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ORCIDBalaraman Ravindran
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- affiliation: Indian Institute of Technology Madras
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2020 – today
- 2023
[c142]Kushal Chauhan, Soumya Chatterjee, Akash Reddy, Aniruddha S, Balaraman Ravindran, Pradeep Shenoy:
Matching Options to Tasks using Option-Indexed Hierarchical Reinforcement Learning. AAMAS 2023: 2631-2633- [i86]Aravind Venugopal, Stephanie Milani, Fei Fang, Balaraman Ravindran:
Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning. CoRR abs/2304.06011 (2023) - [i85]Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran:
Clustering Indices based Automatic Classification Model Selection. CoRR abs/2305.13926 (2023) - [i84]Returaj Burnwal, Anirban Santara, Nirav P. Bhatt, Balaraman Ravindran, Gaurav Aggarwal:
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts. CoRR abs/2305.19111 (2023) - 2022
- [j25]Balaraman Ravindran, Sunita Sarawagi, Aditi Jain:
AI and data science centers in top Indian academic institutions. Commun. ACM 65(11): 94-97 (2022) - [j24]Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran:
Scaling Graph Propagation Kernels for Predictive Learning. Frontiers Big Data 5: 616617 (2022) - [j23]Joseph H. R. Isaac
, Manivannan Muniyandi, Balaraman Ravindran:
Single Shot Corrective CNN for Anatomically Correct 3D Hand Pose Estimation. Frontiers Artif. Intell. 5: 759255 (2022) - [j22]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) - [j21]Saket Gurukar, Priyesh Vijayan, Srinivasan Parthasarathy, Balaraman Ravindran, Aakash Srinivasan, Goonmeet Bajaj, Chen Cai, Moniba Keymanesh, Saravana Kumar, Pranav Maneriker, Anasua Mitra, Vedang Patel:
Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress. Trans. Mach. Learn. Res. 2022 (2022) - [c141]Rishi Saket, Aravindan Raghuveer, Balaraman Ravindran:
On Combining Bags to Better Learn from Label Proportions. AISTATS 2022: 5913-5927 - [c140]Jay Nandy, Rishi Saket, Prateek Jain, Jatin Chauhan, Balaraman Ravindran, Aravindan Raghuveer:
Domain-Agnostic Contrastive Representations for Learning from Label Proportions. CIKM 2022: 1542-1551 - [c139]Pranshu Malviya, Balaraman Ravindran, Sarath Chandar:
TAG: Task-based Accumulated Gradients for Lifelong learning. CoLLAs 2022: 366-389 - [c138]Sai Kiran Narayanaswami, Nandan Sudarsanam, Balaraman Ravindran:
An Active Learning Framework for Efficient Robust Policy Search. COMAD/CODS 2022: 1-9 - [c137]Sapana Chaudhary, Balaraman Ravindran:
Smooth Imitation Learning via Smooth Costs and Smooth Policies. COMAD/CODS 2022: 63-71 - [c136]Sruthikeerthi Nandita, Goutham Zampani, Gokul S. Krishnan, Gitakrishnan Ramadurai, Balaraman Ravindran:
Automated Incident Location Identification for EMS from Ambulance Geospatial Data. COMAD/CODS 2022: 162-168 - [c135]Manoj Bharadhwaj, Gitakrishnan Ramadurai, Balaraman Ravindran:
Detecting Vehicles on the Edge: Knowledge Distillation to Improve Performance in Heterogeneous Road Traffic. CVPR Workshops 2022: 3191-3197 - [c134]Harsha Kokel, Nikhilesh Prabhakar, Balaraman Ravindran, Erik Blasch, Prasad Tadepalli, Sriraam Natarajan:
Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion. FUSION 2022: 1-8 - [c133]Anasua Mitra, Priyesh Vijayan, Sanasam Ranbir Singh, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran:
Revisiting Link Prediction on Heterogeneous Graphs with a Multi-view Perspective. ICDM 2022: 358-367 - [c132]Chandrasekar Subramanian, Balaraman Ravindran:
Causal Contextual Bandits with Targeted Interventions. ICLR 2022 - [c131]Adam Zychowski, Jacek Mandziuk, Elizabeth Bondi, Aravind Venugopal, Milind Tambe, Balaraman Ravindran:
Evolutionary Approach to Security Games with Signaling. IJCAI 2022: 620-627 - [c130]Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran:
Multi-Variate Time Series Forecasting on Variable Subsets. KDD 2022: 76-86 - [i83]Shreya Goyal, Sumanth Doddapaneni, Mitesh M. Khapra, Balaraman Ravindran:
A Survey in Adversarial Defences and Robustness in NLP. CoRR abs/2203.06414 (2022) - [i82]Adam Zychowski, Jacek Mandziuk, Elizabeth Bondi, Aravind Venugopal, Milind Tambe, Balaraman Ravindran:
Evolutionary Approach to Security Games with Signaling. CoRR abs/2204.14173 (2022) - [i81]Kushal Chauhan, Soumya Chatterjee, Akash Reddy, Balaraman Ravindran, Pradeep Shenoy:
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning. CoRR abs/2206.05750 (2022) - [i80]Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran:
Multi-Variate Time Series Forecasting on Variable Subsets. CoRR abs/2206.12626 (2022) - [i79]Jeshuren Chelladurai, Sudarsun Santhiappan, Balaraman Ravindran:
GrabQC: Graph based Query Contextualization for automated ICD coding. CoRR abs/2207.06802 (2022) - [i78]Neeraja Kirtane, Jeshuren Chelladurai, Balaraman Ravindran, Ashish V. Tendulkar:
ReGrAt: Regularization in Graphs using Attention to handle class imbalance. CoRR abs/2211.14770 (2022) - [i77]Adithya Ramesh, Balaraman Ravindran:
Physics-Informed Model-Based Reinforcement Learning. CoRR abs/2212.02179 (2022) - 2021
- [j20]Joseph Hosanna Raj Isaac, Manivannan Muniyandi, Balaraman Ravindran:
Corrective Filter Based on Kinematics of Human Hand for Pose Estimation. Frontiers Virtual Real. 2: 663618 (2021) - [j19]Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran:
MADRaS : Multi Agent Driving Simulator. J. Artif. Intell. Res. 70: 1517-1555 (2021) - [c129]Daksh Anand, Vaibhav Gupta, Praveen Paruchuri, Balaraman Ravindran:
An Enhanced Advising Model in Teacher-Student Framework using State Categorization. AAAI 2021: 6653-6660 - [c128]Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran:
Relational Boosted Bandits. AAAI 2021: 12123-12130 - [c127]Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran:
A Causal Approach for Unfair Edge Prioritization and Discrimination Removal. ACML 2021: 518-533 - [c126]Rajan Kumar Soni, Karthick Seshadri, Balaraman Ravindran:
Metric Learning for comparison of HMMs using Graph Neural Networks. ACML 2021: 1365-1380 - [c125]Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli:
RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction. ICAPS 2021: 533-541 - [c124]Karthik Visweswariah, Beethika Tripathi, Mitesh M. Khapra, Balaraman Ravindran:
A Joint Training Framework for Open-World Knowledge Graph Embeddings. AKBC 2021 - [c123]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS 2021: 1100-1108 - [c122]Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe:
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty. AAMAS 2021: 1353-1361 - [c121]Sudarsun Santhiappan, Balaraman Ravindran:
A semi-supervised approach to growing classification trees. COMAD/CODS 2021: 29-37 - [c120]Vaishnavi Muralidharan, Nandan Sudarsanam, Balaraman Ravindran:
Inferring customer occupancy status in for-hire vehicles using PU Learning. COMAD/CODS 2021: 290-298 - [c119]Sudarsun Santhiappan, Nitin Shravan, Balaraman Ravindran:
Is it hard to learn a classifier on this dataset? COMAD/CODS 2021: 299-306 - [c118]Anasua Mitra, Priyesh Vijayan, Sanasam Ranbir Singh, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran:
Semi-Supervised Deep Learning for Multiplex Networks. KDD 2021: 1234-1244 - [c117]Jeshuren Chelladurai, Sudarsun Santhiappan, Balaraman Ravindran:
GrabQC: Graph Based Query Contextualization for Automated ICD Coding. PAKDD (1) 2021: 225-237 - [i76]Jahnvi Patel, Devika Jay, Balaraman Ravindran, K. Shanti Swarup:
Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1. CoRR abs/2101.02456 (2021) - [i75]Nahas Pareekutty, Francis James, Balaraman Ravindran, Suril Vijaykumar Shah:
qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems. CoRR abs/2101.02635 (2021) - [i74]Deepak Maurya, Balaraman Ravindran:
Hyperedge Prediction using Tensor Eigenvalue Decomposition. CoRR abs/2102.04986 (2021) - [i73]Siddharth Nishtala, Lovish Madaan, Aditya Mate, Harshavardhan Kamarthi, Anirudh Grama, Divy Thakkar, Dhyanesh Narayanan, Suresh Chaudhary, Neha Madhiwalla, Ramesh Padmanabhan, Aparna Hegde, Pradeep Varakantham, Balaraman Ravindran, Milind Tambe:
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes. CoRR abs/2103.09052 (2021) - [i72]Pranshu Malviya, Balaraman Ravindran, Sarath Chandar:
TAG: Task-based Accumulated Gradients for Lifelong learning. CoRR abs/2105.05155 (2021) - [i71]Anasua Mitra, Priyesh Vijayan, Sanasam Ranbir Singh, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran:
Semi-Supervised Deep Learning for Multiplex Networks. CoRR abs/2110.02038 (2021) - [i70]Amrit Diggavi Seshadri, Balaraman Ravindran:
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis. CoRR abs/2110.08143 (2021) - [i69]Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli:
Dynamic probabilistic logic models for effective abstractions in RL. CoRR abs/2110.08318 (2021) - [i68]Sapana Chaudhary, Balaraman Ravindran:
Smooth Imitation Learning via Smooth Costs and Smooth Policies. CoRR abs/2111.02354 (2021) - [i67]Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran:
A Causal Approach for Unfair Edge Prioritization and Discrimination Removal. CoRR abs/2111.14348 (2021) - 2020
- [j18]Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, Balaraman Ravindran:
Hypergraph clustering by iteratively reweighted modularity maximization. Appl. Netw. Sci. 5(1): 52 (2020) - [j17]Sangameshwar Patil, Balaraman Ravindran:
Predicting software defect type using concept-based classification. Empir. Softw. Eng. 25(2): 1341-1378 (2020) - [j16]Abhishek Ghose, Balaraman Ravindran:
Interpretability With Accurate Small Models. Frontiers Artif. Intell. 3: 3 (2020) - [j15]Nandan Sudarsanam, Nishanth Kumar, Abhishek Sharma, Balaraman Ravindran:
Rate of change analysis for interestingness measures. Knowl. Inf. Syst. 62(1): 239-258 (2020) - [j14]Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. ACM Trans. Embed. Comput. Syst. 19(3): 16:1-16:24 (2020) - [c116]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI 2020: 13905-13906 - [c115]Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran:
Towards Transparent and Explainable Attention Models. ACL 2020: 4206-4216 - [c114]Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe:
Influence Maximization in Unknown Social Networks: Learning Policies for Effective Graph Sampling. AAMAS 2020: 575-583 - [c113]Anirban Santara, Rishabh Madan, Pabitra Mitra, Balaraman Ravindran:
ExTra: Transfer-guided Exploration. AAMAS 2020: 1987-1989 - [c112]Siddharth Nayak, Balaraman Ravindran:
Reinforcement Learning for Improving Object Detection. ECCV Workshops (5) 2020: 149-161 - [c111]Sanchari Sen, Balaraman Ravindran, Anand Raghunathan:
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks. ICLR 2020 - [c110]Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K. Madhava Krishna, Balaraman Ravindran, Anoop M. Namboodiri:
Understanding Dynamic Scenes using Graph Convolution Networks. IROS 2020: 8279-8286 - [c109]Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K. Madhava Krishna, Balaraman Ravindran, Anoop M. Namboodiri:
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks. IV 2020: 321-327 - [c108]Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran:
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning. ECML/PKDD (2) 2020: 509-524 - [c107]Anasua Mitra, Priyesh Vijayan, Srinivasan Parthasarathy, Balaraman Ravindran:
A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs. SDM 2020: 487-495 - [c106]Tarun Kumar, K. Darwin, Srinivasan Parthasarathy, Balaraman Ravindran:
HPRA: Hyperedge Prediction using Resource Allocation. WebSci 2020: 135-143 - [i66]Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR abs/2001.05209 (2020) - [i65]Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K. Madhava Krishna, Balaraman Ravindran, Anoop M. Namboodiri:
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks. CoRR abs/2002.00786 (2020) - [i64]Sanchari Sen, Balaraman Ravindran, Anand Raghunathan:
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks. CoRR abs/2004.10162 (2020) - [i63]Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran:
Towards Transparent and Explainable Attention Models. CoRR abs/2004.14243 (2020) - [i62]Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K. Madhava Krishna, Balaraman Ravindran, Anoop M. Namboodiri:
Understanding Dynamic Scenes using Graph Convolution Networks. CoRR abs/2005.04437 (2020) - [i61]Nikita Moghe, Priyesh Vijayan, Balaraman Ravindran, Mitesh M. Khapra:
On Incorporating Structural Information to improve Dialogue Response Generation. CoRR abs/2005.14315 (2020) - [i60]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) - [i59]Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran, Milind Tambe:
Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement. CoRR abs/2006.07590 (2020) - [i58]Tarun Kumar, K. Darwin, Srinivasan Parthasarathy, Balaraman Ravindran:
HPRA: Hyperedge Prediction using Resource Allocation. CoRR abs/2006.11070 (2020) - [i57]Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran:
A Causal Linear Model to Quantify Edge Unfairness for Unfair Edge Prioritization and Discrimination Removal. CoRR abs/2007.05516 (2020) - [i56]Siddharth Nayak, Balaraman Ravindran:
Reinforcement Learning for Improving Object Detection. CoRR abs/2008.08005 (2020) - [i55]Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran:
MADRaS : Multi Agent Driving Simulator. CoRR abs/2010.00993 (2020) - [i54]Anand A. Rajasekar, Karthik Raman, Balaraman Ravindran:
Goal directed molecule generation using Monte Carlo Tree Search. CoRR abs/2010.16399 (2020) - [i53]Deepak Maurya, Balaraman Ravindran:
Hypergraph Partitioning using Tensor Eigenvalue Decomposition. CoRR abs/2011.07683 (2020) - [i52]Ashutosh Kakadiya, Sriraam Natarajan, Balaraman Ravindran:
Relational Boosted Bandits. CoRR abs/2012.09220 (2020) - [i51]Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe:
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty. CoRR abs/2012.10389 (2020)
2010 – 2019
- 2019
- [j13]Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra, Balaraman Ravindran:
Studying the plasticity in deep convolutional neural networks using random pruning. Mach. Vis. Appl. 30(2): 203-216 (2019) - [c105]Manan Tomar, Akhil Sathuluri, Balaraman Ravindran:
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning. AAAI 2019: 10053-10054 - [c104]Sai Kiran Narayanaswami, Balaraman Ravindran, Venkatesh Ramaiyan:
Generalized random Surfer-Pair models. ASONAM 2019: 452-455 - [c103]Vaibhav Gupta, Daksh Anand, Praveen Paruchuri, Balaraman Ravindran:
Advice Replay Approach for Richer Knowledge Transfer in Teacher Student Framework. AAMAS 2019: 1997-1999 - [c102]Manan Tomar, Akhil Sathuluri, Balaraman Ravindran:
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning. AAMAS 2019: 2226-2228 - [c101]Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan:
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing. COMAD/CODS 2019: 150-156 - [c100]Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, Balaraman Ravindran:
A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering. COMPLEX NETWORKS (1) 2019: 286-297 - [c99]Manju Manohar Manjalavil, Gitakrishnan Ramadurai, Balaraman Ravindran:
Temporal Analysis of a Bus Transit Network. COMPLEX NETWORKS (2) 2019: 944-954 - [c98]Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran:
Let's Ask Again: Refine Network for Automatic Question Generation. EMNLP/IJCNLP (1) 2019: 3312-3321 - [c97]Rahul Ramesh, Manan Tomar, Balaraman Ravindran:
Successor Options: An Option Discovery Framework for Reinforcement Learning. IJCAI 2019: 3304-3310 - [c96]Revanth Reddy, Sarath Chandar, Balaraman Ravindran:
Edge Replacement Grammars : A Formal Language Approach for Generating Graphs. SDM 2019: 351-359 - [i50]Sai Kiran Narayanaswami, Nandan Sudarsanam, Balaraman Ravindran:
An Active Learning Framework for Efficient Robust Policy Search. CoRR abs/1901.00117 (2019) - [i49]Harish Kumar, Balaraman Ravindran:
Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning. CoRR abs/1902.01973 (2019) - [i48]Revanth Reddy, Sarath Chandar, Balaraman Ravindran:
Edge Replacement Grammars: A Formal Language Approach for Generating Graphs. CoRR abs/1902.07159 (2019) - [i47]Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan, Goonmeet Bajaj, Chen Cai, Moniba Keymanesh, Saravana Kumar, Pranav Maneriker, Anasua Mitra, Vedang Patel, Balaraman Ravindran, Srinivasan Parthasarathy:
Network Representation Learning: Consolidation and Renewed Bearing. CoRR abs/1905.00987 (2019) - [i46]Abhishek Ghose, Balaraman Ravindran:
Optimal Resampling for Learning Small Models. CoRR abs/1905.01520 (2019) - [i45]Rahul Ramesh, Manan Tomar, Balaraman Ravindran:
Successor Options: An Option Discovery Framework for Reinforcement Learning. CoRR abs/1905.05731 (2019) - [i44]Manan Tomar, Akhil Sathuluri, Balaraman Ravindran:
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning. CoRR abs/1905.07193 (2019) - [i43]Abhishek Ghose, Balaraman Ravindran:
Learning Interpretable Models Using an Oracle. CoRR abs/1906.06852 (2019) - [i42]Anirban Santara, Rishabh Madan, Balaraman Ravindran, Pabitra Mitra:
ExTra: Transfer-guided Exploration. CoRR abs/1906.11785 (2019) - [i41]Sai Kiran Narayanaswami, Balaraman Ravindran, Venkatesh Ramaiyan:
Generalized Random Surfer-Pair Models. CoRR abs/1907.01420 (2019) - [i40]Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe:
Learning policies for Social network discovery with Reinforcement learning. CoRR abs/1907.11625 (2019) - [i39]Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran:
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning. CoRR abs/1909.04134 (2019) - [i38]Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran:
Let's Ask Again: Refine Network for Automatic Question Generation. CoRR abs/1909.05355 (2019) - [i37]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) - 2018
- [j12]Nandan Sudarsanam, Balaraman Ravindran:
Using Linear Stochastic Bandits to extend traditional offline Designed Experiments to online settings. Comput. Ind. Eng. 115: 471-485 (2018) - [j11]Dibu John Philip, Nandan Sudarsanam, Balaraman Ravindran:
Improved Insights on Financial Health through Partially Constrained Hidden Markov Model Clustering on Loan Repayment Data. Data Base 49(3): 98-113 (2018) - [c95]Subhojyoti Mukherjee, K. P. Naveen, Nandan Sudarsanam, Balaraman Ravindran:
Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates. AAAI 2018: 6417-6424 - [c94]Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul:
RAIL: Risk-Averse Imitation Learning. AAMAS 2018: 2062-2063 - [c93]Sudarsun Santhiappan, Jeshuren Chelladurai, Balaraman Ravindran:
A novel topic modeling based weighting framework for class imbalance learning. COMAD/CODS 2018: 20-29 - [c92]Chandramohan T. N, Balaraman Ravindran:
A neural attention based approach for clickstream mining. COMAD/CODS 2018: 118-127 - [c91]Deepak Mittal, Avinash Reddy, Gitakrishnan Ramadurai, Kaushik Mitra, Balaraman Ravindran:
Training a deep learning architecture for vehicle detection using limited heterogeneous traffic data. COMSNETS 2018: 589-294 - [c90]Dibu John Philip, Nandan Sudarsanam, Balaraman Ravindran:
A Partial Parameter HMM Based Clustering on Loan Repayment Data: Insights into Financial Behavior and Intent to Repay. HICSS 2018: 1-10 - [c89]Balaraman Ravindran:
Looking Under the Hood of Deep Neural Networks. HiPC 2018: 1 - [c88]Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, Balaraman Ravindran:
Learning to Multi-Task by Active Sampling. ICLR (Poster) 2018 - [c87]