Vector-Space Markov Random Fields via Exponential Families. - Wesley Tansey; Oscar Hernan Madrid Padilla; Arun Sai Suggala; Pradeep Ravikumar (ICML, 2015)

ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. - Chirag Gupta; Arun Sai Suggala; Ankit Goyal; Harsha Vardhan Simhadri; Bhargavi Paranjape; Ashish Kumar 007; Saurabh Goyal; Raghavendra Udupa; Manik Varma; Prateek Jain 002 (ICML, 2017)

Ordinal Graphical Models: A Tale of Two Approaches. - Arun Sai Suggala; Eunho Yang; Pradeep Ravikumar (ICML, 2017)

Latent Feature Lasso. - Ian En-Hsu Yen; Wei-Cheng Lee; Sung-En Chang; Arun Sai Suggala; Shou-De Lin; Pradeep Ravikumar (ICML, 2017)

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. - Arun Sai Suggala; Mladen Kolar; Pradeep Ravikumar (NIPS, 2017)

Connecting Optimization and Regularization Paths. - Arun Sai Suggala; Adarsh Prasad; Pradeep Ravikumar (NeurIPS, 2018)

Robust Estimation via Robust Gradient Estimation. - Adarsh Prasad; Arun Sai Suggala; Sivaraman Balakrishnan; Pradeep Ravikumar (CoRR, 2018)

On Adversarial Risk and Training. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (CoRR, 2018)

Revisiting Adversarial Risk. - Arun Sai Suggala; Adarsh Prasad; Vaishnavh Nagarajan; Pradeep Ravikumar (AISTATS, 2019)

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. - Arun Sai Suggala; Kush Bhatia; Pradeep Ravikumar; Prateek Jain 002 (COLT, 2019)

On the (In)fidelity and Sensitivity of Explanations. - Chih-Kuan Yeh; Cheng-Yu Hsieh; Arun Sai Suggala; David I. Inouye; Pradeep Ravikumar (NeurIPS, 2019)

How Sensitive are Sensitivity-Based Explanations? - Chih-Kuan Yeh; Cheng-Yu Hsieh; Arun Sai Suggala; David I. Inouye; Pradeep Ravikumar (CoRR, 2019)

Online Non-Convex Learning: Following the Perturbed Leader is Optimal. - Arun Sai Suggala; Praneeth Netrapalli (ALT, 2020)

Generalized Boosting. - Arun Sai Suggala; Bingbin Liu; Pradeep Ravikumar (NeurIPS, 2020)

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. - Arun Sai Suggala; Praneeth Netrapalli (NeurIPS, 2020)

Learning Minimax Estimators via Online Learning. - Kartik Gupta; Arun Sai Suggala; Adarsh Prasad; Praneeth Netrapalli; Pradeep Ravikumar (CoRR, 2020)

Efficient Bandit Convex Optimization: Beyond Linear Losses. - Arun Sai Suggala; Pradeep Ravikumar; Praneeth Netrapalli (COLT, 2021)

Boosted CVaR Classification. - Runtian Zhai; Chen Dan 001; Arun Sai Suggala; J. Zico Kolter; Pradeep Ravikumar (NeurIPS, 2021)

Building Robust Ensembles via Margin Boosting. - Dinghuai Zhang; Hongyang Zhang 001; Aaron C. Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala (ICML, 2022)

Statistical Game Theory. - Arun Sai Suggala (2022)

Flexible Budgets in Restless Bandits: A Primal-Dual Algorithm for Efficient Budget Allocation. - Paula Rodriguez Diaz; Jackson A. Killian; Lily Xu; Arun Sai Suggala; Aparna Taneja; Milind Tambe (AAAI, 2023)

Optimal Algorithms for Latent Bandits with Cluster Structure. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam; Prateek Jain 002 (AISTATS, 2023)

Responsible AI (RAI) Games and Ensembles. - Yash Gupta; Runtian Zhai; Arun Sai Suggala; Pradeep Ravikumar (NeurIPS, 2023)

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (NeurIPS, 2023)

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. - Soumyabrata Pal; Arun Sai Suggala; Karthikeyan Shanmugam; Prateek Jain 002 (NeurIPS, 2023)

Near Optimal Private and Robust Linear Regression. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (CoRR, 2023)

End-to-End Neural Network Compression via 𝓁1/𝓁2 Regularized Latency Surrogates. - Anshul Nasery; Hardik Shah; Arun Sai Suggala; Prateek Jain 002 (CoRR, 2023)

Optimal Best-Arm Identification in Bandits with Access to Offline Data. - Shubhada Agrawal; Sandeep Juneja 001; Karthikeyan Shanmugam; Arun Sai Suggala (CoRR, 2023)

Improving Mobile Maternal and Child Health Care Programs: Collaborative Bandits for Time Slot Selection. - Soumyabrata Pal; Milind Tambe; Arun Sai Suggala; Karthikeyan Shanmugam; Aparna Taneja (AAMAS, 2024)

Efficient Public Health Intervention Planning Using Decomposition-Based Decision-focused Learning. - Sanket Shah; Arun Sai Suggala; Milind Tambe; Aparna Taneja (AAMAS, 2024)

Second Order Methods for Bandit Optimization and Control. - Arun Sai Suggala; Y. Jennifer Sun; Praneeth Netrapalli; Elad Hazan (COLT, 2024)

End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates. - Anshul Nasery; Hardik Shah; Arun Sai Suggala; Prateek Jain 002 (CVPR Workshops, 2024)

Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD. - Aniket Das; Dheeraj Nagaraj; Soumyabrata Pal; Arun Sai Suggala; Prateek Varshney (NeurIPS, 2024)

Time-Reversal Provides Unsupervised Feedback to LLMs. - Yerram Varun; Rahul Madhavan; Sravanti Addepalli; Arun Sai Suggala; Karthikeyan Shanmugam; Prateek Jain 002 (NeurIPS, 2024)

CDQuant: Accurate Post-training Weight Quantization of Large Pre-trained Models using Greedy Coordinate Descent. - Pranav Ajit Nair; Arun Sai Suggala (CoRR, 2024)

Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program. - Arpan Dasgupta; Gagan Jain; Arun Sai Suggala; Karthikeyan Shanmugam; Milind Tambe; Aparna Taneja (CoRR, 2024)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization. - Ramnath Kumar; Kushal Majmundar; Dheeraj Mysore Nagaraj; Arun Sai Suggala (Trans. Mach. Learn. Res., 2024)

Offline-to-Online Hyperparameter Transfer for Stochastic Bandits. - Dravyansh Sharma; Arun Sai Suggala (AAAI, 2025)

Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts? - Sravanti Addepalli; Yerram Varun; Arun Sai Suggala; Karthikeyan Shanmugam; Prateek Jain 002 (ICLR, 2025)

Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health. - Arpan Dasgupta; Gagan Jain; Arun Sai Suggala; Karthikeyan Shanmugam; Milind Tambe; Aparna Taneja (AAMAS, 2025)

Online Bidding under RoS Constraints without Knowing the Value. - Sushant Vijayan; Zhe Feng 004; Swati Padmanabhan; Karthikeyan Shanmugam; Arun Sai Suggala; Di Wang 005 (WWW, 2025)

Robust Reward Modeling via Causal Rubrics. - Pragya Srivastava; Harman Singh; Rahul Madhavan; Gandharv Patil; Sravanti Addepalli; Arun Sai Suggala; Rengarajan Aravamudhan; Soumya Sharma; Anirban Laha; Aravindan Raghuveer; Karthikeyan Shanmugam; Doina Precup (CoRR, 2025)

Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health. - Arpan Dasgupta; Mizhaan Maniyar; Awadhesh Srivastava; Sanat Kumar; Amrita Mahale; Aparna Hedge; Arun Sai Suggala; Karthikeyan Shanmugam; Aparna Taneja; Milind Tambe (CoRR, 2025)

Regret minimization in Linear Bandits with offline data via extended D-optimal exploration. - Sushant Vijayan; Arun Sai Suggala; Karthikeyan Shanmugam; Soumyabrata Pal (CoRR, 2025)

A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial. - Amy A. Lee; Narayan Hegde; Nina Deliu; Emily Rosenzweig; Arun Sai Suggala; Sriram Lakshminarasimhan; Qian He; John Hernandez; Martin Seneviratne; Rahul Singh; Pradnesh Kalkar; Karthikeyan Shanmugam; et al. (CoRR, 2025)