The Efficient Computation of Sparse Jacobian Matrices Using Automatic Differentiation. - Thomas F. Coleman; Arun Verma (SIAM J. Sci. Comput., 1998)

On Efficient Solutions to the Continuous Sensitivity Equation Using Automatic Differentiation. - Jeff Borggaard; Arun Verma (SIAM J. Sci. Comput., 2000)

ADMIT-1: automatic differentiation and MATLAB interface toolbox. - Thomas F. Coleman; Arun Verma (ACM Trans. Math. Softw., 2000)

A Preconditioned Conjugate Gradient Approach to Linear Equality Constrained Minimization. - Thomas F. Coleman; Arun Verma (Comput. Optim. Appl., 2001)

Benchmarking Fast-Data Platforms for the Aadhaar Biometric Database. - Yogesh L. Simmhan; Anshu Shukla; Arun Verma (WBDB, 2015)

Unsupervised cost sensitive predictions with side information. - Arun Verma; Manjesh Kumar Hanawal (COMAD/CODS, 2018)

Online Algorithm for Unsupervised Sensor Selection. - Arun Verma; Manjesh Kumar Hanawal; Csaba Szepesvári; Venkatesh Saligrama (AISTATS, 2019)

Visual Attention Model for Cross-Sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning. - Ran Zhao; Yuntian Deng; Mark Dredze; Arun Verma; David S. Rosenberg; Amanda Stent (FLAIRS, 2019)

Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback. - Arun Verma; Manjesh Kumar Hanawal; Arun Rajkumar; Raman Sankaran (NeurIPS, 2019)

Distributed Algorithms for Efficient Learning and Coordination in Ad Hoc Networks. - Arun Verma; Manjesh Kumar Hanawal; Rahul Vaze (WiOpt, 2019)

Thompson Sampling for Unsupervised Sequential Selection. - Arun Verma; Manjesh Kumar Hanawal; Nandyala Hemachandra (ACML, 2020)

Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis. - Arun Verma; Manjesh Kumar Hanawal; Nandyala Hemachandra (COMSNETS, 2020)

Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach. - Arun Verma; Manjesh Kumar Hanawal (INFOCOM, 2020)

Online Algorithm for Unsupervised Sequential Selection with Contextual Information. - Arun Verma; Manjesh Kumar Hanawal; Csaba Szepesvári; Venkatesh Saligrama (NeurIPS, 2020)

Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach. - Debamita Ghosh; Arun Verma; Manjesh Kumar Hanawal (SPCOM, 2020)

Stochastic Multi-Armed Bandits with Control Variates. - Arun Verma; Manjesh Kumar Hanawal (NeurIPS, 2021)

Censored Semi-Bandits for Resource Allocation. - Arun Verma; Manjesh Kumar Hanawal; Arun Rajkumar; Raman Sankaran (CoRR, 2021)

Bayesian Optimization under Stochastic Delayed Feedback. - Arun Verma; Zhongxiang Dai; Bryan Kian Hsiang Low (ICML, 2022)

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models. - Srijith Balakrishnan; Beatrice Cassottana; Arun Verma (CoRR, 2022)

Federated Neural Bandit. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (CoRR, 2022)

Sequential Decision Problems with Weak Feedback. - Arun Verma (CoRR, 2022)

FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. - Xinyi Xu; Zhaoxuan Wu; Arun Verma; Chuan Sheng Foo; Bryan Kian Hsiang Low (AISTATS, 2023)

Federated Neural Bandits. - Zhongxiang Dai; Yao Shu; Arun Verma; Flint Xiaofeng Fan; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2023)

Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. - Thanh Lam; Arun Verma; Bryan Kian Hsiang Low; Patrick Jaillet (ICLR, 2023)

Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. - Yao Shu; Zhongxiang Dai; Weicong Sng; Arun Verma; Patrick Jaillet; Bryan Kian Hsiang Low (ICLR, 2023)

Quantum Bayesian Optimization. - Zhongxiang Dai; Gregory Kang Ruey Lau; Arun Verma; Yao Shu; Bryan Kian Hsiang Low; Patrick Jaillet (NeurIPS, 2023)

Exploiting Correlated Auxiliary Feedback in Parameterized Bandits. - Arun Verma; Zhongxiang Dai; Yao Shu; Bryan Kian Hsiang Low (NeurIPS, 2023)

Position Paper: Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (EMNLP (Findings), 2024)

Prompt Optimization with Human Feedback. - Xiaoqiang Lin; Zhongxiang Dai; Arun Verma; See-Kiong Ng; Patrick Jaillet; Bryan Kian Hsiang Low (CoRR, 2024)

Data-Centric AI in the Age of Large Language Models. - Xinyi Xu; Zhaoxuan Wu; Rui Qiao 006; Arun Verma; Yao Shu; Jingtan Wang 001; Xinyuan Niu 001; Zhenfeng He; Jiangwei Chen 001; Zijian Zhou; Gregory Kang Ruey Lau; Hieu Dao; et al. (CoRR, 2024)

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models. - Ze Yu Zhang; Arun Verma; Finale Doshi-Velez; Bryan Kian Hsiang Low (CoRR, 2024)

Neural Dueling Bandits. - Arun Verma; Zhongxiang Dai; Xiaoqiang Lin; Patrick Jaillet; Bryan Kian Hsiang Low (CoRR, 2024)

Online Fair Division with Contextual Bandits. - Arun Verma; Indrajit Saha; Makoto Yokoo; Bryan Kian Hsiang Low (CoRR, 2024)

TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding. - Zhaoxuan Wu; Zijian Zhou; Arun Verma; Alok Prakash; Daniela Rus; Bryan Kian Hsiang Low (ACL (1), 2025)

Neural Dueling Bandits: Preference-Based Optimization with Human Feedback. - Arun Verma; Zhongxiang Dai; Xiaoqiang Lin; Patrick Jaillet; Bryan Kian Hsiang Low (ICLR, 2025)

Active Human Feedback Collection via Neural Contextual Dueling Bandits. - Arun Verma; Xiaoqiang Lin; Zhongxiang Dai; Daniela Rus; Bryan Kian Hsiang Low (CoRR, 2025)

ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment. - Xiaoqiang Lin; Arun Verma; Zhongxiang Dai; Daniela Rus; See-Kiong Ng; Bryan Kian Hsiang Low (CoRR, 2025)

COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents. - Arun Verma; Indrajit Saha; Makoto Yokoo; Bryan Kian Hsiang Low (CoRR, 2025)