Papailiopoulos, Dimitris

55 publications

ICLR 2026 Not All Bits Are Equal: Scale-Dependent Memory Optimization Strategies for Reasoning Models Junhyuck Kim, Ethan Ewer, Taehong Moon, Jongho Park, Dimitris Papailiopoulos

ICLR 2026 Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning Vaishnavi Shrivastava, Ahmed Hassan Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, Dimitris Papailiopoulos

ICML 2025 Everything Everywhere All at Once: LLMs Can In-Context Learn Multiple Tasks in Superposition Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos

NeurIPS 2025 Extrapolation by Association: Length Generalization Transfer in Transformers Ziyang Cai, Nayoung Lee, Avi Schwarzschild, Samet Oymak, Dimitris Papailiopoulos

ICLR 2025 From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, Dimitris Papailiopoulos

AISTATS 2025 How Well Can Transformers Emulate In-Context Newton’s Method? Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee

ICML 2025 Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries Junhyuck Kim, Jongho Park, Jaewoong Cho, Dimitris Papailiopoulos

ICLRW 2025 Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries Junhyuck Kim, Jongho Park, Jaewoong Cho, Dimitris Papailiopoulos

ICML 2025 Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges Nayoung Lee, Ziyang Cai, Avi Schwarzschild, Kangwook Lee, Dimitris Papailiopoulos

ICLRW 2025 Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges Nayoung Lee, Ziyang Cai, Avi Schwarzschild, Kangwook Lee, Dimitris Papailiopoulos

ICML 2025 VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data Thomas Zeng, Shuibai Zhang, Shutong Wu, Christian Classen, Daewon Chae, Ethan Ewer, Minjae Lee, Heeju Kim, Wonjun Kang, Jackson Kunde, Ying Fan, Jungtaek Kim, Hyung Il Koo, Kannan Ramchandran, Dimitris Papailiopoulos, Kangwook Lee

ICML 2024 CHAI: Clustered Head Attention for Efficient LLM Inference Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu

ICML 2024 Can Mamba Learn How to Learn? a Comparative Study on In-Context Learning Tasks Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos

ICLR 2024 Looped Transformers Are Better at Learning Learning Algorithms Liu Yang, Kangwook Lee, Robert D Nowak, Dimitris Papailiopoulos

TMLR 2024 Mini-Batch Optimization of Contrastive Loss Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee

TMLR 2024 Predictive Pipelined Decoding: A Compute-Latency Trade-Off for Exact LLM Decoding Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, Kangwook Lee

ICLR 2024 Teaching Arithmetic to Small Transformers Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos

NeurIPSW 2024 Transformers Can Learn Meta-Skills for Task Generalization in In-Context Learning Ying Fan, Steve Yadlowsky, Dimitris Papailiopoulos, Kangwook Lee

NeurIPS 2023 Dissecting Chain-of-Thought: Compositionality Through In-Context Filtering and Learning Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, Samet Oymak

ICMLW 2023 Looped Transformers Are Better at Learning Learning Algorithms Liu Yang, Kangwook Lee, Robert D Nowak, Dimitris Papailiopoulos

ICML 2023 Looped Transformers as Programmable Computers Angeliki Giannou, Shashank Rajput, Jy-Yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos

ICLRW 2023 Looped Transformers as Programmable Computers Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos

ICLRW 2023 Mini-Batch Optimization of Contrastive Loss Kartik Sreenivasan, Keon Lee, Jeong-Gwan Lee, Anna Lee, Jaewoong Cho, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee

ICMLW 2023 Predictive Pipelined Decoding: A Compute-Latency Trade-Off for Exact LLM Decoding Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, Kangwook Lee

NeurIPSW 2023 Teaching Arithmetic to Small Transformers Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, Dimitris Papailiopoulos

COLT 2023 The Expressive Power of Tuning Only the Normalization Layers Angeliki Giannou, Shashank Rajput, Dimitris Papailiopoulos

ICML 2023 Transformers as Algorithms: Generalization and Stability in In-Context Learning Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak

AISTATS 2022 Finding Nearly Everything Within Random Binary Networks Kartik Sreenivasan, Shashank Rajput, Jy-Yong Sohn, Dimitris Papailiopoulos

NeurIPSW 2022 A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris Papailiopoulos, Kangwook Lee, Robert D Nowak

NeurIPSW 2022 Active Learning Is a Strong Baseline for Data Subset Selection Dongmin Park, Dimitris Papailiopoulos, Kangwook Lee

ICML 2022 GenLabel: Mixup Relabeling Using Generative Models Jy-Yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee

NeurIPS 2022 LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee

ICLR 2022 Permutation-Based SGD: Is Random Optimal? Shashank Rajput, Kangwook Lee, Dimitris Papailiopoulos

NeurIPS 2022 Rare Gems: Finding Lottery Tickets at Initialization Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris Papailiopoulos

NeurIPS 2021 An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks Shashank Rajput, Kartik Sreenivasan, Dimitris Papailiopoulos, Amin Karbasi

NeurIPS 2020 Attack of the Tails: Yes, You Really Can Backdoor Federated Learning Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos

NeurIPS 2020 Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas

ICML 2020 Closing the Convergence Gap of SGD Without Replacement Shashank Rajput, Anant Gupta, Dimitris Papailiopoulos

ICLR 2020 Federated Learning with Matched Averaging Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, Yasaman Khazaeni

NeurIPS 2020 Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization Is Sufficient Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos

AISTATS 2019 A Geometric Perspective on the Transferability of Adversarial Directions Zachary Charles, Harrison Rosenberg, Dimitris Papailiopoulos

ICMLW 2019 Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas

NeurIPS 2019 DETOX: A Redundancy-Based Framework for Faster and More Robust Gradient Aggregation Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos

ICML 2019 Does Data Augmentation Lead to Positive Margin? Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos

NeurIPS 2018 ATOMO: Communication-Efficient Learning via Atomic Sparsification Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, Stephen Wright

ICML 2018 DRACO: Byzantine-Resilient Distributed Training via Redundant Gradients Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos

ICML 2018 Stability and Generalization of Learning Algorithms That Converge to Global Optima Zachary Charles, Dimitris Papailiopoulos

NeurIPS 2018 The Effect of Network Width on the Performance of Large-Batch Training Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris Papailiopoulos, Paraschos Koutris

NeurIPS 2016 Cyclades: Conflict-Free Asynchronous Machine Learning Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I Jordan, Kannan Ramchandran, Christopher Ré

NeurIPS 2015 Orthogonal NMF Through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G Dimakis

NeurIPS 2015 Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I Jordan

NeurIPS 2015 Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G Dimakis

ICML 2014 Finding Dense Subgraphs via Low-Rank Bilinear Optimization Dimitris Papailiopoulos, Ioannis Mitliagkas, Alexandros Dimakis, Constantine Caramanis

ICML 2014 Nonnegative Sparse PCA with Provable Guarantees Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros Dimakis

ICML 2013 Sparse PCA Through Low-Rank Approximations Dimitris Papailiopoulos, Alexandros Dimakis, Stavros Korokythakis