Tournament versus fitness uniform selection. - Shane Legg; Marcus Hutter; Akshat Kumar (IEEE Congress on Evolutionary Computation, 2004)

An Artificial Immune System Based Approach for English Grammar Checking. - Akshat Kumar; Shivashankar B. Nair (ICARIS, 2007)

H-DPOP: Using Hard Constraints for Search Space Pruning in DCOP. - Akshat Kumar; Adrian Petcu; Boi Faltings (AAAI, 2008)

Distributed constraint optimization with structured resource constraints. - Akshat Kumar; Boi Faltings; Adrian Petcu (AAMAS (2), 2009)

Constraint-based dynamic programming for decentralized POMDPs with structured interactions. - Akshat Kumar; Shlomo Zilberstein (AAMAS (1), 2009)

Dynamic Programming Approximations for Partially Observable Stochastic Games. - Akshat Kumar; Shlomo Zilberstein (FLAIRS, 2009)

Event-Detecting Multi-Agent MDPs: Complexity and Constant-Factor Approximations. - Akshat Kumar; Shlomo Zilberstein (IJCAI, 2009)

Point-based backup for decentralized POMDPs: complexity and new algorithms. - Akshat Kumar; Shlomo Zilberstein (AAMAS, 2010)

MAP Estimation for Graphical Models by Likelihood Maximization. - Akshat Kumar; Shlomo Zilberstein (NIPS, 2010)

Anytime Planning for Decentralized POMDPs using Expectation Maximization. - Akshat Kumar; Shlomo Zilberstein (UAI, 2010)

Influence Diagrams with Memory States: Representation and Algorithms. - XiaoJian Wu; Akshat Kumar; Shlomo Zilberstein (ADT, 2011)

Message-passing algorithms for large structured decentralized POMDPs. - Akshat Kumar; Shlomo Zilberstein (AAMAS, 2011)

Scalable Multiagent Planning Using Probabilistic Inference. - Akshat Kumar; Shlomo Zilberstein; Marc Toussaint (IJCAI, 2011)

Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation. - Akshat Kumar; Shlomo Zilberstein (UAI, 2011)

Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning. - Akshat Kumar; XiaoJian Wu; Shlomo Zilberstein (AAAI, 2012)

Message-Passing Algorithms for MAP Estimation Using DC Programming. - Akshat Kumar; Shlomo Zilberstein; Marc Toussaint (AISTATS, 2012)

Observer: Assisted Adaptive Tracking Control of an Underactuated Autonomous Underwater Vehicle. - Mohan Santhakumar; Gaurav Parchani; Akshat Kumar; Shanmukh Santosh (AIR, 2013)

Optimization Approaches for Solving Chance Constrained Stochastic Orienteering Problems. - Pradeep Varakantham; Akshat Kumar (ADT, 2013)

Approximate Inference in Collective Graphical Models. - Daniel Sheldon; Tao Sun 008; Akshat Kumar; Thomas G. Dietterich (ICML (3), 2013)

Parameter Learning for Latent Network Diffusion. - XiaoJian Wu; Akshat Kumar; Daniel Sheldon; Shlomo Zilberstein (IJCAI, 2013)

Automated Generation of Interaction Graphs for Value-Factored Dec-POMDPs. - William Yeoh 001; Akshat Kumar; Shlomo Zilberstein (IJCAI, 2013)

Collective Diffusion Over Networks: Models and Inference. - Akshat Kumar; Daniel Sheldon; Biplav Srivastava (UAI, 2013)

Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition. - Akshat Kumar; Sudhanshu Shekhar Singh; Pranav Gupta; Gyana R. Parija (ICAPS, 2014)

On understanding diffusion dynamics of patrons at a theme park. - Jiali Du 003; Akshat Kumar; Pradeep Varakantham (AAMAS, 2014)

History-Based Controller Design and Optimization for Partially Observable MDPs. - Akshat Kumar; Shlomo Zilberstein (ICAPS, 2015)

Near-Optimal Decentralized Power Supply Restoration in Smart Grids. - Pritee Agrawal; Akshat Kumar; Pradeep Varakantham (AAMAS, 2015)

Decomposition techniques for urban consolidation problems. - Duc Thien Nguyen; Hoong Chuin Lau; Akshat Kumar (CASE, 2015)

Message Passing for Collective Graphical Models. - Tao Sun 008; Daniel Sheldon; Akshat Kumar (ICML, 2015)

Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs. - Supriyo Ghosh; Akshat Kumar; Pradeep Varakantham (IJCAI, 2015)

Learning and Controlling Network Diffusion in Dependent Cascade Models. - Jiali Du 003; Pradeep Varakantham; Akshat Kumar; Shih-Fen Cheng (WI-IAT (2), 2015)

Probabilistic Inference Techniques for Scalable Multiagent Decision Making. - Akshat Kumar; Shlomo Zilberstein; Marc Toussaint (J. Artif. Intell. Res., 2015)

Shortest Path Based Decision Making Using Probabilistic Inference. - Akshat Kumar (AAAI, 2016)

Robust Decision Making for Stochastic Network Design. - Akshat Kumar; Arambam James Singh; Pradeep Varakantham; Daniel Sheldon (AAAI, 2016)

Dual Formulations for Optimizing Dec-POMDP Controllers. - Akshat Kumar; Hala Mostafa; Shlomo Zilberstein (ICAPS, 2016)

Approximate Inference Using DC Programming For Collective Graphical Models. - Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau; Daniel Sheldon (AISTATS, 2016)

Robust Influence Maximization: (Extended Abstract). - Meghna Lowalekar; Pradeep Varakantham; Akshat Kumar (AAMAS, 2016)

Simultaneous Optimization and Sampling of Agent Trajectories over a Network. - Hala Mostafa; Akshat Kumar; Hoong Chuin Lau (AAMAS Workshops (Visionary Papers), 2016)

Song year prediction using Apache Spark. - Prakhar Mishra; Ratika Garg; Akshat Kumar; Arpan Gupta; Praveen Kumar (ICACCI, 2016)

Decentralized Planning in Stochastic Environments with Submodular Rewards. - Rajiv Ranjan Kumar; Pradeep Varakantham; Akshat Kumar (AAAI, 2017)

Collective Multiagent Sequential Decision Making Under Uncertainty. - Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau (AAAI, 2017)

Robust Optimization for Tree-Structured Stochastic Network Design. - XiaoJian Wu; Akshat Kumar; Daniel Sheldon; Shlomo Zilberstein (AAAI, 2017)

Multiagent Coordination Using Graph Structured Mathematical Optimization. - Arambam James Singh; Akshat Kumar (AAMAS, 2017)

Coordinating Vessel Traffic to Improve Safety and Efficiency. - Teck-Hou Teng; Hoong Chuin Lau; Akshat Kumar (AAMAS, 2017)

A Multi-Agent System for Coordinating Vessel Traffic. - Teck-Hou Teng; Hoong Chuin Lau; Akshat Kumar (AAMAS, 2017)

Policy Gradient With Value Function Approximation For Collective Multiagent Planning. - Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau (NIPS, 2017)

Resource-Constrained Scheduling for Maritime Traffic Management. - Lucas Agussurja; Akshat Kumar; Hoong Chuin Lau (AAAI, 2018)

Planning and Learning for Decentralized MDPs With Event Driven Rewards. - Tarun Gupta 002; Akshat Kumar; Praveen Paruchuri (AAAI, 2018)

Integrated Cooperation and Competition in Multi-Agent Decision-Making. - Kyle Hollins Wray; Akshat Kumar; Shlomo Zilberstein (AAAI, 2018)

Credit Assignment For Collective Multiagent RL With Global Rewards. - Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau (NeurIPS, 2018)

Risk-Sensitive Stochastic Orienteering Problems for Trip Optimization in Urban Environments. - Pradeep Varakantham; Akshat Kumar; Hoong Chuin Lau; William Yeoh 001 (ACM Trans. Intell. Syst. Technol., 2018)

Successor Features Based Multi-Agent RL for Event-Based Decentralized MDPs. - Tarun Gupta 002; Akshat Kumar; Praveen Paruchuri (AAAI, 2019)

Multiagent Decision Making For Maritime Traffic Management. - Arambam James Singh; Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau (AAAI, 2019)

Resource Constrained Deep Reinforcement Learning. - Abhinav Bhatia; Pradeep Varakantham; Akshat Kumar (ICAPS, 2019)

Graph Based Optimization for Multiagent Cooperation. - Arambam James Singh; Akshat Kumar (AAMAS, 2019)

Decision Making for Improving Maritime Traffic Safety Using Constraint Programming. - Saumya Bhatnagar; Akshat Kumar; Hoong Chuin Lau (IJCAI, 2019)

Multiagent Decision Making and Learning in Urban Environments. - Akshat Kumar (IJCAI, 2019)

Reinforcement Learning for Zone Based Multiagent Pathfinding under Uncertainty. - Jiajing Ling; Tarun Gupta 002; Akshat Kumar (ICAPS, 2020)

Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. - Arambam James Singh; Akshat Kumar; Hoong Chuin Lau (AAMAS, 2020)

Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems. - Jiajing Ling; Kushagra Chandak; Akshat Kumar (CoRR, 2020)

Integrating Knowledge Compilation with Reinforcement Learning for Routes. - Jiajing Ling; Kushagra Chandak; Akshat Kumar (ICAPS, 2021)

Learning and Exploiting Shaped Reward Models for Large Scale Multiagent RL. - Arambam James Singh; Akshat Kumar; Hoong Chuin Lau (ICAPS, 2021)

Ship-GAN: Generative Modeling Based Maritime Traffic Simulator. - Chaithanya Basrur; Arambam James Singh; Arunesh Sinha; Akshat Kumar (AAMAS, 2021)

Action Selection for Composable Modular Deep Reinforcement Learning. - Vaibhav Gupta; Daksh Anand; Praveen Paruchuri; Akshat Kumar (AAMAS, 2021)

Approximate Difference Rewards for Scalable Multigent Reinforcement Learning. - Arambam James Singh; Akshat Kumar; Hoong Chuin Lau (AAMAS, 2021)

Manifold learning via quantum dynamics. - Akshat Kumar; Mohan Sarovar (CoRR, 2021)

Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs. - Siow Meng Low; Akshat Kumar; Scott Sanner (AAAI, 2022)

Preface. - Sylvie Thiébaux; William Yeoh 001; Akshat Kumar; Pradeep Varakantham (ICAPS, 2022)

InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools. - Mustafa Doga Dogan; Ahmad Taka; Michael Lu; Yunyi Zhu; Akshat Kumar; Aakar Gupta; Stefanie Müller 001 (CHI, 2022)

Trajectory Optimization for Safe Navigation in Maritime Traffic Using Historical Data. - Chaithanya Basrur; Arambam James Singh; Arunesh Sinha; Akshat Kumar; T. K. Satish Kumar (CP, 2022)

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. - Gerald Woo; Chenghao Liu; Doyen Sahoo; Akshat Kumar; Steven C. H. Hoi (ICLR, 2022)

Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies. - Mikhail Kazdagli; Mohit Tiwari; Akshat Kumar (IJCAI, 2022)

Constrained Multiagent Reinforcement Learning for Large Agent Population. - Jiajing Ling; Arambam James Singh; Nguyen Duc Thien; Akshat Kumar (ECML/PKDD (4), 2022)

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting. - Gerald Woo; Chenghao Liu; Doyen Sahoo; Akshat Kumar; Steven C. H. Hoi (CoRR, 2022)

DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting. - Gerald Woo; Chenghao Liu; Doyen Sahoo; Akshat Kumar; Steven C. H. Hoi (CoRR, 2022)

Shining light on data: Geometric data analysis through quantum dynamics. - Akshat Kumar; Mohan Sarovar (CoRR, 2022)

Closing the loop: automatically identifying abnormal imaging results in scanned documents. - Akshat Kumar; Heath Goodrum; Ashley Kim; Carly Stender; Kirk Roberts; Elmer V. Bernstam (J. Am. Medical Informatics Assoc., 2022)

Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming. - Aravinth Chembu; Scott Sanner; Hassan Khurram; Akshat Kumar (AAAI, 2023)

Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers. - Aishwarya Srivastava; Sandhya Saisubramanian; Praveen Paruchuri; Akshat Kumar; Shlomo Zilberstein (AAAI, 2023)

Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects. - Siow Meng Low; Akshat Kumar; Scott Sanner (ICAPS, 2023)

Knowledge Compilation for Constrained Combinatorial Action Spaces in Reinforcement Learning. - Jiajing Ling; Moritz Lukas Schuler; Akshat Kumar; Pradeep Varakantham (AAMAS, 2023)

A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination. - Jihwan Jeong; Scott Sanner; Akshat Kumar (CPAIOR, 2023)

Learning Deep Time-index Models for Time Series Forecasting. - Gerald Woo; Chenghao Liu; Doyen Sahoo; Akshat Kumar; Steven C. H. Hoi (ICML, 2023)

FlowPG: Action-constrained Policy Gradient with Normalizing Flows. - Janaka Chathuranga Brahmanage; Jiajing Ling; Akshat Kumar (NeurIPS, 2023)

Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain. - Gerald Woo; Chenghao Liu; Akshat Kumar; Doyen Sahoo (CoRR, 2023)

Mean-field games among teams. - Jayakumar Subramanian; Akshat Kumar; Aditya Mahajan (CoRR, 2023)

Factored MDP based Moving Target Defense with Dynamic Threat Modeling. - Megha Bose; Praveen Paruchuri; Akshat Kumar (AAMAS, 2024)

Difference of Convex Functions Programming for Policy Optimization in Reinforcement Learning. - Akshat Kumar (AAMAS, 2024)

An Efficient Approach Using Graphical Neural Network for Network Intrusion Detection System. - Shailendra Pratap Singh; Bansal Abhay; Akshat Kumar; Prakhar Agrawal; Druhan Singh; Vivashwan Ghosh (ICCCNT, 2024)

Unified Training of Universal Time Series Forecasting Transformers. - Gerald Woo; Chenghao Liu; Akshat Kumar; Caiming Xiong; Silvio Savarese; Doyen Sahoo (ICML, 2024)

COMEX: Deeply Observing Application Behavior on Real Android Devices. - Zeya Umayya; Dhruv Malik; Arpit Nandi; Akshat Kumar; Sareena Karapoola; Sambuddho Chakravarty (CSET @ USENIX Security Symposium, 2024)

An Adaptive Multi-Objective Optimization on CMOS Two Stage Op-Amp Circuit Synthesis. - Sridhar P; Harish Mallikarjun Kittur; Arghya Korantak; Akshat Kumar (VDAT, 2024)

Leveraging AI Planning For Detecting Cloud Security Vulnerabilities. - Mikhail Kazdagli; Mohit Tiwari; Akshat Kumar (CoRR, 2024)

Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints. - Siow Meng Low; Akshat Kumar (CoRR, 2024)

A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency. - Megha Bose; Praveen Paruchuri; Akshat Kumar (CoRR, 2024)

Leveraging Constraint Violation Signals for Action Constrained Reinforcement Learning. - Janaka Chathuranga Brahmanage; Jiajing Ling; Akshat Kumar (AAAI, 2025)

Offline Safe Reinforcement Learning Using Trajectory Classification. - Ze Gong; Akshat Kumar; Pradeep Varakantham (AAAI, 2025)

Adaptive Moving Target Defense in Web Applications and Networks using Factored MDP. - Megha Bose; Praveen Paruchuri; Akshat Kumar (COMSNETS, 2025)

ShipNaviSim: Data-Driven Simulation for Real-World Maritime Navigation. - Quang Anh Pham; Janaka Chathuranga Brahmanage; Akshat Kumar (AAMAS, 2025)

TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback. - Siow Meng Low; Akshat Kumar (CoRR, 2025)