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2020 – today
- 2024
[c80]Megha Bose, Praveen Paruchuri, Akshat Kumar:
Factored MDP based Moving Target Defense with Dynamic Threat Modeling. AAMAS 2024: 2165-2167- [c79]Akshat Kumar:
Difference of Convex Functions Programming for Policy Optimization in Reinforcement Learning. AAMAS 2024: 2339-2341 - [c78]Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo:
Unified Training of Universal Time Series Forecasting Transformers. ICML 2024 - [c77]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: 100-109 - [i24]Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo:
Unified Training of Universal Time Series Forecasting Transformers. CoRR abs/2402.02592 (2024) - [i23]Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar:
FlowPG: Action-constrained Policy Gradient with Normalizing Flows. CoRR abs/2402.05149 (2024) - [i22]Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar:
Leveraging AI Planning For Detecting Cloud Security Vulnerabilities. CoRR abs/2402.10985 (2024) - [i21]Siow Meng Low, Akshat Kumar:
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints. CoRR abs/2405.03005 (2024) - [i20]Megha Bose, Praveen Paruchuri, Akshat Kumar:
A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency. CoRR abs/2408.08934 (2024) - 2023
- [c76]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: 7015-7023 - [c75]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: 15144-15151 - [c74]Siow Meng Low, Akshat Kumar, Scott Sanner:
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. ICAPS 2023: 596-604 - [c73]Jiajing Ling, Moritz Lukas Schuler, Akshat Kumar, Pradeep Varakantham:
Knowledge Compilation for Constrained Combinatorial Action Spaces in Reinforcement Learning. AAMAS 2023: 860-868 - [c72]Jihwan Jeong, Scott Sanner, Akshat Kumar:
A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination. CPAIOR 2023: 79-95 - [c71]Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi:
Learning Deep Time-index Models for Time Series Forecasting. ICML 2023: 37217-37237 - [c70]Janaka Chathuranga Brahmanage, Jiajing Ling, Akshat Kumar:
FlowPG: Action-constrained Policy Gradient with Normalizing Flows. NeurIPS 2023 - [i19]Siow Meng Low, Akshat Kumar, Scott Sanner:
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. CoRR abs/2304.03081 (2023) - [i18]Gerald Woo, Chenghao Liu, Akshat Kumar, Doyen Sahoo:
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain. CoRR abs/2310.05063 (2023) - [i17]Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan:
Mean-field games among teams. CoRR abs/2310.12282 (2023) - 2022
- [j3]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): 831-840 (2022) - [c69]Siow Meng Low, Akshat Kumar, Scott Sanner:
Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. AAAI 2022: 9840-9848 - [c68]Sylvie Thiébaux, William Yeoh, Akshat Kumar, Pradeep Varakantham:
Preface. ICAPS 2022 - [c67]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: 269:1-269:12 - [c66]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: 5:1-5:17 - [c65]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 - [c64]Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar:
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies. IJCAI 2022: 1850-1858 - [c63]Jiajing Ling, Arambam James Singh, Nguyen Duc Thien, Akshat Kumar:
Constrained Multiagent Reinforcement Learning for Large Agent Population. ECML/PKDD (4) 2022: 183-199 - [e1]Akshat Kumar, Sylvie Thiébaux, Pradeep Varakantham, William Yeoh:
Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, ICAPS 2022, Singapore (virtual), June 13-24, 2022. AAAI Press 2022, ISBN 978-1-57735-874-9 [contents] - [i16]Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi:
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting. CoRR abs/2202.01381 (2022) - [i15]Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi:
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. CoRR abs/2202.01575 (2022) - [i14]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 abs/2202.06165 (2022) - [i13]Siow Meng Low, Akshat Kumar, Scott Sanner:
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. CoRR abs/2203.12679 (2022) - [i12]Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar:
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies. CoRR abs/2205.01240 (2022) - [i11]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 abs/2207.06046 (2022) - [i10]Akshat Kumar, Mohan Sarovar:
Shining light on data: Geometric data analysis through quantum dynamics. CoRR abs/2212.00682 (2022) - 2021
- [c62]Jiajing Ling, Kushagra Chandak, Akshat Kumar:
Integrating Knowledge Compilation with Reinforcement Learning for Routes. ICAPS 2021: 542-550 - [c61]Arambam James Singh, Akshat Kumar, Hoong Chuin Lau:
Learning and Exploiting Shaped Reward Models for Large Scale Multiagent RL. ICAPS 2021: 588-596 - [c60]Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Akshat Kumar:
Action Selection for Composable Modular Deep Reinforcement Learning. AAMAS 2021: 565-573 - [c59]Arambam James Singh, Akshat Kumar, Hoong Chuin Lau:
Approximate Difference Rewards for Scalable Multigent Reinforcement Learning. AAMAS 2021: 1655-1657 - [c58]Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar:
Ship-GAN: Generative Modeling Based Maritime Traffic Simulator. AAMAS 2021: 1755-1757 - [i9]Akshat Kumar, Mohan Sarovar:
Manifold learning via quantum dynamics. CoRR abs/2112.11161 (2021) - 2020
- [c57]Jiajing Ling, Tarun Gupta, Akshat Kumar:
Reinforcement Learning for Zone Based Multiagent Pathfinding under Uncertainty. ICAPS 2020: 551-559 - [c56]Arambam James Singh, Akshat Kumar, Hoong Chuin Lau:
Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. AAMAS 2020: 1278-1286 - [i8]Jiajing Ling, Kushagra Chandak, Akshat Kumar:
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems. CoRR abs/2011.04405 (2020)
2010 – 2019
- 2019
- [c55]Tarun Gupta, Akshat Kumar, Praveen Paruchuri:
Successor Features Based Multi-Agent RL for Event-Based Decentralized MDPs. AAAI 2019: 6054-6061 - [c54]Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Multiagent Decision Making For Maritime Traffic Management. AAAI 2019: 6171-6178 - [c53]Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar:
Resource Constrained Deep Reinforcement Learning. ICAPS 2019: 610-620 - [c52]Arambam James Singh, Akshat Kumar:
Graph Based Optimization for Multiagent Cooperation. AAMAS 2019: 1497-1505 - [c51]Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau:
Decision Making for Improving Maritime Traffic Safety Using Constraint Programming. IJCAI 2019: 5794-5800 - [c50]Akshat Kumar:
Multiagent Decision Making and Learning in Urban Environments. IJCAI 2019: 6398-6402 - 2018
- [j2]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): 24:1-24:25 (2018) - [c49]Tarun Gupta, Akshat Kumar, Praveen Paruchuri:
Planning and Learning for Decentralized MDPs with Event Driven Rewards. AAAI Workshops 2018: 665 - [c48]Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein:
Integrated Cooperation and Competition in Multi-Agent Decision-Making. AAAI 2018: 4751-4758 - [c47]Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau:
Resource-Constrained Scheduling for Maritime Traffic Management. AAAI 2018: 6086-6093 - [c46]Tarun Gupta, Akshat Kumar, Praveen Paruchuri:
Planning and Learning for Decentralized MDPs With Event Driven Rewards. AAAI 2018: 6186-6194 - [c45]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Credit Assignment For Collective Multiagent RL With Global Rewards. NeurIPS 2018: 8113-8124 - [i7]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Policy Gradient With Value Function Approximation For Collective Multiagent Planning. CoRR abs/1804.02884 (2018) - [i6]Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar:
Resource Constrained Deep Reinforcement Learning. CoRR abs/1812.00600 (2018) - 2017
- [c44]Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar:
Decentralized Planning in Stochastic Environments with Submodular Rewards. AAAI 2017: 3021-3028 - [c43]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Collective Multiagent Sequential Decision Making Under Uncertainty. AAAI 2017: 3036-3043 - [c42]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Robust Optimization for Tree-Structured Stochastic Network Design. AAAI 2017: 4545-4551 - [c41]Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar:
Coordinating Vessel Traffic to Improve Safety and Efficiency. AAMAS 2017: 141-149 - [c40]Arambam James Singh, Akshat Kumar:
Multiagent Coordination Using Graph Structured Mathematical Optimization. AAMAS 2017: 1739-1741 - [c39]Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar:
A Multi-Agent System for Coordinating Vessel Traffic. AAMAS 2017: 1814-1816 - [c38]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau:
Policy Gradient With Value Function Approximation For Collective Multiagent Planning. NIPS 2017: 4319-4329 - 2016
- [c37]Akshat Kumar:
Shortest Path Based Decision Making Using Probabilistic Inference. AAAI 2016: 3849-3856 - [c36]Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon:
Robust Decision Making for Stochastic Network Design. AAAI 2016: 3857-3863 - [c35]Akshat Kumar, Hala Mostafa, Shlomo Zilberstein:
Dual Formulations for Optimizing Dec-POMDP Controllers. ICAPS 2016: 202-210 - [c34]Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon:
Approximate Inference Using DC Programming For Collective Graphical Models. AISTATS 2016: 685-693 - [c33]Hala Mostafa, Akshat Kumar, Hoong Chuin Lau:
Simultaneous Optimization and Sampling of Agent Trajectories over a Network. AAMAS Workshops (Visionary Papers) 2016: 50-66 - [c32]Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar:
Robust Influence Maximization: (Extended Abstract). AAMAS 2016: 1395-1396 - [c31]Prakhar Mishra, Ratika Garg, Akshat Kumar, Arpan Gupta, Praveen Kumar:
Song year prediction using Apache Spark. ICACCI 2016: 1590-1594 - [i5]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Robust Optimization for Tree-Structured Stochastic Network Design. CoRR abs/1612.00104 (2016) - 2015
- [j1]Akshat Kumar, Shlomo Zilberstein, Marc Toussaint:
Probabilistic Inference Techniques for Scalable Multiagent Decision Making. J. Artif. Intell. Res. 53: 223-270 (2015) - [c30]Akshat Kumar, Shlomo Zilberstein:
History-Based Controller Design and Optimization for Partially Observable MDPs. ICAPS 2015: 156-164 - [c29]Pritee Agrawal, Akshat Kumar, Pradeep Varakantham:
Near-Optimal Decentralized Power Supply Restoration in Smart Grids. AAMAS 2015: 1275-1283 - [c28]Duc Thien Nguyen, Hoong Chuin Lau, Akshat Kumar:
Decomposition techniques for urban consolidation problems. CASE 2015: 57-62 - [c27]Tao Sun, Daniel Sheldon, Akshat Kumar:
Message Passing for Collective Graphical Models. ICML 2015: 853-861 - [c26]Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham:
Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs. IJCAI 2015: 411-417 - [c25]Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng:
Learning and Controlling Network Diffusion in Dependent Cascade Models. WI-IAT (2) 2015: 336-343 - 2014
- [c24]Akshat Kumar, Sudhanshu Shekhar Singh, Pranav Gupta, Gyana R. Parija:
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition. ICAPS 2014 - [c23]Jiali Du, Akshat Kumar, Pradeep Varakantham:
On understanding diffusion dynamics of patrons at a theme park. AAMAS 2014: 1501-1502 - 2013
- [c22]Mohan Santhakumar, Gaurav Parchani, Akshat Kumar, Shanmukh Santosh:
Observer: Assisted Adaptive Tracking Control of an Underactuated Autonomous Underwater Vehicle. AIR 2013: 5:1-5:7 - [c21]Pradeep Varakantham, Akshat Kumar:
Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems. ADT 2013: 387-398 - [c20]Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich:
Approximate Inference in Collective Graphical Models. ICML (3) 2013: 1004-1012 - [c19]William Yeoh, Akshat Kumar, Shlomo Zilberstein:
Automated Generation of Interaction Graphs for Value-Factored Dec-POMDPs. IJCAI 2013: 411-417 - [c18]XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein:
Parameter Learning for Latent Network Diffusion. IJCAI 2013: 2923-2930 - [c17]Akshat Kumar, Daniel Sheldon, Biplav Srivastava:
Collective Diffusion Over Networks: Models and Inference. UAI 2013 - [i4]Akshat Kumar, Daniel Sheldon, Biplav Srivastava:
Collective Diffusion Over Networks: Models and Inference. CoRR abs/1309.6841 (2013) - 2012
- [c16]Akshat Kumar, XiaoJian Wu, Shlomo Zilberstein:
Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning. AAAI 2012: 309-315 - [c15]Akshat Kumar, Shlomo Zilberstein, Marc Toussaint:
Message-Passing Algorithms for MAP Estimation Using DC Programming. AISTATS 2012: 656-664 - [i3]Akshat Kumar, Shlomo Zilberstein:
Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation. CoRR abs/1202.3739 (2012) - [i2]Akshat Kumar, Shlomo Zilberstein:
Anytime Planning for Decentralized POMDPs using Expectation Maximization. CoRR abs/1203.3490 (2012) - 2011
- [c14]XiaoJian Wu, Akshat Kumar, Shlomo Zilberstein:
Influence Diagrams with Memory States: Representation and Algorithms. ADT 2011: 306-319 - [c13]Akshat Kumar, Shlomo Zilberstein:
Message-passing algorithms for large structured decentralized POMDPs. AAMAS 2011: 1087-1088 - [c12]Akshat Kumar, Shlomo Zilberstein, Marc Toussaint:
Scalable Multiagent Planning Using Probabilistic Inference. IJCAI 2011: 2140-2146 - [c11]Akshat Kumar, Shlomo Zilberstein:
Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation. UAI 2011: 428-435 - 2010
- [c10]Akshat Kumar, Shlomo Zilberstein:
Point-based backup for decentralized POMDPs: complexity and new algorithms. AAMAS 2010: 1315-1322 - [c9]Akshat Kumar, Shlomo Zilberstein:
MAP Estimation for Graphical Models by Likelihood Maximization. NIPS 2010: 1180-1188 - [c8]Akshat Kumar, Shlomo Zilberstein:
Anytime Planning for Decentralized POMDPs using Expectation Maximization. UAI 2010: 294-301
2000 – 2009
- 2009
- [c7]Akshat Kumar, Shlomo Zilberstein:
Constraint-based dynamic programming for decentralized POMDPs with structured interactions. AAMAS (1) 2009: 561-568 - [c6]Akshat Kumar, Boi Faltings, Adrian Petcu:
Distributed constraint optimization with structured resource constraints. AAMAS (2) 2009: 923-930 - [c5]Akshat Kumar, Shlomo Zilberstein:
Dynamic Programming Approximations for Partially Observable Stochastic Games. FLAIRS 2009 - [c4]Akshat Kumar, Shlomo Zilberstein:
Event-Detecting Multi-Agent MDPs: Complexity and Constant-Factor Approximations. IJCAI 2009: 201-207 - 2008
- [c3]Akshat Kumar, Adrian Petcu, Boi Faltings:
H-DPOP: Using Hard Constraints for Search Space Pruning in DCOP. AAAI 2008: 325-330 - 2007
- [c2]Akshat Kumar, Shivashankar B. Nair:
An Artificial Immune System Based Approach for English Grammar Checking. ICARIS 2007: 348-357 - 2004
- [c1]Shane Legg, Marcus Hutter, Akshat Kumar:
Tournament versus fitness uniform selection. IEEE Congress on Evolutionary Computation 2004: 2144-2151 - [i1]Shane Legg, Marcus Hutter, Akshat Kumar:
Tournament versus Fitness Uniform Selection. CoRR cs.LG/0403038 (2004)
Coauthor Index
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