Transformers can optimally learn regression mixture models - Reese Pathak; Rajat Sen; Weihao Kong; Abhimanyu Das (The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, 2024)

A decoder-only foundation model for time-series forecasting - Abhimanyu Das; Weihao Kong; Rajat Sen; Yichen Zhou (Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, 2024)

Linear Regression using Heterogeneous Data Batches - Ayush Jain; Rajat Sen; Weihao Kong; Abhimanyu Das; Alon Orlitsky (Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024, 2024)

In-Context Fine-Tuning for Time-Series Foundation Models - Abhimanyu Das; Matthew Faw; Rajat Sen; Yichen Zhou (CoRR, 2024)

Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data - Kai Kim; Howard Tsai; Rajat Sen; Abhimanyu Das; Zihao Zhou; Abhishek Tanpure; Mathew Luo; Rose Yu (CoRR, 2024)

Long-term Forecasting with TiDE: Time-series Dense Encoder - Abhimanyu Das; Weihao Kong; Andrew Leach; Shaan Mathur; Rajat Sen; Rose Yu (Trans. Mach. Learn. Res., 2023)

Efficient List-Decodable Regression using Batches - Abhimanyu Das; Ayush Jain; Weihao Kong; Rajat Sen (International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, 2023)

Blackbox optimization of unimodal functions - Ashok Cutkosky; Abhimanyu Das; Weihao Kong; Chansoo Lee; Rajat Sen (Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA, 2023)

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting - Abhimanyu Das; Weihao Kong; Biswajit Paria; Rajat Sen (Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA, 2023)

Long-term Forecasting with TiDE: Time-series Dense Encoder - Abhimanyu Das; Weihao Kong; Andrew Leach; Shaan Mathur; Rajat Sen; Rose Yu (CoRR, 2023)

Linear Regression using Heterogeneous Data Batches - Ayush Jain; Rajat Sen; Weihao Kong; Abhimanyu Das; Alon Orlitsky (CoRR, 2023)

A decoder-only foundation model for time-series forecasting - Abhimanyu Das; Weihao Kong; Rajat Sen; Yichen Zhou (CoRR, 2023)

Transformers can optimally learn regression mixture models - Reese Pathak; Rajat Sen; Weihao Kong; Abhimanyu Das (CoRR, 2023)

Beyond GNNs: An Efficient Architecture for Graph Problems - Pranjal Awasthi; Abhimanyu Das; Sreenivas Gollapudi (Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, 2022)

Leveraging Initial Hints for Free in Stochastic Linear Bandits - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Qiuyi (Richard) Zhang (International Conference on Algorithmic Learning Theory, 29 March - 1 April 2022, Paris, France, 2022)

On the benefits of maximum likelihood estimation for Regression and Forecasting - Pranjal Awasthi; Abhimanyu Das; Rajat Sen; Ananda Theertha Suresh (The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, 2022)

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model - Pranjal Awasthi; Abhimanyu Das; Weihao Kong; Rajat Sen (Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, 2022)

Leveraging Initial Hints for Free in Stochastic Linear Bandits - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Qiuyi (Richard) Zhang (CoRR, 2022)

A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting - Abhimanyu Das; Weihao Kong; Biswajit Paria; Rajat Sen (CoRR, 2022)

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models - Weihao Kong; Rajat Sen; Pranjal Awasthi; Abhimanyu Das (CoRR, 2022)

Efficient List-Decodable Regression using Batches - Abhimanyu Das; Ayush Jain; Weihao Kong; Rajat Sen (CoRR, 2022)

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks - Atish Agarwala; Abhimanyu Das; Brendan Juba; Rina Panigrahy; Vatsal Sharan; Xin Wang; Qiuyi Zhang (9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, 2021)

Robust Pure Exploration in Linear Bandits with Limited Budget - Ayya Alieva; Ashok Cutkosky; Abhimanyu Das (Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, 2021)

Dynamic Balancing for Model Selection in Bandits and RL - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Claudio Gentile; Aldo Pacchiano; Manish Purohit (Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, 2021)

A Convergence Analysis of Gradient Descent on Graph Neural Networks - Pranjal Awasthi; Abhimanyu Das; Sreenivas Gollapudi (Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, 2021)

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks - Atish Agarwala; Abhimanyu Das; Brendan Juba; Rina Panigrahy; Vatsal Sharan; Xin Wang; Qiuyi Zhang (CoRR, 2021)

Hierarchically Regularized Deep Forecasting - Biswajit Paria; Rajat Sen; Amr Ahmed; Abhimanyu Das (CoRR, 2021)

On the benefits of maximum likelihood estimation for Regression and Forecasting - Pranjal Awasthi; Abhimanyu Das; Rajat Sen; Ananda Theertha Suresh (CoRR, 2021)

On the Learnability of Random Deep Networks - Abhimanyu Das; Sreenivas Gollapudi; Ravi Kumar; Rina Panigrahy (Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, 2020)

Learning the gravitational force law and other analytic functions - Atish Agarwala; Abhimanyu Das; Rina Panigrahy; Qiuyi Zhang (CoRR, 2020)

Upper Confidence Bounds for Combining Stochastic Bandits - Ashok Cutkosky; Abhimanyu Das; Manish Purohit (CoRR, 2020)

On the Learnability of Deep Random Networks - Abhimanyu Das; Sreenivas Gollapudi; Ravi Kumar; Rina Panigrahy (CoRR, 2019)

Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection - Abhimanyu Das; David Kempe (J. Mach. Learn. Res., 2018)

Minimizing Latency in Online Ride and Delivery Services - Abhimanyu Das; Sreenivas Gollapudi; Anthony Kim; Debmalya Panigrahi; Chaitanya Swamy (Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23-27, 2018, 2018)

Minimizing Latency in Online Ride and Delivery Services - Abhimanyu Das; Sreenivas Gollapudi; Anthony Kim; Debmalya Panigrahi; Chaitanya Swamy (CoRR, 2018)

Information dissemination in heterogeneous-intent networks - Abhimanyu Das; Sreenivas Gollapudi; Emre Kiciman; Onur Varol (Proceedings of the 8th ACM Conference on Web Science, WebSci 2016, Hannover, Germany, May 22-25, 2016, 2016)

STEPS: Predicting place attributes via spatio-temporal analysis - Shuxin Nie; Abhimanyu Das; Evgeniy Gabrilovich; Wei-Lwun Lu; Boris Mazniker; Chris Schilling (CoRR, 2016)

Approximate Modularity - Flavio Chierichetti; Abhimanyu Das; Anirban Dasgupta; Ravi Kumar (IEEE 56th Annual Symposium on Foundations of Computer Science, FOCS 2015, Berkeley, CA, USA, 17-20 October, 2015, 2015)

Discovering Topical Aspects in Microblogs - Abhimanyu Das; Anitha Kannan (COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin, Ireland, 2014)

Role of conformity in opinion dynamics in social networks - Abhimanyu Das; Sreenivas Gollapudi; Arindam Khan; Renato Paes Leme (Proceedings of the second ACM conference on Online social networks, COSN 2014, Dublin, Ireland, October 1-2, 2014, 2014)

Ranking Twitter discussion groups - James Cook; Abhimanyu Das; Krishnaram Kenthapadi; Nina Mishra (Proceedings of the second ACM conference on Online social networks, COSN 2014, Dublin, Ireland, October 1-2, 2014, 2014)

Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising - Amr Ahmed; Abhimanyu Das; Alexander J. Smola (Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, 2014)

Modeling opinion dynamics in social networks - Abhimanyu Das; Sreenivas Gollapudi; Kamesh Munagala (Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, 2014)

Debiasing social wisdom - Abhimanyu Das; Sreenivas Gollapudi; Rina Panigrahy; Mahyar Salek (The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, 2013)

Web-scale multi-task feature selection for behavioral targeting - Amr Ahmed; Mohamed Aly; Abhimanyu Das; Alexander J. Smola; Tasos Anastasakos (21st ACM International Conference on Information and Knowledge Management, CIKM'12, Maui, HI, USA, October 29 - November 02, 2012, 2012)

Factoring past exposure in display advertising targeting - Neha Gupta; Abhimanyu Das; Sandeep Pandey; Vijay K. Narayanan (The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, Beijing, China, August 12-16, 2012, 2012)

Selecting Diverse Features via Spectral Regularization - Abhimanyu Das; Anirban Dasgupta; Ravi Kumar (Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012)

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection - Abhimanyu Das; David Kempe (Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, 2011)

Estimating the Average of a Lipschitz-Continuous Function from One Sample - Abhimanyu Das; David Kempe (CoRR, 2011)

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection - Abhimanyu Das; David Kempe (CoRR, 2011)

Estimating the Average of a Lipschitz-Continuous Function from One Sample - Abhimanyu Das; David Kempe (Algorithms - ESA 2010, 18th Annual European Symposium, Liverpool, UK, September 6-8, 2010. Proceedings, Part I, 2010)

Sensor Selection for Minimizing Worst-Case Prediction Error - Abhimanyu Das; David Kempe (Proceedings of the 7th International Conference on Information Processing in Sensor Networks, IPSN 2008, St. Louis, Missouri, USA, April 22-24, 2008, 2008)

Algorithms for subset selection in linear regression - Abhimanyu Das; David Kempe (Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia, Canada, May 17-20, 2008, 2008)

AMBROSia: An Autonomous Model-Based Reactive Observing System - 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 (Computational Science - ICCS 2007, 7th International Conference Beijing, China, May 27-30, 2007, Proceedings, Part I, 2007)

A Generic Multi-scale Modeling Framework for Reactive Observing Systems: An Overview - 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 (Computational Science - ICCS 2006, 6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part III, 2006)

Adaptive Torque Control of Electro-rheological Fluid Brakes used in Active Knee Rehabilitation Devices - Jason Nikitczuk; Abhimanyu Das; Harsh Vyas; Brian Weinberg; Constantinos Mavroidis (Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, May 15-19, 2006, Orlando, Florida, USA, 2006)

Data acquisition in multiple-sink sensor networks - Abhimanyu Das; Debojyoti Dutta (ACM SIGMOBILE Mob. Comput. Commun. Rev., 2005)

Low-state fairness: lower bounds and practical enforcement - Abhimanyu Das; Debojyoti Dutta; Ahmed Helmy; Ashish Goel; John S. Heidemann (INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 13-17 March 2005, Miami, FL, USA, 2005)

A low-state packet marking framework for approximate fair bandwidth allocation - Abhimanyu Das; Debojyoti Dutta; Ahmed Helmy (IEEE Commun. Lett., 2004)

On the Issues in Implementing the Peer Model in Integrated Optical Networks - Vishal Sharma; Abhimanyu Das; Charles Chen (Photonic Netw. Commun., 2004)

Data acquisition in multiple-sink sensor networks - Abhimanyu Das; Debojyoti Dutta (Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, SenSys 2004, Baltimore, MD, USA, November 3-5, 2004, 2004)

Leveraging IP signaling and routing to manage UPSR-based transport networks - Vishal Sharma; Abhimanyu Das; Charles Chen (Proceedings of IEEE International Conference on Communications, ICC 2003, Anchorage, Alaska, USA, 11-15 May, 2003, 2003)

Fair Stateless Aggregate Traffic Marking Using Active Queue Management Techniques - Abhimanyu Das; Debojyoti Dutta; Ahmed Helmy (Management of Multimedia on the Internet, 5th IFIP/IEEE International Conference on Management of Multimedia Networks and Services, MMNS 2002, Santa Barbara, CA, USA, October 6-9, 2002, Proceedings, 2002)

Fair Stateless Aggregate Traffic Marking using Active Queue Management Techniques - Abhimanyu Das; Debojyoti Dutta; Ahmed Helmy (CoRR, 2002)