Vaneet Aggarwal:
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control. ICRA 2023: 5902-5908 - [c123]Debanjan Konar, Vaneet Aggarwal, Aditya Das Sarma, Soham Bhandary, Siddhratha Bhattacharyya, Attila Cangi:
Deep Spiking Quantum Neural Network for Noisy Image Classification. IJCNN 2023: 1-10 - [c122]Jiaqi Yao, Ke Liu, Ting Liang, Theophilus A. Benson, Jack Y. B. Lee, Vaneet Aggarwal, Yungang Bao, Mingyu Chen:
A Data-Driven Framework for TCP to Achieve Flexible QoS Control in Mobile Data Networks. IWQoS 2023: 1-11 - [c121]Guanghui Zhang, Ke Liu, Mengbai Xiao, Bingshu Wang, Vaneet Aggarwal:
An Intelligent Learning Approach to Achieve Near-Second Low-Latency Live Video Streaming under Highly Fluctuating Networks. ACM Multimedia 2023: 8067-8075 - [c120]Jiayu Chen, Vaneet Aggarwal, Tian Lan:
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process. NeurIPS 2023 - [c119]Guangchen Lan, Han Wang, James Anderson, Christopher G. Brinton, Vaneet Aggarwal:
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. NeurIPS 2023 - [c118]Ahmadreza Moradipari, Mohammad Pedramfar, Modjtaba Shokrian Zini, Vaneet Aggarwal:
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning. NeurIPS 2023 - [c117]Mohammad Pedramfar, Christopher J. Quinn, Vaneet Aggarwal:
A Unified Approach for Maximizing Continuous DR-submodular Functions. NeurIPS 2023 - [i175]Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri:
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State. CoRR abs/2301.06889 (2023) - [i174]Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang:
Quantum Heavy-tailed Bandits. CoRR abs/2301.09680 (2023) - [i173]Guanyu Nie, Yididiya Y. Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn:
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback. CoRR abs/2301.13326 (2023) - [i172]Fares Fourati, Vaneet Aggarwal, Christopher John Quinn, Mohamed-Slim Alouini:
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback. CoRR abs/2302.01324 (2023) - [i171]Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini:
FilFL: Accelerating Federated Learning via Client Filtering. CoRR abs/2302.06599 (2023) - [i170]Bhargav Ganguly, Yulian Wu, Di Wang, Vaneet Aggarwal:
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning. CoRR abs/2302.08617 (2023) - [i169]Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal, Christopher G. Brinton, David J. Love, Weifeng Su, Mung Chiang:
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks. CoRR abs/2303.08361 (2023) - [i168]Mohammad Pedramfar, Vaneet Aggarwal:
Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback. CoRR abs/2303.13604 (2023) - [i167]Washim Uddin Mondal, Vaneet Aggarwal:
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward. CoRR abs/2305.02527 (2023) - [i166]Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal:
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. CoRR abs/2305.12633 (2023) - [i165]Mohammad Pedramfar, Christopher John Quinn, Vaneet Aggarwal:
A Unified Approach for Maximizing Continuous DR-submodular Functions. CoRR abs/2305.16671 (2023) - [i164]Jiayu Chen, Tian Lan, Vaneet Aggarwal:
Hierarchical Deep Counterfactual Regret Minimization. CoRR abs/2305.17327 (2023) - [i163]Chenyi Liu, Vaneet Aggarwal, Tian Lan, Nan Geng, Yuan Yang, Mingwei Xu, Qing Li:
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design. CoRR abs/2305.19153 (2023) - [i162]Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal:
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization. CoRR abs/2306.10486 (2023) - [i161]Chang-Lin Chen, Hanhan Zhou, Jiayu Chen, Mohammad Pedramfar, Vaneet Aggarwal, Tian Lan, Zheqing Zhu, Chi Zhou, Tim Gasser, Pol Mauri Ruiz, Vijay Menon, Neeraj Kumar, Hongbo Dong:
Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation via Deep Reinforcement Learning. CoRR abs/2306.17054 (2023) - [i160]Dheeraj Peddireddy, Utkarsh Priyam, Vaneet Aggarwal:
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization. CoRR abs/2307.03884 (2023) - [i159]Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal:
Scalable Multi-agent Skill Discovery based on Kronecker Graphs. CoRR abs/2307.11629 (2023) - [i158]Hanhan Zhou, Tian Lan, Vaneet Aggarwal:
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning. CoRR abs/2308.14897 (2023) - [i157]Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal:
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes. CoRR abs/2309.01922 (2023) - [i156]Debabrata Pal, Deeptej More, Sai Bhargav, Dipesh Tamboli, Vaneet Aggarwal, Biplab Banerjee:
Domain Adaptive Few-Shot Open-Set Learning. CoRR abs/2309.12814 (2023) - [i155]Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal, Bijaya K. Panigrahi:
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks. CoRR abs/2310.01515 (2023) - [i154]Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang:
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. CoRR abs/2310.07367 (2023) - [i153]Washim Uddin Mondal, Vaneet Aggarwal:
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes. CoRR abs/2310.11677 (2023) - [i152]Bhargav Ganguly, Vaneet Aggarwal:
Quantum Acceleration of Infinite Horizon Average-Reward Reinforcement Learning. CoRR abs/2310.11684 (2023) - [i151]Guangchen Lan, Han Wang, James Anderson, Christopher G. Brinton, Vaneet Aggarwal:
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. CoRR abs/2310.19807 (2023) - [i150]Ahmadreza Moradipari, Mohammad Pedramfar, Modjtaba Shokrian Zini, Vaneet Aggarwal:
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning. CoRR abs/2310.20007 (2023) - [i149]Aditya Malusare, Harish Kothandaraman, Dipesh Tamboli, Nadia A. Lanman, Vaneet Aggarwal:
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision. CoRR abs/2311.02333 (2023) - [i148]Ruibo Wang, Washim Uddin Mondal, Mustafa A. Kishk, Vaneet Aggarwal, Mohamed-Slim Alouini:
Terrain-based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation? CoRR abs/2312.01826 (2023) - [i147]Fares Fourati, Christopher John Quinn, Mohamed-Slim Alouini, Vaneet Aggarwal:
Combinatorial Stochastic-Greedy Bandit. CoRR abs/2312.08057 (2023) - 2022
- [j123]