Kyrillidis, Anastasios

54 publications

NeurIPS 2025 Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings Yehya Farhat, Hamza ElMokhtar Shili, Fangshuo Liao, Chen Dun, Mirian Del Carmen Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah, Robert Sim, Dimitrios Dimitriadis, Anastasios Kyrillidis

CPAL 2025 Provable Model-Parallel Distributed Principal Component Analysis with Parallel Deflation Fangshuo Liao, Wenyi Su, Anastasios Kyrillidis

CPAL 2025 Quantum EigenGame for Excited State Calculation David A. Quiroga, Jason Han, Anastasios Kyrillidis

CPAL 2025 RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs Tom Pan, Evan Dramko, Mitchell D. Miller, George N Phillips Jr., Anastasios Kyrillidis

AAAI 2025 Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation Chen Dun, Mirian del Carmen Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah, Robert Sim, Anastasios Kyrillidis

ICLR 2024 Adaptive Federated Learning with Auto-Tuned Clients Junhyung Lyle Kim, Taha Toghani, Cesar A Uribe, Anastasios Kyrillidis

MLJ 2024 Better Schedules for Low Precision Training of Deep Neural Networks Cameron R. Wolfe, Anastasios Kyrillidis

TMLR 2024 How Much Pre-Training Is Enough to Discover a Good Subnetwork? Cameron R. Wolfe, Fangshuo Liao, Qihan Wang, Junhyung Lyle Kim, Anastasios Kyrillidis

CPAL 2024 Leveraging Sparse Input and Sparse Models: Efficient Distributed Learning in Resource-Constrained Environments Emmanouil Kariotakis, Grigorios Tsagkatakis, Panagiotis Tsakalides, Anastasios Kyrillidis

ICML 2024 On the Error-Propagation of Inexact Hotelling’s Deflation for Principal Component Analysis Fangshuo Liao, Junhyung Lyle Kim, Cruz Barnum, Anastasios Kyrillidis

ALT 2024 Provable Accelerated Convergence of Nesterov’s Momentum for Deep ReLU Neural Networks Fangshuo Liao, Anastasios Kyrillidis

ICLRW 2024 Smoothness-Adaptive Sharpness-Aware Minimization for Finding Flatter Minima Hiroki Naganuma, Junhyung Lyle Kim, Anastasios Kyrillidis, Ioannis Mitliagkas

TMLR 2024 When Is Momentum Extragradient Optimal? a Polynomial-Based Analysis Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

ICMLW 2023 Adaptive Federated Learning with Auto-Tuned Clients via Local Smoothness Junhyung Lyle Kim, Taha Toghani, Cesar A Uribe, Anastasios Kyrillidis

AISTATS 2023 Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout Chen Dun, Mirian Hipolito, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis

NeurIPSW 2023 FedJETs: Efficient Just-in-Time Personalization with Federated Mixture of Experts Chen Dun, Mirian Hipolito Garcia, Guoqing Zheng, Ahmed Awadallah, Robert Sim, Anastasios Kyrillidis, Dimitrios Dimitriadis

ICCV 2023 Federated Learning over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat Erdong Hu, Yuxin Tang, Anastasios Kyrillidis, Chris Jermaine

AISTATS 2023 LOFT: Finding Lottery Tickets Through Filter-Wise Training Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis

NeurIPS 2023 Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, Anshumali Shrivastava

AISTATS 2023 Strong Lottery Ticket Hypothesis with $\varepsilon$–perturbation Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis

NeurIPSW 2023 Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation Chen Dun, Mirian Del Carmen Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah, Anastasios Kyrillidis, Robert Sim

L4DC 2022 Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis

NeurIPSW 2022 Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout Chen Dun, Mirian Del Carmen Hipolito Garcia, Christopher Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis

NeurIPSW 2022 GIST: Distributed Training for Large-Scale Graph Convolutional Networks Cameron R. Wolfe, Jingkang Yang, Fangshuo Liao, Arindam Chowdhury, Chen Dun, Artun Bayer, Santiago Segarra, Anastasios Kyrillidis

L4DC 2022 I-SpaSP: Structured Neural Pruning via Sparse Signal Recovery Cameron R. Wolfe, Anastasios Kyrillidis

NeurIPSW 2022 LOFT: Finding Lottery Tickets Through Filter-Wise Training Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis

NeurIPSW 2022 Momentum Extragradient Is Optimal for Games with Cross-Shaped Spectrum Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

TMLR 2022 On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons Fangshuo Liao, Anastasios Kyrillidis

ICLR 2022 PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Lin

UAI 2022 ResIST: Layer-Wise Decomposition of ResNets for Distributed Training Chen Dun, Cameron R. Wolfe, Christopher M. Jermaine, Anastasios Kyrillidis

UAI 2022 Stackmix: A Complementary Mix Algorithm John Chen, Samarth Sinha, Anastasios Kyrillidis

NeurIPSW 2022 Strong Lottery Ticket Hypothesis with $\epsilon$–perturbation Fangshuo Liao, Zheyang Xiong, Anastasios Kyrillidis

AISTATS 2021 Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective Jacky Zhang, Rajiv Khanna, Anastasios Kyrillidis, Sanmi Koyejo

AAAI 2021 On Continuous Local BDD-Based Search for Hybrid SAT Solving Anastasios Kyrillidis, Moshe Y. Vardi, Zhiwei Zhang

AAAI 2020 FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi, Zhiwei Zhang

AISTATS 2020 Low-Rank Regularization and Solution Uniqueness in Over-Parameterized Matrix Sensing Kelly Geyer, Anastasios Kyrillidis, Amir Kalev

ICML 2020 Negative Sampling in Semi-Supervised Learning John Chen, Vatsal Shah, Anastasios Kyrillidis

ICML 2019 Compressing Gradient Optimizers via Count-Sketches Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava

NeurIPS 2019 Learning Sparse Distributions Using Iterative Hard Thresholding Jacky Y Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O Koyejo

AISTATS 2018 IHT Dies Hard: Provable Accelerated Iterative Hard Thresholding Rajiv Khanna, Anastasios Kyrillidis

UAI 2018 Simple and Practical Algorithms for 𝓁p-Norm Low-Rank Approximation Anastasios Kyrillidis

AAAI 2018 Statistical Inference Using SGD Tianyang Li, Liu Liu, Anastasios Kyrillidis, Constantine Caramanis

AISTATS 2017 Non-Square Matrix Sensing Without Spurious Local Minima via the Burer-Monteiro Approach Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi

ICML 2016 A Simple and Provable Algorithm for Sparse Diagonal CCA Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

AISTATS 2016 Bipartite Correlation Clustering: Maximizing Agreements Megasthenis Asteris, Anastasios Kyrillidis, Dimitris S. Papailiopoulos, Alexandros G. Dimakis

AISTATS 2016 Convex Block-Sparse Linear Regression with Expanders - Provably Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran-Dinh, Luca Baldassarre, Volkan Cevher

COLT 2016 Dropping Convexity for Faster Semi-Definite Optimization Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi

AISTATS 2016 Learning Sparse Additive Models with Interactions in High Dimensions Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

JMLR 2015 Composite Self-Concordant Minimization Quoc Tran-Dinh, Anastasios Kyrillidis, Volkan Cevher

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

ICML 2015 Stay on Path: PCA Along Graph Paths Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran

AAAI 2014 Scalable Sparse Covariance Estimation via Self-Concordance Anastasios Kyrillidis, Rabeeh Karimi Mahabadi, Quoc Tran-Dinh, Volkan Cevher

ICML 2013 A Proximal Newton Framework for Composite Minimization: Graph Learning Without Cholesky Decompositions and Matrix Inversions Quoc Tran Dinh, Anastasios Kyrillidis, Volkan Cevher

ICML 2013 Sparse Projections onto the Simplex Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch