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Dimitris S. Papailiopoulos
Dimitris Papailiopoulos
Person information
- affiliation: University of Wisconsin-Madison
- affiliation (former): University of California, Berkeley, AMPLab
- affiliation (former): University of Texas at Austin, Dept. of ECE
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2020 – today
- 2024
- [j14]Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee:
Mini-Batch Optimization of Contrastive Loss. Trans. Mach. Learn. Res. 2024 (2024) - [j13]Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, Kangwook Lee:
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding. Trans. Mach. Learn. Res. 2024 (2024) - [c63]Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos:
Teaching Arithmetic to Small Transformers. ICLR 2024 - [c62]Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos:
Looped Transformers are Better at Learning Learning Algorithms. ICLR 2024 - [c61]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. ICML 2024 - [c60]Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos:
Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks. ICML 2024 - [i66]Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos:
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks. CoRR abs/2402.04248 (2024) - [i65]Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee:
How Well Can Transformers Emulate In-context Newton's Method? CoRR abs/2403.03183 (2024) - [i64]Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu:
CHAI: Clustered Head Attention for Efficient LLM Inference. CoRR abs/2403.08058 (2024) - [i63]Zheyang Xiong, Vasilis Papageorgiou
, Kangwook Lee, Dimitris Papailiopoulos:
From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data. CoRR abs/2406.19292 (2024) - [i62]Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios G. Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos:
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition. CoRR abs/2410.05603 (2024) - 2023
- [c59]Gibbeum Lee, Volker Hartmann, Jongho Park, Dimitris Papailiopoulos, Kangwook Lee:
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation. ACL (Findings) 2023: 4536-4554 - [c58]Angeliki Giannou, Shashank Rajput, Dimitris Papailiopoulos:
The Expressive Power of Tuning Only the Normalization Layers. COLT 2023: 4130-4131 - [c57]Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos:
Looped Transformers as Programmable Computers. ICML 2023: 11398-11442 - [c56]Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak:
Transformers as Algorithms: Generalization and Stability in In-context Learning. ICML 2023: 19565-19594 - [c55]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. MLSys 2023 - [c54]Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, Samet Oymak:
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning. NeurIPS 2023 - [i61]Yingcong Li, Muhammed Emrullah Ildiz, Dimitris S. Papailiopoulos, Samet Oymak:
Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning. CoRR abs/2301.07067 (2023) - [i60]Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris S. Papailiopoulos:
Looped Transformers as Programmable Computers. CoRR abs/2301.13196 (2023) - [i59]Angeliki Giannou, Shashank Rajput, Dimitris S. Papailiopoulos:
The Expressive Power of Tuning Only the Norm Layers. CoRR abs/2302.07937 (2023) - [i58]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris S. Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. CoRR abs/2305.02538 (2023) - [i57]Gibbeum Lee, Volker Hartmann, Jongho Park, Dimitris Papailiopoulos, Kangwook Lee:
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation. CoRR abs/2305.04533 (2023) - [i56]Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris S. Papailiopoulos, Samet Oymak:
Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs. CoRR abs/2305.18869 (2023) - [i55]Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos:
Teaching Arithmetic to Small Transformers. CoRR abs/2307.03381 (2023) - [i54]Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee:
Mini-Batch Optimization of Contrastive Loss. CoRR abs/2307.05906 (2023) - [i53]Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris S. Papailiopoulos, Kangwook Lee:
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding. CoRR abs/2307.05908 (2023) - [i52]Liu Yang, Kangwook Lee, Robert D. Nowak, Dimitris Papailiopoulos:
Looped Transformers are Better at Learning Learning Algorithms. CoRR abs/2311.12424 (2023) - 2022
- [c53]Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos:
Finding Nearly Everything within Random Binary Networks. AISTATS 2022: 3531-3541 - [c52]Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris S. Papailiopoulos, Kangwook Lee:
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment. EMNLP (Findings) 2022: 154-168 - [c51]Shashank Rajput, Kangwook Lee, Dimitris S. Papailiopoulos:
Permutation-Based SGD: Is Random Optimal? ICLR 2022 - [c50]Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris S. Papailiopoulos, Kangwook Lee:
GenLabel: Mixup Relabeling using Generative Models. ICML 2022: 20278-20313 - [c49]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. MLSys 2022 - [c48]Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos, Kangwook Lee:
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks. NeurIPS 2022 - [c47]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. NeurIPS 2022 - [i51]Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris S. Papailiopoulos, Kangwook Lee:
GenLabel: Mixup Relabeling using Generative Models. CoRR abs/2201.02354 (2022) - [i50]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. CoRR abs/2202.12002 (2022) - [i49]Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris S. Papailiopoulos, Kangwook Lee:
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment. CoRR abs/2205.11616 (2022) - [i48]Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos, Kangwook Lee:
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks. CoRR abs/2206.06565 (2022) - [i47]Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris S. Papailiopoulos, Kangwook Lee, Robert D. Nowak:
A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets. CoRR abs/2210.03069 (2022) - 2021
- [c46]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys 2021 - [c45]Hongyi Wang, Saurabh Agarwal, Dimitris S. Papailiopoulos:
Pufferfish: Communication-efficient Models At No Extra Cost. MLSys 2021 - [c44]Shashank Rajput, Kartik Sreenivasan, Dimitris S. Papailiopoulos, Amin Karbasi:
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks. NeurIPS 2021: 12674-12685 - [i46]Shashank Rajput, Kangwook Lee, Dimitris S. Papailiopoulos:
Permutation-Based SGD: Is Random Optimal? CoRR abs/2102.09718 (2021) - [i45]Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. CoRR abs/2103.00543 (2021) - [i44]Hongyi Wang, Saurabh Agarwal, Dimitris S. Papailiopoulos:
Pufferfish: Communication-efficient Models At No Extra Cost. CoRR abs/2103.03936 (2021) - [i43]Shashank Rajput, Kartik Sreenivasan, Dimitris S. Papailiopoulos, Amin Karbasi:
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks. CoRR abs/2106.07724 (2021) - [i42]Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn, Dimitris S. Papailiopoulos:
Finding Everything within Random Binary Networks. CoRR abs/2110.08996 (2021) - 2020
- [j12]Waheed U. Bajwa
Machine Learning From Distributed, Streaming Data [From the Guest Editors]. IEEE Signal Process. Mag. 37(3): 11-13 (2020) - [c43]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. ICLR 2020 - [c42]Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos:
Closing the convergence gap of SGD without replacement. ICML 2020: 7964-7973 - [c41]Shengchao Liu, Dimitris S. Papailiopoulos, Dimitris Achlioptas:
Bad Global Minima Exist and SGD Can Reach Them. NeurIPS 2020 - [c40]Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient. NeurIPS 2020 - [c39]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. NeurIPS 2020 - [e1]Inderjit S. Dhillon, Dimitris S. Papailiopoulos, Vivienne Sze:
Proceedings of the Third Conference on Machine Learning and Systems, MLSys 2020, Austin, TX, USA, March 2-4, 2020. mlsys.org 2020 [contents] - [i41]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. CoRR abs/2002.06440 (2020) - [i40]Shashank Rajput, Anant Gupta, Dimitris S. Papailiopoulos:
Closing the convergence gap of SGD without replacement. CoRR abs/2002.10400 (2020) - [i39]Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris S. Papailiopoulos:
Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient. CoRR abs/2006.07990 (2020) - [i38]Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris S. Papailiopoulos:
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. CoRR abs/2007.05084 (2020) - [i37]Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. CoRR abs/2010.16248 (2020)
2010 – 2019
- 2019
- [c38]Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. AISTATS 2019: 1960-1968 - [c37]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? ICML 2019: 5321-5330 - [c36]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. NeurIPS 2019: 10320-10330 - [i36]Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding. CoRR abs/1901.09671 (2019) - [i35]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i34]Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? CoRR abs/1905.03177 (2019) - [i33]Zachary Charles, Shashank Rajput, Stephen J. Wright, Dimitris S. Papailiopoulos:
Convergence and Margin of Adversarial Training on Separable Data. CoRR abs/1905.09209 (2019) - [i32]Shengchao Liu, Dimitris S. Papailiopoulos, Dimitris Achlioptas:
Bad Global Minima Exist and SGD Can Reach Them. CoRR abs/1906.02613 (2019) - [i31]Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. CoRR abs/1907.12205 (2019) - 2018
- [j11]Kangwook Lee
Speeding Up Distributed Machine Learning Using Codes. IEEE Trans. Inf. Theory 64(3): 1514-1529 (2018) - [c35]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS 2018: 1998-2007 - [c34]Zachary Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. ICML 2018: 744-753 - [c33]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. ICML 2018: 902-911 - [c32]Zachary Charles, Dimitris S. Papailiopoulos:
Gradient Coding Using the Stochastic Block Model. ISIT 2018: 1998-2002 - [c31]Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris:
The Effect of Network Width on the Performance of Large-batch Training. NeurIPS 2018: 9322-9332 - [c30]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. NeurIPS 2018: 9872-9883 - [i30]Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos:
DRACO: Robust Distributed Training via Redundant Gradients. CoRR abs/1803.09877 (2018) - [i29]Zachary Charles, Dimitris S. Papailiopoulos:
Gradient Coding via the Stochastic Block Model. CoRR abs/1805.10378 (2018) - [i28]Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris:
The Effect of Network Width on the Performance of Large-batch Training. CoRR abs/1806.03791 (2018) - [i27]Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright:
ATOMO: Communication-efficient Learning via Atomic Sparsification. CoRR abs/1806.04090 (2018) - [i26]Zachary Charles, Harrison Rosenberg, Dimitris S. Papailiopoulos:
A Geometric Perspective on the Transferability of Adversarial Directions. CoRR abs/1811.03531 (2018) - [i25]Po-Ling Loh, Arya Mazumdar, Dimitris S. Papailiopoulos, Rüdiger L. Urbanke:
Coding Theory for Inference, Learning and Optimization (Dagstuhl Seminar 18112). Dagstuhl Reports 8(3): 60-73 (2018) - 2017
- [j10]Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. SIAM J. Optim. 27(4): 2202-2229 (2017) - [c29]Kangwook Lee, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Coded computation for multicore setups. ISIT 2017: 2413-2417 - [i24]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity Empowers Distributed Learning. CoRR abs/1706.05699 (2017) - [i23]Zachary Charles, Dimitris S. Papailiopoulos:
Stability and Generalization of Learning Algorithms that Converge to Global Optima. CoRR abs/1710.08402 (2017) - [i22]Zachary Charles, Dimitris S. Papailiopoulos, Jordan S. Ellenberg:
Approximate Gradient Coding via Sparse Random Graphs. CoRR abs/1711.06771 (2017) - 2016
- [j9]Ankit Singh Rawat
Locality and Availability in Distributed Storage. IEEE Trans. Inf. Theory 62(8): 4481-4493 (2016) - [j8]Itzhak Tamo, Dimitris S. Papailiopoulos, Alexandros G. Dimakis
Optimal Locally Repairable Codes and Connections to Matroid Theory. IEEE Trans. Inf. Theory 62(12): 6661-6671 (2016) - [c28]Megasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Bipartite Correlation Clustering: Maximizing Agreements. AISTATS 2016: 121-129 - [c27]Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Speeding up distributed machine learning using codes. ISIT 2016: 1143-1147 - [i21]Megasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Bipartite Correlation Clustering - Maximizing Agreements. CoRR abs/1603.02782 (2016) - [i20]Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht:
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR abs/1605.09721 (2016) - 2015
- [c26]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. NIPS 2015: 82-90 - [c25]Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Orthogonal NMF through Subspace Exploration. NIPS 2015: 343-351 - [c24]Megasthenis Asteris, Dimitris S. Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis:
Sparse PCA via Bipartite Matchings. NIPS 2015: 766-774 - [i19]Xinghao Pan, Dimitris S. Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Parallel Correlation Clustering on Big Graphs. CoRR abs/1507.05086 (2015) - [i18]Siu On Chan, Dimitris S. Papailiopoulos, Aviad Rubinstein:
On the Worst-Case Approximability of Sparse PCA. CoRR abs/1507.05950 (2015) - [i17]Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. CoRR abs/1507.06970 (2015) - [i16]Megasthenis Asteris, Dimitris S. Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis:
Sparse PCA via Bipartite Matchings. CoRR abs/1508.00625 (2015) - [i15]Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Speeding Up Distributed Machine Learning Using Codes. CoRR abs/1512.02673 (2015) - 2014
- [j7]Karthikeyan Shanmugam
A Repair Framework for Scalar MDS Codes. IEEE J. Sel. Areas Commun. 32(5): 998-1007 (2014) - [j6]Megasthenis Asteris, Dimitris S. Papailiopoulos, George N. Karystinos:
The Sparse Principal Component of a Constant-Rank Matrix. IEEE Trans. Inf. Theory 60(4): 2281-2290 (2014) - [j5]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Locally Repairable Codes. IEEE Trans. Inf. Theory 60(10): 5843-5855 (2014) - [c23]Dimitris S. Papailiopoulos, Megasthenis Asteris, Alexandros G. Dimakis:
Combinatorial QPs via a low-dimensional subspace sampling. CISS 2014: 1-3 - [c22]Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Nonnegative Sparse PCA with Provable Guarantees. ICML 2014: 1728-1736 - [c21]Dimitris S. Papailiopoulos, Ioannis Mitliagkas, Alexandros G. Dimakis, Constantine Caramanis:
Finding Dense Subgraphs via Low-Rank Bilinear Optimization. ICML 2014: 1890-1898 - [c20]Ankit Singh Rawat
On codes with availability for distributed storage. ISCCSP 2014: 15-18 - [c19]Ankit Singh Rawat
Locality and availability in distributed storage. ISIT 2014: 681-685 - [c18]Dimitris S. Papailiopoulos, Anastasios Kyrillidis, Christos Boutsidis:
Provable deterministic leverage score sampling. KDD 2014: 997-1006 - [i14]Ankit Singh Rawat, Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Sriram Vishwanath:
Locality and Availability in Distributed Storage. CoRR abs/1402.2011 (2014) - [i13]Dimitris S. Papailiopoulos, Anastasios Kyrillidis, Christos Boutsidis:
Provable Deterministic Leverage Score Sampling. CoRR abs/1404.1530 (2014) - 2013
- [j4]Maheswaran Sathiamoorthy, Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Ramkumar Vadali, Scott Chen, Dhruba Borthakur:
XORing Elephants: Novel Erasure Codes for Big Data. Proc. VLDB Endow. 6(5): 325-336 (2013) - [j3]Dimitris S. Papailiopoulos, Georgina Abou Elkheir, George N. Karystinos:
Maximum-Likelihood Noncoherent PAM Detection. IEEE Trans. Commun. 61(3): 1152-1159 (2013) - [c17]Ankit Singh Rawat
Availability and locality in distributed storage. GlobalSIP 2013: 923-928 - [c16]Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Stavros Korokythakis:
Sparse PCA through Low-rank Approximations. ICML (3) 2013: 747-755 - [c15]Itzhak Tamo, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Optimal locally repairable codes and connections to matroid theory. ISIT 2013: 1814-1818 - [i12]Maheswaran Sathiamoorthy, Megasthenis Asteris, Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Ramkumar Vadali, Scott Chen, Dhruba Borthakur:
XORing Elephants: Novel Erasure Codes for Big Data. CoRR abs/1301.3791 (2013) - [i11]Itzhak Tamo, Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Optimal Locally Repairable Codes and Connections to Matroid Theory. CoRR abs/1301.7693 (2013) - [i10]Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Stavros Korokythakis:
Sparse PCA through Low-rank Approximations. CoRR abs/1303.0551 (2013) - [i9]Karthikeyan Shanmugam, Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Giuseppe Caire:
A Repair Framework for Scalar MDS Codes. CoRR abs/1312.2135 (2013) - [i8]Megasthenis Asteris, Dimitris S. Papailiopoulos, George N. Karystinos:
The Sparse Principal Component of a Constant-rank Matrix. CoRR abs/1312.5891 (2013) - 2012
- [j2]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Interference Alignment as a Rank Constrained Rank Minimization. IEEE Trans. Signal Process. 60(8): 4278-4288 (2012) - [c14]Karthikeyan Shanmugam
A repair framework for scalar MDS codes. Allerton Conference 2012: 1166-1173 - [c13]Dimitris S. Papailiopoulos, Georgina Abou Elkheir, George N. Karystinos:
Maximum-likelihood blind PAM detection. ICC 2012: 2283-2287 - [c12]Dimitris S. Papailiopoulos, Jianqiang Luo, Alexandros G. Dimakis, Cheng Huang, Jin Li:
Simple regenerating codes: Network coding for cloud storage. INFOCOM 2012: 2801-2805 - [c11]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Locally repairable codes. ISIT 2012: 2771-2775 - [c10]Dimitris S. Papailiopoulos, Changho Suh
Feedback in the K-user interference channel. ISIT 2012: 3130-3134 - [i7]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Locally Repairable Codes. CoRR abs/1206.3804 (2012) - 2011
- [c9]Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Viveck R. Cadambe
Repair optimal erasure codes through hadamard designs. Allerton 2011: 1382-1389 - [c8]Megasthenis Asteris, Dimitris S. Papailiopoulos, George N. Karystinos:
Sparse principal component of a rank-deficient matrix. ISIT 2011: 673-677 - [c7]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Distributed storage codes through Hadamard designs. ISIT 2011: 1230-1234 - [i6]Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Viveck R. Cadambe:
Repair Optimal Erasure Codes through Hadamard Designs. CoRR abs/1106.1634 (2011) - [i5]Megasthenis Asteris, Dimitris S. Papailiopoulos, George N. Karystinos:
Sparse Principal Component of a Rank-deficient Matrix. CoRR abs/1106.1651 (2011) - [i4]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Distributed Storage Codes through Hadamard Designs. CoRR abs/1106.1652 (2011) - [i3]Dimitris S. Papailiopoulos, Jianqiang Luo, Alexandros G. Dimakis, Cheng Huang, Jin Li:
Simple Regenerating Codes: Network Coding for Cloud Storage. CoRR abs/1109.0264 (2011) - 2010
- [j1]Dimitris S. Papailiopoulos, George N. Karystinos:
Maximum-likelihood noncoherent OSTBC detection with polynomial complexity. IEEE Trans. Wirel. Commun. 9(6): 1935-1945 (2010) - [c6]Babak Hassibi, Alexandros G. Dimakis, Dimitris Papailiopoulos:
MCMC methods for integer least-squares problems. Allerton 2010: 495-501 - [c5]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Distributed storage codes meet multiple-access wiretap channels. Allerton 2010: 1420-1427 - [c4]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Interference Alignment as a Rank Constrained Rank Minimization. GLOBECOM 2010: 1-6 - [i2]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Interference Alignment as a Rank Constrained Rank Minimization. CoRR abs/1010.0476 (2010) - [i1]Dimitris S. Papailiopoulos, Alexandros G. Dimakis:
Distributed Storage Codes Meet Multiple-Access Wiretap Channels. CoRR abs/1010.0485 (2010)
2000 – 2009
- 2008
- [c3]Dimitris S. Papailiopoulos, George N. Karystinos:
Efficient maximum-likelihood noncoherent orthogonal STBC detection. Allerton 2008: 294-300 - [c2]Dimitris S. Papailiopoulos, George N. Karystinos:
Efficient computation of the M-phase vector that maximizes a rank-deficient quadratic form. CISS 2008: 1086-1090 - [c1]Dimitris S. Papailiopoulos, George N. Karystinos:
Polynomial-complexity maximum-likelihood block noncoherent MPSK detection. ICASSP 2008: 2681-2684
Coauthor Index
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