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2020 – today
- 2024
[c37]Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das:
Transformers can optimally learn regression mixture models. ICLR 2024- [c36]Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou:
A decoder-only foundation model for time-series forecasting. ICML 2024 - 2023
- [j5]Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu:
Long-term Forecasting with TiDE: Time-series Dense Encoder. Trans. Mach. Learn. Res. 2023 (2023) - [c35]Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. ICML 2023: 7025-7065 - [c34]Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen:
Blackbox optimization of unimodal functions. UAI 2023: 476-484 - [c33]Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen:
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. UAI 2023: 518-528 - [i19]Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu:
Long-term Forecasting with TiDE: Time-series Dense Encoder. CoRR abs/2304.08424 (2023) - [i18]Ayush Jain, Rajat Sen, Weihao Kong, Abhimanyu Das, Alon Orlitsky:
Linear Regression using Heterogeneous Data Batches. CoRR abs/2309.01973 (2023) - [i17]Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou:
A decoder-only foundation model for time-series forecasting. CoRR abs/2310.10688 (2023) - [i16]Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das:
Transformers can optimally learn regression mixture models. CoRR abs/2311.08362 (2023) - 2022
- [c32]Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi:
Beyond GNNs: An Efficient Architecture for Graph Problems. AAAI 2022: 6019-6027 - [c31]Ashok Cutkosky
, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang:
Leveraging Initial Hints for Free in Stochastic Linear Bandits. ALT 2022: 282-318 - [c30]Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh:
On the benefits of maximum likelihood estimation for Regression and Forecasting. ICLR 2022 - [c29]Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen:
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. NeurIPS 2022 - [i15]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang:
Leveraging Initial Hints for Free in Stochastic Linear Bandits. CoRR abs/2203.04274 (2022) - [i14]Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen:
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting. CoRR abs/2204.10414 (2022) - [i13]Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das:
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. CoRR abs/2206.04777 (2022) - [i12]Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. CoRR abs/2211.12743 (2022) - 2021
- [c28]Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang:
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. ICLR 2021 - [c27]Ayya Alieva, Ashok Cutkosky, Abhimanyu Das:
Robust Pure Exploration in Linear Bandits with Limited Budget. ICML 2021: 187-195 - [c26]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit:
Dynamic Balancing for Model Selection in Bandits and RL. ICML 2021: 2276-2285 - [c25]Pranjal Awasthi, Abhimanyu Das, Sreenivas Gollapudi:
A Convergence Analysis of Gradient Descent on Graph Neural Networks. NeurIPS 2021: 20385-20397 - [i11]Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang:
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. CoRR abs/2103.15261 (2021) - [i10]Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das:
Hierarchically Regularized Deep Forecasting. CoRR abs/2106.07630 (2021) - [i9]Pranjal Awasthi, Abhimanyu Das, Rajat Sen, Ananda Theertha Suresh:
On the benefits of maximum likelihood estimation for Regression and Forecasting. CoRR abs/2106.10370 (2021) - 2020
- [c24]Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy:
On the Learnability of Random Deep Networks. SODA 2020: 398-410 - [i8]Atish Agarwala, Abhimanyu Das, Rina Panigrahy, Qiuyi Zhang:
Learning the gravitational force law and other analytic functions. CoRR abs/2005.07724 (2020) - [i7]Ashok Cutkosky, Abhimanyu Das, Manish Purohit:
Upper Confidence Bounds for Combining Stochastic Bandits. CoRR abs/2012.13115 (2020)
2010 – 2019
- 2019
- [i6]Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy:
On the Learnability of Deep Random Networks. CoRR abs/1904.03866 (2019) - 2018
- [j4]Abhimanyu Das, David Kempe:
Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection. J. Mach. Learn. Res. 19: 3:1-3:34 (2018) - [c23]Abhimanyu Das, Sreenivas Gollapudi, Anthony Kim, Debmalya Panigrahi, Chaitanya Swamy:
Minimizing Latency in Online Ride and Delivery Services. WWW 2018: 379-388 - [i5]Abhimanyu Das, Sreenivas Gollapudi, Anthony Kim, Debmalya Panigrahi, Chaitanya Swamy:
Minimizing Latency in Online Ride and Delivery Services. CoRR abs/1802.02744 (2018) - 2016
- [c22]Abhimanyu Das, Sreenivas Gollapudi, Emre Kiciman, Onur Varol:
Information dissemination in heterogeneous-intent networks. WebSci 2016: 259-268 - [i4]Shuxin Nie, Abhimanyu Das, Evgeniy Gabrilovich, Wei-Lwun Lu, Boris Mazniker, Chris Schilling:
STEPS: Predicting place attributes via spatio-temporal analysis. CoRR abs/1610.07090 (2016) - 2015
- [c21]Flavio Chierichetti, Abhimanyu Das, Anirban Dasgupta, Ravi Kumar:
Approximate Modularity. FOCS 2015: 1143-1162 - 2014
- [c20]Abhimanyu Das, Anitha Kannan:
Discovering Topical Aspects in Microblogs. COLING 2014: 860-871 - [c19]Abhimanyu Das, Sreenivas Gollapudi, Arindam Khan, Renato Paes Leme:
Role of conformity in opinion dynamics in social networks. COSN 2014: 25-36 - [c18]James Cook, Abhimanyu Das, Krishnaram Kenthapadi, Nina Mishra:
Ranking Twitter discussion groups. COSN 2014: 177-190 - [c17]Amr Ahmed, Abhimanyu Das, Alexander J. Smola:
Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. WSDM 2014: 153-162 - [c16]Abhimanyu Das, Sreenivas Gollapudi, Kamesh Munagala:
Modeling opinion dynamics in social networks. WSDM 2014: 403-412 - 2013
- [c15]Abhimanyu Das, Sreenivas Gollapudi, Rina Panigrahy, Mahyar Salek:
Debiasing social wisdom. KDD 2013: 500-508 - 2012
- [c14]Amr Ahmed, Mohamed Aly, Abhimanyu Das, Alexander J. Smola, Tasos Anastasakos:
Web-scale multi-task feature selection for behavioral targeting. CIKM 2012: 1737-1741 - [c13]Neha Gupta, Abhimanyu Das, Sandeep Pandey, Vijay K. Narayanan:
Factoring past exposure in display advertising targeting. KDD 2012: 1204-1212 - [c12]Abhimanyu Das, Anirban Dasgupta, Ravi Kumar:
Selecting Diverse Features via Spectral Regularization. NIPS 2012: 1592-1600 - 2011
- [c11]Abhimanyu Das, David Kempe:
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection. ICML 2011: 1057-1064 - [i3]Abhimanyu Das, David Kempe:
Estimating the Average of a Lipschitz-Continuous Function from One Sample. CoRR abs/1101.3804 (2011) - [i2]Abhimanyu Das, David Kempe:
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection. CoRR abs/1102.3975 (2011) - 2010
- [c10]Abhimanyu Das, David Kempe:
Estimating the Average of a Lipschitz-Continuous Function from One Sample. ESA (1) 2010: 219-230
2000 – 2009
- 2008
- [c9]Abhimanyu Das, David Kempe:
Sensor Selection for Minimizing Worst-Case Prediction Error. IPSN 2008: 97-108 - [c8]Abhimanyu Das, David Kempe:
Algorithms for subset selection in linear regression. STOC 2008: 45-54 - 2007
- [c7]David A. Caron, Abhimanyu Das, Amit Dhariwal, Leana Golubchik, Ramesh Govindan, David Kempe, Carl Oberg, Abhishek B. Sharma, Beth Stauffer, Gaurav S. Sukhatme, Bin Zhang:
AMBROSia: An Autonomous Model-Based Reactive Observing System. International Conference on Computational Science (1) 2007: 995-1001 - 2006
- [c6]Leana Golubchik, David A. Caron, Abhimanyu Das, Amit Dhariwal, Ramesh Govindan, David Kempe, Carl Oberg, Abhishek B. Sharma, Beth Stauffer, Gaurav S. Sukhatme, Bin Zhang:
A Generic Multi-scale Modeling Framework for Reactive Observing Systems: An Overview. International Conference on Computational Science (3) 2006: 514-521 - [c5]Jason Nikitczuk, Abhimanyu Das, Harsh Vyas, Brian Weinberg, Constantinos Mavroidis:
Adaptive Torque Control of Electro-rheological Fluid Brakes used in Active Knee Rehabilitation Devices. ICRA 2006: 393-399 - 2005
- [j3]Abhimanyu Das, Debojyoti Dutta:
Data acquisition in multiple-sink sensor networks. ACM SIGMOBILE Mob. Comput. Commun. Rev. 9(3): 82-85 (2005) - [c4]Abhimanyu Das, Debojyoti Dutta, Ahmed Helmy, Ashish Goel, John S. Heidemann:
Low-state fairness: lower bounds and practical enforcement. INFOCOM 2005: 2436-2446 - 2004
- [j2]Abhimanyu Das, Debojyoti Dutta, Ahmed Helmy:
A low-state packet marking framework for approximate fair bandwidth allocation. IEEE Commun. Lett. 8(9): 588-590 (2004) - [j1]Vishal Sharma, Abhimanyu Das, Charles Chen:
On the Issues in Implementing the Peer Model in Integrated Optical Networks. Photonic Netw. Commun. 8(1): 7-21 (2004) - [c3]Abhimanyu Das, Debojyoti Dutta:
Data acquisition in multiple-sink sensor networks. SenSys 2004: 271-272 - 2003
- [c2]Vishal Sharma, Abhimanyu Das, Charles Chen:
Leveraging IP signaling and routing to manage UPSR-based transport networks. ICC 2003: 1268-1272 - 2002
- [c1]Abhimanyu Das, Debojyoti Dutta, Ahmed Helmy:
Fair Stateless Aggregate Traffic Marking Using Active Queue Management Techniques. MMNS 2002: 211-223 - [i1]Abhimanyu Das, Debojyoti Dutta, Ahmed Helmy:
Fair Stateless Aggregate Traffic Marking using Active Queue Management Techniques. CoRR cs.NI/0204011 (2002)
Coauthor Index
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