Akshat Kumar

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 70

Top Venues

Publications

Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
Unified Training of Universal Time Series Forecasting Transformers. CoRR (2024)
Megha Bose, Praveen Paruchuri, Akshat Kumar
Factored MDP based Moving Target Defense with Dynamic Threat Modeling. AAMAS (2024)
Zeya Umayya, Dhruv Malik, Arpit Nandi, Akshat Kumar, Sareena Karapoola, Sambuddho Chakravarty
COMEX: Deeply Observing Application Behavior on Real Android Devices. CSET @ USENIX Security Symposium (2024)
Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Leveraging AI Planning For Detecting Cloud Security Vulnerabilities. CoRR (2024)
Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar
FlowPG: Action-constrained Policy Gradient with Normalizing Flows. CoRR (2024)
Akshat Kumar
Difference of Convex Functions Programming for Policy Optimization in Reinforcement Learning. AAMAS (2024)
Siow Meng Low, Akshat Kumar
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints. CoRR (2024)
Siow Meng Low, Akshat Kumar, Scott Sanner
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. ICAPS (2023)
Siow Meng Low, Akshat Kumar, Scott Sanner
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. CoRR (2023)
Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar
FlowPG: Action-constrained Policy Gradient with Normalizing Flows. NeurIPS (2023)
Gerald Woo, Chenghao Liu, Akshat Kumar, Doyen Sahoo
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain. CoRR (2023)
Jihwan Jeong, Scott Sanner, Akshat Kumar
A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination. CPAIOR (2023)
Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar
Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming. AAAI (2023)
Aishwarya Srivastava, Sandhya Saisubramanian, Praveen Paruchuri, Akshat Kumar, Shlomo Zilberstein
Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers. AAAI (2023)
Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan
Mean-field games among teams. CoRR (2023)
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi
Learning Deep Time-index Models for Time Series Forecasting. ICML (2023)
Jiajing Ling, Moritz Lukas Schuler, Akshat Kumar, Pradeep Varakantham
Knowledge Compilation for Constrained Combinatorial Action Spaces in Reinforcement Learning. AAMAS (2023)
Jiajing Ling, Arambam James Singh, Nguyen Duc Thien, Akshat Kumar
Constrained Multiagent Reinforcement Learning for Large Agent Population. ECML/PKDD (4) (2022)
Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies. CoRR (2022)
Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies. IJCAI (2022)
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. ICLR (2022)
Siow Meng Low, Akshat Kumar, Scott Sanner
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. CoRR (2022)
Mustafa Doga Dogan, Ahmad Taka, Michael Lu, Yunyi Zhu, Akshat Kumar, Aakar Gupta, Stefanie Mueller
InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools. CoRR (2022)
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting. CoRR (2022)
Akshat Kumar, Mohan Sarovar
Shining light on data: Geometric data analysis through quantum dynamics. CoRR (2022)
Mustafa Doga Dogan, Ahmad Taka, Michael Lu, Yunyi Zhu, Akshat Kumar, Aakar Gupta, Stefanie Müller
InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools. CHI (2022)
Siow Meng Low, Akshat Kumar, Scott Sanner
Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. AAAI (2022)
Akshat Kumar, Heath Goodrum, Ashley Kim, Carly Stender, Kirk Roberts, Elmer V. Bernstam
Closing the loop: automatically identifying abnormal imaging results in scanned documents. J. Am. Medical Informatics Assoc. 29 (5) (2022)
Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar
Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data. CP (2022)
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi
DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting. CoRR (2022)
Sylvie Thiébaux, William Yeoh, Akshat Kumar, Pradeep Varakantham
Preface. ICAPS (2022)
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. CoRR (2022)
Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar
Action Selection for Composable Modular Deep Reinforcement Learning. AAMAS (2021)
Jiajing Ling, Kushagra Chandak, Akshat Kumar
Integrating Knowledge Compilation with Reinforcement Learning for Routes. ICAPS (2021)
Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning and Exploiting Shaped Reward Models for Large Scale Multiagent RL. ICAPS (2021)
Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar
Ship-GAN: Generative Modeling Based Maritime Traffic Simulator. AAMAS (2021)
Akshat Kumar, Mohan Sarovar
Manifold learning via quantum dynamics. CoRR (2021)
Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Approximate Difference Rewards for Scalable Multigent Reinforcement Learning. AAMAS (2021)
Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. AAMAS (2020)
Jiajing Ling, Tarun Gupta, Akshat Kumar
Reinforcement Learning for Zone Based Multiagent Pathfinding under Uncertainty. ICAPS (2020)
Jiajing Ling, Kushagra Chandak, Akshat Kumar
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems. CoRR (2020)
Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau
Decision Making for Improving Maritime Traffic Safety Using Constraint Programming. IJCAI (2019)
Tarun Gupta, Akshat Kumar, Praveen Paruchuri
Successor Features Based Multi-Agent RL for Event-Based Decentralized MDPs. AAAI (2019)
Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Multiagent Decision Making For Maritime Traffic Management. AAAI (2019)
Arambam James Singh, Akshat Kumar
Graph Based Optimization for Multiagent Cooperation. AAMAS (2019)
Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar
Resource Constrained Deep Reinforcement Learning. ICAPS (2019)
Akshat Kumar
Multiagent Decision Making and Learning in Urban Environments. IJCAI (2019)
Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Integrated Cooperation and Competition in Multi-Agent Decision-Making. AAAI (2018)
Tarun Gupta, Akshat Kumar, Praveen Paruchuri
Planning and Learning for Decentralized MDPs With Event Driven Rewards. AAAI (2018)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Credit Assignment For Collective Multiagent RL With Global Rewards. NeurIPS (2018)
Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau
Resource-Constrained Scheduling for Maritime Traffic Management. AAAI (2018)
Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh
Risk-Sensitive Stochastic Orienteering Problems for Trip Optimization in Urban Environments. ACM Trans. Intell. Syst. Technol. 9 (3) (2018)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Policy Gradient With Value Function Approximation For Collective Multiagent Planning. CoRR (2018)
Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar
Resource Constrained Deep Reinforcement Learning. CoRR (2018)
Tarun Gupta, Akshat Kumar, Praveen Paruchuri
Planning and Learning for Decentralized MDPs with Event Driven Rewards. AAAI Workshops (2018)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Collective Multiagent Sequential Decision Making Under Uncertainty. AAAI (2017)
Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
A Multi-Agent System for Coordinating Vessel Traffic. AAMAS (2017)
Arambam James Singh, Akshat Kumar
Multiagent Coordination Using Graph Structured Mathematical Optimization. AAMAS (2017)
Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar
Decentralized Planning in Stochastic Environments with Submodular Rewards. AAAI (2017)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Robust Optimization for Tree-Structured Stochastic Network Design. AAAI (2017)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Policy Gradient With Value Function Approximation For Collective Multiagent Planning. NIPS (2017)
Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
Coordinating Vessel Traffic to Improve Safety and Efficiency. AAMAS (2017)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Robust Optimization for Tree-Structured Stochastic Network Design. CoRR (2016)
Prakhar Mishra, Ratika Garg, Akshat Kumar, Arpan Gupta, Praveen Kumar
Song year prediction using Apache Spark. ICACCI (2016)
Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Simultaneous Optimization and Sampling of Agent Trajectories over a Network. AAMAS Workshops (Visionary Papers) (2016)
Akshat Kumar, Hala Mostafa, Shlomo Zilberstein
Dual Formulations for Optimizing Dec-POMDP Controllers. ICAPS (2016)
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon
Approximate Inference Using DC Programming For Collective Graphical Models. AISTATS (2016)
Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
Robust Influence Maximization: (Extended Abstract). AAMAS (2016)
Akshat Kumar
Shortest Path Based Decision Making Using Probabilistic Inference. AAAI (2016)
Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Robust Decision Making for Stochastic Network Design. AAAI (2016)
Pritee Agrawal, Akshat Kumar, Pradeep Varakantham
Near-Optimal Decentralized Power Supply Restoration in Smart Grids. AAMAS (2015)
Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Learning and Controlling Network Diffusion in Dependent Cascade Models. WI-IAT (2) (2015)
Tao Sun, Daniel Sheldon, Akshat Kumar
Message Passing for Collective Graphical Models. ICML (2015)
Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
Probabilistic Inference Techniques for Scalable Multiagent Decision Making. J. Artif. Intell. Res. 53 (2015)
Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham
Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs. IJCAI (2015)
Duc Thien Nguyen, Hoong Chuin Lau, Akshat Kumar
Decomposition techniques for urban consolidation problems. CASE (2015)
Akshat Kumar, Shlomo Zilberstein
History-Based Controller Design and Optimization for Partially Observable MDPs. ICAPS (2015)
Jiali Du, Akshat Kumar, Pradeep Varakantham
On understanding diffusion dynamics of patrons at a theme park. AAMAS (2014)
Akshat Kumar, Sudhanshu Shekhar Singh, Pranav Gupta, Gyana R. Parija
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition. ICAPS (2014)
William Yeoh, Akshat Kumar, Shlomo Zilberstein
Automated Generation of Interaction Graphs for Value-Factored Dec-POMDPs. IJCAI (2013)
Mohan Santhakumar, Gaurav Parchani, Akshat Kumar, Shanmukh Santosh
Observer: Assisted Adaptive Tracking Control of an Underactuated Autonomous Underwater Vehicle. AIR (2013)
Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich
Approximate Inference in Collective Graphical Models. ICML (3) (2013)
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Collective Diffusion Over Networks: Models and Inference. UAI (2013)
XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
Parameter Learning for Latent Network Diffusion. IJCAI (2013)
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Collective Diffusion Over Networks: Models and Inference. CoRR (2013)
Pradeep Varakantham, Akshat Kumar
Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems. ADT (2013)
Akshat Kumar, Shlomo Zilberstein
Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation CoRR (2012)
Akshat Kumar, Shlomo Zilberstein
Anytime Planning for Decentralized POMDPs using Expectation Maximization CoRR (2012)
Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
Message-Passing Algorithms for MAP Estimation Using DC Programming. AISTATS (2012)
Akshat Kumar, XiaoJian Wu, Shlomo Zilberstein
Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning. AAAI (2012)
Akshat Kumar, Shlomo Zilberstein
Message-passing algorithms for large structured decentralized POMDPs. AAMAS (2011)
Akshat Kumar, Shlomo Zilberstein
Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation. UAI (2011)
Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
Scalable Multiagent Planning Using Probabilistic Inference. IJCAI (2011)
XiaoJian Wu, Akshat Kumar, Shlomo Zilberstein
Influence Diagrams with Memory States: Representation and Algorithms. ADT (2011)
Akshat Kumar, Shlomo Zilberstein
Anytime Planning for Decentralized POMDPs using Expectation Maximization. UAI (2010)
Akshat Kumar, Shlomo Zilberstein
MAP Estimation for Graphical Models by Likelihood Maximization. NIPS (2010)
Akshat Kumar, Shlomo Zilberstein
Point-based backup for decentralized POMDPs: complexity and new algorithms. AAMAS (2010)
Akshat Kumar, Shlomo Zilberstein
Dynamic Programming Approximations for Partially Observable Stochastic Games. FLAIRS Conference (2009)
Akshat Kumar, Shlomo Zilberstein
Constraint-based dynamic programming for decentralized POMDPs with structured interactions. AAMAS (1) (2009)
Akshat Kumar, Boi Faltings, Adrian Petcu
Distributed constraint optimization with structured resource constraints. AAMAS (2) (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Akshat Kumar