| Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... arXiv preprint arXiv:1711.05851, 2017 | 424 | 2017 |
| Deep batch active learning by diverse, uncertain gradient lower bounds JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal arXiv preprint arXiv:1906.03671, 2019 | 381 | 2019 |
| Contextual decision processes with low bellman rank are pac-learnable N Jiang, A Krishnamurthy, A Agarwal, J Langford, RE Schapire International Conference on Machine Learning, 1704-1713, 2017 | 320 | 2017 |
| Parallelised Bayesian optimisation via Thompson sampling K Kandasamy, A Krishnamurthy, J Schneider, B Póczos International Conference on Artificial Intelligence and Statistics, 133-142, 2018 | 217* | 2018 |
| Learning to search better than your teacher KW Chang, A Krishnamurthy, A Agarwal, J Langford, H Daumé III International Conference on Machine Learning, 2015, 2015 | 202 | 2015 |
| Low-rank matrix and tensor completion via adaptive sampling A Krishnamurthy, A Singh Advances in neural information processing systems 26, 2013 | 174 | 2013 |
| Off-policy evaluation for slate recommendation A Swaminathan, A Krishnamurthy, A Agarwal, M Dudik, J Langford, ... Advances in Neural Information Processing Systems 30, 2017 | 168 | 2017 |
| Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches W Sun, N Jiang, A Krishnamurthy, A Agarwal, J Langford Conference on learning theory, 2898-2933, 2019 | 166* | 2019 |
| Provably efficient rl with rich observations via latent state decoding S Du, A Krishnamurthy, N Jiang, A Agarwal, M Dudik, J Langford International Conference on Machine Learning, 1665-1674, 2019 | 163 | 2019 |
| DEGAS: de novo discovery of dysregulated pathways in human diseases I Ulitsky, A Krishnamurthy, RM Karp, R Shamir PloS one 5 (10), e13367, 2010 | 134 | 2010 |
| Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020 | 132 | 2020 |
| Flambe: Structural complexity and representation learning of low rank mdps A Agarwal, S Kakade, A Krishnamurthy, W Sun Advances in neural information processing systems 33, 20095-20107, 2020 | 132 | 2020 |
| PAC reinforcement learning with rich observations A Krishnamurthy, A Agarwal, J Langford Advances in Neural Information Processing Systems 29, 2016 | 130 | 2016 |
| Kinematic state abstraction and provably efficient rich-observation reinforcement learning D Misra, M Henaff, A Krishnamurthy, J Langford International conference on machine learning, 6961-6971, 2020 | 112 | 2020 |
| Optimism in reinforcement learning with generalized linear function approximation Y Wang, R Wang, SS Du, A Krishnamurthy arXiv preprint arXiv:1912.04136, 2019 | 110 | 2019 |
| Nonparametric von mises estimators for entropies, divergences and mutual informations K Kandasamy, A Krishnamurthy, B Poczos, L Wasserman Advances in Neural Information Processing Systems 28, 2015 | 109 | 2015 |
| Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems 24, 2011 | 109 | 2011 |
| Contrastive learning, multi-view redundancy, and linear models C Tosh, A Krishnamurthy, D Hsu Algorithmic Learning Theory, 1179-1206, 2021 | 92 | 2021 |
| Efficient active algorithms for hierarchical clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh arXiv preprint arXiv:1206.4672, 2012 | 92 | 2012 |
| A hierarchical algorithm for extreme clustering A Kobren, N Monath, A Krishnamurthy, A McCallum Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 91 | 2017 |
