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ORCIDHarshad Khadilkar
Harshad D. Khadilkar
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Journal Articles
- 2022
[j7]Hardik Meisheri
, Nazneen N. Sultana, Mayank Baranwal, Vinita Baniwal, Somjit Nath, Satyam Verma, Balaraman Ravindran, Harshad Khadilkar:
Scalable multi-product inventory control with lead time constraints using reinforcement learning. Neural Comput. Appl. 34(3): 1735-1757 (2022)- 2019
- [j6]Sumit Raut, Sudhir K. Sinha, Harshad Khadilkar, Shripad Salsingikar:
A rolling horizon optimisation model for consolidated hump yard operational planning. J. Rail Transp. Plan. Manag. 9: 3-21 (2019) - [j5]Harshad Khadilkar:
A Scalable Reinforcement Learning Algorithm for Scheduling Railway Lines. IEEE Trans. Intell. Transp. Syst. 20(2): 727-736 (2019) - 2017
- [j4]Harshad Khadilkar:
Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks. Transp. Sci. 51(4): 1161-1176 (2017) - 2016
- [j3]Harshad Khadilkar, Hamsa Balakrishnan:
Integrated Control of Airport and Terminal Airspace Operations. IEEE Trans. Control. Syst. Technol. 24(1): 216-225 (2016) - 2014
- [j2]Pangun Park, Harshad Khadilkar, Hamsa Balakrishnan, Claire J. Tomlin:
High Confidence Networked Control for Next Generation Air Transportation Systems. IEEE Trans. Autom. Control. 59(12): 3357-3372 (2014) - [j1]Pangun Park, Harshad Khadilkar, Hamsa Balakrishnan, Claire J. Tomlin:
Hybrid Communication Protocols and Control Algorithms for NextGen Aircraft Arrivals. IEEE Trans. Intell. Transp. Syst. 15(2): 615-626 (2014)
Conference and Workshop Papers
- 2024
- [c38]Pranavi Pathakota, Hardik Meisheri, Harshad Khadilkar:
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces. AAMAS 2024: 2411-2413 - [c37]Richa Verma, Durgesh Kalwar, Harshad Khadilkar, Balaraman Ravindran:
Guiding Offline Reinforcement Learning Using a Safety Expert. COMAD/CODS 2024: 82-90 - [c36]Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan, Hardik Meisheri, Harshad Khadilkar, Balaraman Ravindran:
A Learning Approach for Discovering Cost-Efficient Integrated Sourcing and Routing Strategies in E-Commerce. COMAD/CODS 2024: 430-438 - [c35]Hastyn Doshi, Ayush Tripathi, Keshav Agarwal, Harshad Khadilkar, Shivaram Kalyanakrishnan:
Linear-Time Optimal Deadlock Detection for Efficient Scheduling in Multi-Track Railway Networks. IJCAI 2024: 5799-5807 - 2023
- [c34]Durgesh Kalwar, Omkar Shelke, Somjit Nath, Hardik Meisheri, Harshad Khadilkar:
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning. AAMAS 2023: 802-809 - [c33]Gauri Garg, Harshad Khadilkar, Ankur A. Kulkarni, Aditya A. Paranjape:
Classifier Design for Decentralised Sensing with Digital Communication. CDC 2023: 8432-8437 - [c32]Harshad Khadilkar, Hardik Meisheri:
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning. COMAD/CODS 2023: 108-112 - [c31]Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara, Hardik Meisheri, Shaun D'Souza, Dheeraj Shah, Harshad Khadilkar:
Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce. COMAD/CODS 2023: 176-184 - [c30]Swaroop Nath, Pushpak Bhattacharyya, Harshad Khadilkar:
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning. EMNLP 2023: 15770-15789 - 2022
- [c29]Kavya Borra, Ashwin Krishnan, Harshad Khadilkar, Manoj Nambiar, Ansuma Basumatary, Rekha Singhal, Arijit Mukherjee:
Performance improvement of reinforcement learning algorithms for online 3D bin packing using FPGA. AIMLSystems 2022: 18:1-18:7 - [c28]Omkar Shelke, Hardik Meisheri, Harshad Khadilkar:
Identifying efficient curricula for reinforcement learning in complex environments with a fixed computational budget. COMAD/CODS 2022: 81-89 - [c27]Deepak Mohapatra, Ankush Ojha, Harshad Khadilkar, Supratim Ghosh:
Gatekeeper: A deep reinforcement learning-cum-heuristic based algorithm for scheduling and routing trains in complex environments. IJCNN 2022: 1-7 - [c26]Ramya Hebbalaguppe, Soumya Suvra Ghosal, Jatin Prakash, Harshad Khadilkar, Chetan Arora:
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection Using Compounded Corruptions. ECML/PKDD (3) 2022: 529-545 - 2021
- [c25]Somjit Nath, Richa Verma, Abhik Ray, Harshad Khadilkar:
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning. AAMAS 2021: 1607-1609 - [c24]Somjit Nath, Mayank Baranwal, Harshad Khadilkar:
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays. CIKM 2021: 1346-1355 - [c23]Omkar Shelke, Vinita Baniwal, Harshad Khadilkar:
Anticipatory Decisions in Retail E-Commerce Warehouses using Reinforcement Learning. COMAD/CODS 2021: 272-280 - [c22]Hardik Meisheri, Harshad Khadilkar:
FoLaR: Foggy Latent Representations for Reinforcement Learning with Partial Observability. IJCNN 2021: 1-8 - [c21]Supratim Ghosh, Aritra Pal, Prashant Kumar, Ankush Ojha, Aditya A. Paranjape, Souvik Barat, Harshad Khadilkar:
A Simulation Driven Optimization Algorithm for Scheduling Sorting Center Operations. WSC 2021: 1-12 - 2019
- [c20]Vinita Baniwal, Chandrai Kayal, Dheeraj Shah, Padmakumar Ma, Harshad Khadilkar:
An Imitation Learning Approach for Computing Anticipatory Picking Decisions in Retail Distribution Centres. ACC 2019: 4186-4191 - [c19]Souvik Barat, Harshad Khadilkar, Hardik Meisheri, Vinay Kulkarni, Vinita Baniwal, Prashant Kumar, Monika Gajrani:
Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning. AAMAS 2019: 1802-1804 - [c18]Richa Verma, Sarmimala Saikia, Harshad Khadilkar, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan:
A Reinforcement Learning Framework for Container Selection and Ship Load Sequencing in Ports. AAMAS 2019: 2250-2252 - [c17]Souvik Barat, Prashant Kumar, Monika Gajrani, Harshad Khadilkar, Hardik Meisheri, Vinita Baniwal, Vinay Kulkarni:
Reinforcement Learning of Supply Chain Control Policy Using Closed Loop Multi-agent Simulation. MABS 2019: 26-38 - 2018
- [c16]Hardik Meisheri, Harshad Khadilkar:
Learning representations for sentiment classification using Multi-task framework. WASSA@EMNLP 2018: 299-308 - 2017
- [c15]Harshad Khadilkar:
Scheduling of vehicle movement in resource-constrained transportation networks using a capacity-aware heuristic. ACC 2017: 5617-5622 - 2016
- [c14]Harshad Khadilkar:
Modelling the impact of control strategy on stochastic delay propagation in transportation networks. ECC 2016: 2471-2476 - [c13]Megha Nawhal, Heena Bansal, Ashok Pon Kumar, Vikas Chandan, Sridhar R, Babitha Ramesh, Sunil Kumar Ghai, Harshad Khadilkar, Deva P. Seetharam, Zainul M. Charbiwala, Vijay Arya, Amith Singhee:
Unlocking the hidden potential of data towards efficient buildings: Findings from a pilot study in India. ISGT Europe 2016: 1-6 - [c12]Seema Nagar, Sandhya Aneja, Harshad Khadilkar, Sampath Dechu, Zainul Charbiwala:
SMOME: A framework for evaluating the costs and benefits of instrumentation in smart home systems. SmartGridComm 2016: 134-139 - 2015
- [c11]Sambaran Bandyopadhyay, Tanuja Ganu, Harshad Khadilkar, Vijay Arya:
Individual and Aggregate Electrical Load Forecasting: One for All and All for One. e-Energy 2015: 121-130 - [c10]Vikas Chandan, Mohit Jain, Harshad Khadilkar, Zainul Charbiwala, Anupam Jain, Sunil Kumar Ghai, Rajesh Kunnath, Deva P. Seetharam:
UrJar: A Device to Address Energy Poverty Using E-Waste. e-Energy 2015: 195-196 - [c9]Seema Nagar, Sandhya Aneja, Harshad Khadilkar, Sampath Dechu, Zainul Charbiwala:
A Framework for Evaluating the Costs and Benefits of Instrumentation in Smart Home Systems. e-Energy 2015: 221-222 - [c8]Pandarasamy Arjunan, Harshad D. Khadilkar, Tanuja Ganu, Zainul M. Charbiwala, Amarjeet Singh, Pushpendra Singh:
Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information. BuildSys@SenSys 2015: 35-44 - [c7]Harshad Khadilkar, Pratyush Kumar, Subendhu Rongali, Sampath Dechu, Pg Mohammad Iskandarbin Pg Hj Petra:
A socially aware incentive strategy for encouraging residential solar uptake in Brunei. SmartGridComm 2015: 545-550 - 2014
- [c6]Vikas Chandan, Mohit Jain, Harshad Khadilkar, Zainul Charbiwala, Anupam Jain, Sunil Kumar Ghai, Rajesh Kunnath, Deva P. Seetharam:
UrJar: A Lighting Solution using Discarded Laptop Batteries. ACM DEV 2014: 21-30 - [c5]Harshad Khadilkar, Tanuja Ganu, Zainul Charbiwala, Chee Ming Lim, Sathyajith Mathew, Deva P. Seetharam:
Algorithms for upgrading the resolution of aggregate energy meter data. e-Energy 2014: 277-288 - [c4]Mohit Jain, Harshad Khadilkar, Neha Sengupta, Zainul Charbiwala, Kushan U. Tennakoon, Rodzay bin Haji Abdul Wahab, Liyanage Chandratilake De Silva, Deva P. Seetharam:
Collaborative energy conservation in a microgrid. BuildSys@SenSys 2014: 130-139 - [c3]Harshad Khadilkar, Vikas Chandan, Sandeep Kalra, Sunil Kumar Ghai, Zainul Charbiwala, Tanuja Ganu, Rajesh Kunnath, Chee Ming Lim, Deva P. Seetharam:
DC Picogrids as power backups for office buildings. SmartGridComm 2014: 157-162 - 2013
- [c2]Harshad Khadilkar, Hamsa Balakrishnan:
Optimal control of airport operations with gate capacity constraints. ECC 2013: 608-613 - 2012
- [c1]Harshad Khadilkar, Hamsa Balakrishnan:
A network congestion control approach to airport departure management. ACC 2012: 1682-1688
Informal and Other Publications
- 2024
- [i26]Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju, Harshad Khadilkar, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Shekhar Singh, Muthusamy Chelliah, Nikesh Garera:
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization. CoRR abs/2402.15473 (2024) - [i25]Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya:
Transformers are Expressive, But Are They Expressive Enough for Regression? CoRR abs/2402.15478 (2024) - [i24]Aditya Kapoor, Harshad Khadilkar, Jayavardhana Gubbi:
DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions. CoRR abs/2407.16302 (2024) - 2023
- [i23]Harshad Khadilkar:
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas. CoRR abs/2305.07571 (2023) - [i22]Pranavi Pathakota, Hardik Meisheri, Harshad Khadilkar:
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces. CoRR abs/2306.15913 (2023) - [i21]Harshad Khadilkar:
Using Linear Regression for Iteratively Training Neural Networks. CoRR abs/2307.05189 (2023) - [i20]Durgesh Kalwar, Omkar Shelke, Harshad Khadilkar:
Using General Value Functions to Learn Domain-Backed Inventory Management Policies. CoRR abs/2311.02125 (2023) - [i19]Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan, Harshad Khadilkar, Hardik Meisheri, Balaraman Ravindran:
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce. CoRR abs/2311.16171 (2023) - [i18]Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya:
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning. CoRR abs/2311.17514 (2023) - 2022
- [i17]Somjit Nath, Omkar Shelke, Durgesh Kalwar, Hardik Meisheri, Harshad Khadilkar:
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning. CoRR abs/2203.00874 (2022) - [i16]Hardik Meisheri, Somjit Nath, Mayank Baranwal, Harshad Khadilkar:
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management. CoRR abs/2203.00885 (2022) - [i15]Harshad Khadilkar:
Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT. CoRR abs/2206.06618 (2022) - [i14]Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash, Harshad Khadilkar, Chetan Arora:
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions. CoRR abs/2207.13916 (2022) - [i13]Harshad Khadilkar, Hardik Meisheri:
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning. CoRR abs/2210.17296 (2022) - 2021
- [i12]Omkar Shelke, Hardik Meisheri, Harshad Khadilkar:
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget. CoRR abs/2102.11762 (2021) - [i11]Nazneen N. Sultana, Vinita Baniwal, Ansuma Basumatary, Piyush Mittal, Supratim Ghosh, Harshad Khadilkar:
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows. CoRR abs/2102.12088 (2021) - [i10]Somjit Nath, Mayank Baranwal, Harshad Khadilkar:
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays. CoRR abs/2108.07555 (2021) - [i9]Supratim Ghosh, Aritra Pal, Prashant Kumar, Ankush Ojha, Aditya A. Paranjape, Souvik Barat, Harshad Khadilkar:
A simulation driven optimization algorithm for scheduling sorting center operations. CoRR abs/2112.04567 (2021) - [i8]Pranavi Pathakota, Kunwar Zaid, Anulekha Dhara, Hardik Meisheri, Shaun D'Souza, Dheeraj Shah, Harshad Khadilkar:
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce. CoRR abs/2112.08736 (2021) - 2020
- [i7]Harshad Khadilkar, Tanuja Ganu, Deva P. Seetharam:
Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning. CoRR abs/2003.14093 (2020) - [i6]Somjit Nath, Richa Verma, Abhik Ray, Harshad Khadilkar:
SIBRE: Self Improvement Based REwards for Reinforcement Learning. CoRR abs/2004.09846 (2020) - [i5]Nazneen N. Sultana, Hardik Meisheri, Vinita Baniwal, Somjit Nath, Balaraman Ravindran, Harshad Khadilkar:
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains. CoRR abs/2006.04037 (2020) - [i4]Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary, Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha:
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing. CoRR abs/2007.00463 (2020) - [i3]Hardik Meisheri, Harshad Khadilkar:
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication. CoRR abs/2011.00424 (2020) - 2019
- [i2]Hardik Meisheri, Vinita Baniwal, Nazneen N. Sultana, Balaraman Ravindran, Harshad Khadilkar:
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems. CoRR abs/1910.00211 (2019) - [i1]Hardik Meisheri, Omkar Shelke, Richa Verma, Harshad Khadilkar:
Accelerating Training in Pommerman with Imitation and Reinforcement Learning. CoRR abs/1911.04947 (2019)
Coauthor Index
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