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

Leveraging IP signaling and routing to manage UPSR-based transport networks. - Vishal Sharma; Abhimanyu Das; Charles Chen (ICC, 2003)

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)

Low-state fairness: lower bounds and practical enforcement. - Abhimanyu Das; Debojyoti Dutta; Ahmed Helmy; Ashish Goel; John S. Heidemann (INFOCOM, 2005)

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

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 001; Carl Oberg; Abhishek B. Sharma; Beth Stauffer; Gaurav S. Sukhatme; Bin Zhang 010 (International Conference on Computational Science (3), 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 (ICRA, 2006)

AMBROSia: An Autonomous Model-Based Reactive Observing System. - David A. Caron; Abhimanyu Das; Amit Dhariwal; Leana Golubchik; Ramesh Govindan; David Kempe 001; Carl Oberg; Abhishek B. Sharma; Beth Stauffer; Gaurav S. Sukhatme; Bin Zhang 010 (International Conference on Computational Science (1), 2007)

Sensor Selection for Minimizing Worst-Case Prediction Error. - Abhimanyu Das; David Kempe 001 (IPSN, 2008)

Algorithms for subset selection in linear regression. - Abhimanyu Das; David Kempe 001 (STOC, 2008)

Estimating the Average of a Lipschitz-Continuous Function from One Sample. - Abhimanyu Das; David Kempe 001 (ESA (1), 2010)

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection. - Abhimanyu Das; David Kempe 001 (ICML, 2011)

Web-scale multi-task feature selection for behavioral targeting. - Amr Ahmed 001; Mohamed Aly 002; Abhimanyu Das; Alexander J. Smola; Tasos Anastasakos (CIKM, 2012)

Factoring past exposure in display advertising targeting. - Neha Gupta 001; Abhimanyu Das; Sandeep Pandey; Vijay K. Narayanan (KDD, 2012)

Selecting Diverse Features via Spectral Regularization. - Abhimanyu Das; Anirban Dasgupta 001; Ravi Kumar 001 (NIPS, 2012)

Debiasing social wisdom. - Abhimanyu Das; Sreenivas Gollapudi; Rina Panigrahy; Mahyar Salek (KDD, 2013)

Discovering Topical Aspects in Microblogs. - Abhimanyu Das; Anitha Kannan (COLING, 2014)

Ranking Twitter discussion groups. - James Cook; Abhimanyu Das; Krishnaram Kenthapadi; Nina Mishra (COSN, 2014)

Role of conformity in opinion dynamics in social networks. - Abhimanyu Das; Sreenivas Gollapudi; Arindam Khan 001; Renato Paes Leme (COSN, 2014)

Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. - Amr Ahmed 001; Abhimanyu Das; Alexander J. Smola (WSDM, 2014)

Modeling opinion dynamics in social networks. - Abhimanyu Das; Sreenivas Gollapudi; Kamesh Munagala (WSDM, 2014)

Approximate Modularity. - Flavio Chierichetti; Abhimanyu Das; Anirban Dasgupta 001; Ravi Kumar 001 (FOCS, 2015)

Information dissemination in heterogeneous-intent networks. - Abhimanyu Das; Sreenivas Gollapudi; Emre Kiciman; Onur Varol (WebSci, 2016)

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

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

Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection. - Abhimanyu Das; David Kempe 001 (J. Mach. Learn. Res., 2018)

On the Learnability of Deep Random Networks. - Abhimanyu Das; Sreenivas Gollapudi; Ravi Kumar 001; Rina Panigrahy (CoRR, 2019)

On the Learnability of Random Deep Networks. - Abhimanyu Das; Sreenivas Gollapudi; Ravi Kumar 001; Rina Panigrahy (SODA, 2020)

Learning the gravitational force law and other analytic functions. - Atish Agarwala; Abhimanyu Das; Rina Panigrahy; Qiuyi Zhang 001 (CoRR, 2020)

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

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. - Atish Agarwala; Abhimanyu Das; Brendan Juba; Rina Panigrahy; Vatsal Sharan; Xin Wang 116; Qiuyi Zhang 001 (ICLR, 2021)

Robust Pure Exploration in Linear Bandits with Limited Budget. - Ayya Alieva; Ashok Cutkosky; Abhimanyu Das (ICML, 2021)

Dynamic Balancing for Model Selection in Bandits and RL. - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Claudio Gentile; Aldo Pacchiano; Manish Purohit (ICML, 2021)

A Convergence Analysis of Gradient Descent on Graph Neural Networks. - Pranjal Awasthi; Abhimanyu Das; Sreenivas Gollapudi (NeurIPS, 2021)

Hierarchically Regularized Deep Forecasting. - Biswajit Paria; Rajat Sen; Amr Ahmed 001; Abhimanyu Das (CoRR, 2021)

Beyond GNNs: An Efficient Architecture for Graph Problems. - Pranjal Awasthi; Abhimanyu Das; Sreenivas Gollapudi (AAAI, 2022)

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

On the benefits of maximum likelihood estimation for Regression and Forecasting. - Pranjal Awasthi; Abhimanyu Das; Rajat Sen; Ananda Theertha Suresh (ICLR, 2022)

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. - Pranjal Awasthi; Abhimanyu Das; Weihao Kong; Rajat Sen (NeurIPS, 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 001; Weihao Kong; Rajat Sen (ICML, 2023)

Blackbox optimization of unimodal functions. - Ashok Cutkosky; Abhimanyu Das; Weihao Kong; Chansoo Lee; Rajat Sen (UAI, 2023)

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. - Abhimanyu Das; Weihao Kong; Biswajit Paria; Rajat Sen (UAI, 2023)

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)

Transformers can optimally learn regression mixture models. - Reese Pathak; Rajat Sen; Weihao Kong; Abhimanyu Das (ICLR, 2024)

A decoder-only foundation model for time-series forecasting. - Abhimanyu Das; Weihao Kong; Rajat Sen; Yichen Zhou (ICML, 2024)

Linear Regression using Heterogeneous Data Batches. - Ayush Jain 001; Rajat Sen; Weihao Kong; Abhimanyu Das; Alon Orlitsky (NeurIPS, 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)