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Arun Sai Suggala
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2020 – today
- 2024
- [c24]Soumyabrata Pal, Milind Tambe, Arun Sai Suggala, Karthikeyan Shanmugam, Aparna Taneja:
Improving Mobile Maternal and Child Health Care Programs: Collaborative Bandits for Time Slot Selection. AAMAS 2024: 1540-1548 - [c23]Sanket Shah, Arun Sai Suggala, Milind Tambe, Aparna Taneja:
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-focused Learning. AAMAS 2024: 1701-1709 - [c22]Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan:
Second Order Methods for Bandit Optimization and Control. COLT 2024: 4691-4763 - [c21]Anshul Nasery, Hardik Shah, Arun Sai Suggala, Prateek Jain:
End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates. CVPR Workshops 2024: 5866-5877 - [i19]Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan:
Second Order Methods for Bandit Optimization and Control. CoRR abs/2402.08929 (2024) - [i18]Sanket Shah, Arun Sai Suggala, Milind Tambe, Aparna Taneja:
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning. CoRR abs/2403.05683 (2024) - [i17]Pranav Ajit Nair, Arun Sai Suggala:
CDQuant: Accurate Post-training Weight Quantization of Large Pre-trained Models using Greedy Coordinate Descent. CoRR abs/2406.17542 (2024) - 2023
- [c20]Paula Rodriguez Diaz, Jackson A. Killian, Lily Xu, Arun Sai Suggala, Aparna Taneja, Milind Tambe:
Flexible Budgets in Restless Bandits: A Primal-Dual Algorithm for Efficient Budget Allocation. AAAI 2023: 12103-12111 - [c19]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam
, Prateek Jain:
Optimal Algorithms for Latent Bandits with Cluster Structure. AISTATS 2023: 7540-7577 - [c18]Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar:
Responsible AI (RAI) Games and Ensembles. NeurIPS 2023 - [c17]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. NeurIPS 2023 - [c16]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. NeurIPS 2023 - [i16]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam
Optimal Algorithms for Latent Bandits with Cluster Structure. CoRR abs/2301.07040 (2023) - [i15]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Near Optimal Private and Robust Linear Regression. CoRR abs/2301.13273 (2023) - [i14]Anshul Nasery, Hardik Shah, Arun Sai Suggala, Prateek Jain:
End-to-End Neural Network Compression via 𝓁1/𝓁2 Regularized Latency Surrogates. CoRR abs/2306.05785 (2023) - [i13]Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam
Optimal Best-Arm Identification in Bandits with Access to Offline Data. CoRR abs/2306.09048 (2023) - [i12]Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj, Arun Sai Suggala:
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization. CoRR abs/2306.09222 (2023) - [i11]Yash Gupta, Runtian Zhai, Arun Sai Suggala, Pradeep Ravikumar:
Responsible AI (RAI) Games and Ensembles. CoRR abs/2310.18832 (2023) - [i10]Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain:
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints. CoRR abs/2311.03376 (2023) - 2022
- [b1]Arun Sai Suggala:
Statistical Game Theory. Carnegie Mellon University, USA, 2022 - [c15]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. ICML 2022: 26669-26692 - [i9]Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala:
Building Robust Ensembles via Margin Boosting. CoRR abs/2206.03362 (2022) - 2021
- [c14]Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli:
Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT 2021: 4008-4067 - [c13]Runtian Zhai, Chen Dan, Arun Sai Suggala, J. Zico Kolter, Pradeep Ravikumar:
Boosted CVaR Classification. NeurIPS 2021: 21860-21871 - [i8]Runtian Zhai
Boosted CVaR Classification. CoRR abs/2110.13948 (2021) - 2020
- [c12]Arun Sai Suggala, Praneeth Netrapalli:
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. ALT 2020: 845-861 - [c11]Arun Sai Suggala, Bingbin Liu, Pradeep Ravikumar:
Generalized Boosting. NeurIPS 2020 - [c10]Arun Sai Suggala, Praneeth Netrapalli:
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. NeurIPS 2020 - [i7]Arun Sai Suggala, Praneeth Netrapalli:
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. CoRR abs/2006.07541 (2020) - [i6]Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar:
Learning Minimax Estimators via Online Learning. CoRR abs/2006.11430 (2020)
2010 – 2019
- 2019
- [c9]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
Revisiting Adversarial Risk. AISTATS 2019: 2331-2339 - [c8]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. COLT 2019: 2892-2897 - [c7]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
On the (In)fidelity and Sensitivity of Explanations. NeurIPS 2019: 10965-10976 - [i5]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
How Sensitive are Sensitivity-Based Explanations? CoRR abs/1901.09392 (2019) - [i4]Arun Sai Suggala, Praneeth Netrapalli:
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. CoRR abs/1903.08110 (2019) - [i3]Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain:
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. CoRR abs/1903.08192 (2019) - 2018
- [c6]Arun Sai Suggala, Adarsh Prasad, Pradeep Ravikumar:
Connecting Optimization and Regularization Paths. NeurIPS 2018: 10631-10641 - [i2]Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar:
Robust Estimation via Robust Gradient Estimation. CoRR abs/1802.06485 (2018) - [i1]Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar:
On Adversarial Risk and Training. CoRR abs/1806.02924 (2018) - 2017
- [c5]Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain:
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. ICML 2017: 1331-1340 - [c4]Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar:
Ordinal Graphical Models: A Tale of Two Approaches. ICML 2017: 3260-3269 - [c3]Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar:
Latent Feature Lasso. ICML 2017: 3949-3957 - [c2]Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar:
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities. NIPS 2017: 4446-4456 - 2015
- [c1]Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar:
Vector-Space Markov Random Fields via Exponential Families. ICML 2015: 684-692
Coauthor Index
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