Recipes on hard thresholding methods. - Anastasios Kyrillidis; Volkan Cevher (CAMSAP, 2011)

Rank-deficient quadratic-form maximization over M-phase alphabet: Polynomial-complexity solvability and algorithmic developments. - Anastasios Kyrillidis; George N. Karystinos (ICASSP, 2011)

Hard thresholding with norm constraints. - Anastasios Kyrillidis; Gilles Puy; Volkan Cevher (ICASSP, 2012)

Combinatorial selection and least absolute shrinkage via the Clash algorithm. - Anastasios Kyrillidis; Volkan Cevher (ISIT, 2012)

MATRIX ALPS: Accelerated low rank and sparse matrix reconstruction. - Anastasios Kyrillidis; Volkan Cevher (SSP, 2012)

Sparse projections onto the simplex - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher (CoRR, 2012)

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors. - Nicholas D. Sidiropoulos; Anastasios Kyrillidis (IEEE Signal Process. Lett., 2012)

On quantifying qualitative geospatial data: a probabilistic approach. - Georgios Skoumas; Dieter Pfoser; Anastasios Kyrillidis (GEOGROWD, 2013)

Sparse simplex projections for portfolio optimization. - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch 001 (GlobalSIP, 2013)

Fast proximal algorithms for Self-concordant function minimization with application to sparse graph selection. - Anastasios Kyrillidis; Volkan Cevher (ICASSP, 2013)

Sparse projections onto the simplex. - Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch 001 (ICML (2), 2013)

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions. - Quoc Tran-Dinh; Anastasios Kyrillidis; Volkan Cevher (ICML (2), 2013)

Randomized Low-Memory Singular Value Projection - Stephen Becker; Volkan Cevher; Anastasios Kyrillidis (CoRR, 2013)

Group-Sparse Model Selection: Hardness and Relaxations - Luca Baldassarre; Nirav Bhan; Volkan Cevher; Anastasios Kyrillidis (CoRR, 2013)

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

Improving Co-Cluster Quality with Application to Product Recommendations. - Michail Vlachos; Francesco Fusco; Charalampos Mavroforakis; Anastasios Kyrillidis; Vassilios G. Vassiliadis (CIKM, 2014)

Non-uniform feature sampling for decision tree ensembles. - Anastasios Kyrillidis; Anastasios Zouzias (ICASSP, 2014)

Approximate matrix multiplication with application to linear embeddings. - Anastasios Kyrillidis; Michail Vlachos; Anastasios Zouzias (ISIT, 2014)

Provable deterministic leverage score sampling. - Dimitris S. Papailiopoulos; Anastasios Kyrillidis; Christos Boutsidis (KDD, 2014)

Fixed-rank Rayleigh Quotient Maximization by an $M$PSK Sequence. - Anastasios Kyrillidis; George N. Karystinos (CoRR, 2014)

Location Estimation Using Crowdsourced Geospatial Narratives. - Georgios Skoumas; Dieter Pfoser; Anastasios Kyrillidis (CoRR, 2014)

Matrix Recipes for Hard Thresholding Methods. - Anastasios Kyrillidis; Volkan Cevher (J. Math. Imaging Vis., 2014)

An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization. - Quoc Tran-Dinh; Anastasios Kyrillidis; Volkan Cevher (SIAM J. Optim., 2014)

Fixed-Rank Rayleigh Quotient Maximization by an MPSK Sequence. - Anastasios Kyrillidis; George N. Karystinos (IEEE Trans. Commun., 2014)

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences. - Anastasios Kyrillidis (2014)

Stay on path: PCA along graph paths. - Megasthenis Asteris; Anastasios Kyrillidis; Alexandros G. Dimakis; Han-Gyol Yi; Bharath Chandrasekaran (ICML, 2015)

Sparse PCA via Bipartite Matchings. - Megasthenis Asteris; Dimitris S. Papailiopoulos; Anastasios Kyrillidis; Alexandros G. Dimakis (NIPS, 2015)

Linear Inverse Problems with Norm and Sparsity Constraints. - Volkan Cevher; Sina Jafarpour; Anastasios Kyrillidis (CoRR, 2015)

Structured Sparsity: Discrete and Convex approaches. - Anastasios Kyrillidis; Luca Baldassarre; Marwa El Halabi; Quoc Tran-Dinh; Volkan Cevher (CoRR, 2015)

Composite self-concordant minimization. - Quoc Tran-Dinh; Anastasios Kyrillidis; Volkan Cevher (J. Mach. Learn. Res., 2015)

Compressive mining: fast and optimal data mining in the compressed domain. - Michail Vlachos; Nikolaos M. Freris; Anastasios Kyrillidis (VLDB J., 2015)

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 (AISTATS, 2016)

Learning Sparse Additive Models with Interactions in High Dimensions. - Hemant Tyagi; Anastasios Kyrillidis; Bernd Gärtner; Andreas Krause 001 (AISTATS, 2016)

Finding low-rank solutions to smooth convex problems via the Burer-Monteiro approach. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (Allerton, 2016)

Dropping Convexity for Faster Semi-definite Optimization. - Srinadh Bhojanapalli; Anastasios Kyrillidis; Sujay Sanghavi (COLT, 2016)

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

Provable non-convex projected gradient descent for a class of constrained matrix optimization problems. - Dohyung Park; Anastasios Kyrillidis; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Trading-off variance and complexity in stochastic gradient descent. - Vatsal Shah; Megasthenis Asteris; Anastasios Kyrillidis; Sujay Sanghavi (CoRR, 2016)

Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions. - Hemant Tyagi; Anastasios Kyrillidis; Bernd Gärtner; Andreas Krause 001 (CoRR, 2016)

Group-Sparse Model Selection: Hardness and Relaxations. - Luca Baldassarre; Nirav Bhan; Volkan Cevher; Anastasios Kyrillidis; Siddhartha Satpathi (IEEE Trans. Inf. Theory, 2016)

Location Estimation Using Crowdsourced Spatial Relations. - Georgios Skoumas; Dieter Pfoser; Anastasios Kyrillidis; Timos Sellis (ACM Trans. Spatial Algorithms Syst., 2016)

Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (AISTATS, 2017)

Provable quantum state tomography via non-convex methods. - Anastasios Kyrillidis; Amir Kalev; Dohyung Park; Srinadh Bhojanapalli; Constantine Caramanis; Sujay Sanghavi (CoRR, 2017)

Statistical Inference Using SGD. - Tianyang Li; Liu Liu; Anastasios Kyrillidis; Constantine Caramanis (AAAI, 2018)

IHT dies hard: Provable accelerated Iterative Hard Thresholding. - Rajiv Khanna; Anastasios Kyrillidis (AISTATS, 2018)

Simple and practical algorithms for 𝓁p-norm low-rank approximation. - Anastasios Kyrillidis (UAI, 2018)

Approximate Newton-based statistical inference using only stochastic gradients. - Tianyang Li; Anastasios Kyrillidis; Liu Liu; Constantine Caramanis (CoRR, 2018)

Run Procrustes, Run! On the convergence of accelerated Procrustes Flow. - Anastasios Kyrillidis; Shashanka Ubaru; Georgios Kollias; Kristofer E. Bouchard (CoRR, 2018)

Implicit regularization and solution uniqueness in over-parameterized matrix sensing. - Anastasios Kyrillidis; Amir Kalev (CoRR, 2018)

A Single-Phase, Proximal Path-Following Framework. - Quoc Tran-Dinh; Anastasios Kyrillidis; Volkan Cevher (Math. Oper. Res., 2018)

Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably. - Dohyung Park; Anastasios Kyrillidis; Constantine Caramanis; Sujay Sanghavi (SIAM J. Imaging Sci., 2018)

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization. - Ya-Ping Hsieh; Yu-Chun Kao; Rabeeh Karimi Mahabadi; Alp Yurtsever; Anastasios Kyrillidis; Volkan Cevher (IEEE Trans. Signal Process., 2018)

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

Learning Sparse Distributions using Iterative Hard Thresholding. - Jacky Y. Zhang; Rajiv Khanna; Anastasios Kyrillidis; Oluwasanmi Koyejo (NeurIPS, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

Distributed Learning of Deep Neural Networks using Independent Subnet Training. - Binhang Yuan; Anastasios Kyrillidis; Christopher M. Jermaine (CoRR, 2019)

Decaying momentum helps neural network training. - John Chen 002; Anastasios Kyrillidis (CoRR, 2019)

Optimal Mini-Batch Size Selection for Fast Gradient Descent. - Michael P. Perrone; Haidar Khan; Changhoan Kim; Anastasios Kyrillidis; Jerry Quinn; Valentina Salapura (CoRR, 2019)

FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints. - Anastasios Kyrillidis; Anshumali Shrivastava; Moshe Y. Vardi; Zhiwei Zhang 001 (AAAI, 2020)

Low-rank regularization and solution uniqueness in over-parameterized matrix sensing. - Kelly Geyer; Anastasios Kyrillidis; Amir Kalev (AISTATS, 2020)

Negative Sampling in Semi-Supervised learning. - John Chen 002; Vatsal Shah; Anastasios Kyrillidis (ICML, 2020)

Bayesian Coresets: An Optimization Perspective. - Jacky Y. Zhang; Rajiv Khanna; Anastasios Kyrillidis; Oluwasanmi Koyejo (CoRR, 2020)

ImCLR: Implicit Contrastive Learning for Image Classification. - John Chen 002; Samarth Sinha; Anastasios Kyrillidis (CoRR, 2020)

On Generalization of Adaptive Methods for Over-parameterized Linear Regression. - Vatsal Shah; Soumya Basu 001; Anastasios Kyrillidis; Sujay Sanghavi (CoRR, 2020)

Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets. - T. Mitchell Roddenberry; Santiago Segarra; Anastasios Kyrillidis (CoRR, 2020)

On Continuous Local BDD-Based Search for Hybrid SAT Solving. - Anastasios Kyrillidis; Moshe Y. Vardi; Zhiwei Zhang 001 (AAAI, 2021)

Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective. - Jacky Zhang; Rajiv Khanna; Anastasios Kyrillidis; Sanmi Koyejo (AISTATS, 2021)

Mitigating Deep Double Descent by Concatenating Inputs. - John Chen 002; Qihan Wang; Anastasios Kyrillidis (CIKM, 2021)

Robust Optimization-based Motion Planning for high-DOF Robots under Sensing Uncertainty. - Carlos Quintero-Peña; Anastasios Kyrillidis; Lydia E. Kavraki (ICRA, 2021)

Solving hybrid Boolean constraints in continuous space via multilinear Fourier expansions. - Anastasios Kyrillidis; Anshumali Shrivastava; Moshe Y. Vardi; Zhiwei Zhang 001 (Artif. Intell., 2021)

GIST: Distributed Training for Large-Scale Graph Convolutional Networks. - Cameron R. Wolfe; Jingkang Yang; Arindam Chowdhury; Chen Dun; Artun Bayer; Santiago Segarra; Anastasios Kyrillidis (CoRR, 2021)

Fast quantum state reconstruction via accelerated non-convex programming. - Junhyung Lyle Kim; George Kollias; Amir Kalev; Ken X. Wei; Anastasios Kyrillidis (CoRR, 2021)

Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs. - Junhyung Lyle Kim; Jose Antonio Lara Benitez; Mohammad Taha Toghani; Cameron R. Wolfe; Zhiwei Zhang 001; Anastasios Kyrillidis (CoRR, 2021)

Provably Efficient Lottery Ticket Discovery. - Cameron R. Wolfe; Qihan Wang; Junhyung Lyle Kim; Anastasios Kyrillidis (CoRR, 2021)

Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks. - Zhenwei Dai; Chen Dun; Yuxin Tang; Anastasios Kyrillidis; Anshumali Shrivastava (CoRR, 2021)

Demon: Improved Neural Network Training With Momentum Decay. - John Chen 002; Cameron R. Wolfe; Zhao Li; Anastasios Kyrillidis (ICASSP, 2022)

No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. - Ahmed Imtiaz Humayun; Randall Balestriero; Anastasios Kyrillidis; Richard G. Baraniuk (ICASSP, 2022)

PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication. - Cheng Wan 005; Youjie Li; Cameron R. Wolfe; Anastasios Kyrillidis; Nam Sung Kim; Yingyan Lin (ICLR, 2022)

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

i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery. - Cameron R. Wolfe; Anastasios Kyrillidis (L4DC, 2022)

REX: Revisiting Budgeted Training with an Improved Schedule. - John Chen 002; Cameron R. Wolfe; Tasos Kyrillidis (MLSys, 2022)

Stackmix: a complementary mix algorithm. - John Chen 002; Samarth Sinha; Anastasios Kyrillidis (UAI, 2022)

ResIST: Layer-wise decomposition of ResNets for distributed training. - Chen Dun; Cameron R. Wolfe; Christopher M. Jermaine; Anastasios Kyrillidis (UAI, 2022)

DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving. - Anastasios Kyrillidis; Moshe Y. Vardi; Zhiwei Zhang 001 (CoRR, 2022)

Cold Start Streaming Learning for Deep Networks. - Cameron R. Wolfe; Anastasios Kyrillidis (CoRR, 2022)

Extragradient with Positive Momentum is Optimal for Games with Cross-Shaped Jacobian Spectrum. - Junhyung Lyle Kim; Gauthier Gidel; Anastasios Kyrillidis; Fabian Pedregosa (CoRR, 2022)

Distributed Learning of Fully Connected Neural Networks using Independent Subnet Training. - Binhang Yuan; Cameron R. Wolfe; Chen Dun; Yuxin Tang; Anastasios Kyrillidis; Chris Jermaine (Proc. VLDB Endow., 2022)

On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons. - Fangshuo Liao; Anastasios Kyrillidis (Trans. Mach. Learn. Res., 2022)

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

LOFT: Finding Lottery Tickets through Filter-wise Training. - Qihan Wang; Chen Dun; Fangshuo Liao; Chris Jermaine; Anastasios Kyrillidis (AISTATS, 2023)

Strong Lottery Ticket Hypothesis with ε-perturbation. - Zheyang Xiong; Fangshuo Liao; Anastasios Kyrillidis (AISTATS, 2023)

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

Optimal Grasps and Placements for Task and Motion Planning in Clutter. - Carlos Quintero-Peña; Zachary Kingston; Tianyang Pan; Rahul Shome; Anastasios Kyrillidis; Lydia E. Kavraki (ICRA, 2023)

Using non-convex optimization in quantum process tomography: Factored gradient descent is tough to beat. - David A. Quiroga; Anastasios Kyrillidis (ICRC, 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 (NeurIPS, 2023)

Accelerated Convergence of Nesterov's Momentum for Deep Neural Networks under Partial Strong Convexity. - Fangshuo Liao; Anastasios Kyrillidis (CoRR, 2023)

Fed-ZERO: Efficient Zero-shot Personalization with Federated Mixture of Experts. - Chen Dun; Mirian Hipolito Garcia; Guoqing Zheng; Ahmed Hassan Awadallah; Robert Sim; Anastasios Kyrillidis; Dimitrios Dimitriadis (CoRR, 2023)

CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention. - Chen Dun; Qiutai Pan; Shikai Jin; Ria Stevens; Mitchell D. Miller; George N. Phillips Jr.; Anastasios Kyrillidis (CoRR, 2023)

On the Error-Propagation of Inexact Deflation for Principal Component Analysis. - Fangshuo Liao; Junhyung Lyle Kim; Cruz Barnum; Anastasios Kyrillidis (CoRR, 2023)

Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography. - Junhyung Lyle Kim; Mohammad Taha Toghani; César A. Uribe; Anastasios Kyrillidis (IEEE Control. Syst. Lett., 2023)

Provable Accelerated Convergence of Nesterov's Momentum for Deep ReLU Neural Networks. - Fangshuo Liao; Anastasios Kyrillidis (ALT, 2024)

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

Adaptive Federated Learning with Auto-Tuned Clients. - Junhyung Lyle Kim; Mohammad Taha Toghani; César A. Uribe; Anastasios Kyrillidis (ICLR, 2024)

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

Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty. - Carlos Quintero-Peña; Wil Thomason; Zachary Kingston; Anastasios Kyrillidis; Lydia E. Kavraki (ICRA, 2024)

Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size. - John Chen 002; Chen Dun; Anastasios Kyrillidis (ISIT, 2024)

A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm. - Junhyung Lyle Kim; Nai-Hui Chia; Anastasios Kyrillidis (CoRR, 2024)

When is Momentum Extragradient Optimal? A Polynomial-Based Analysis. - Junhyung Lyle Kim; Gauthier Gidel; Anastasios Kyrillidis; Fabian Pedregosa (Trans. Mach. Learn. Res., 2024)

How Much Pre-training Is Enough to Discover a Good Subnetwork? - Cameron R. Wolfe; Fangshuo Liao; Qihan Wang; Junhyung Lyle Kim; Anastasios Kyrillidis (Trans. Mach. Learn. Res., 2024)

Better schedules for low precision training of deep neural networks. - Cameron R. Wolfe; Anastasios Kyrillidis (Mach. Learn., 2024)

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 (J. Appl. Comput. Topol., 2024)

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 (AAAI, 2025)

Provable Model-Parallel Distributed Principal Component Analysis with Parallel Deflation. - Fangshuo Liao; Wenyi Su; 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 (CPAL, 2025)

Quantum EigenGame for excited state calculation. - David A. Quiroga; Jason Han; Anastasios Kyrillidis (CPAL, 2025)

Unveiling Hidden Pivotal Players with GoalNet: A GNN-Based Soccer Player Evaluation System. - Jacky Hao Jiang; Jerry Cai; Anastasios Kyrillidis (CoRR, 2025)

Exploring How LLMs Capture and Represent Domain-Specific Knowledge. - Mirian Hipolito Garcia; Camille Couturier; Daniel Madrigal Diaz; Ankur Mallick; Anastasios Kyrillidis; Robert Sim; Victor Rühle; Saravan Rajmohan (CoRR, 2025)

One Rank at a Time: Cascading Error Dynamics in Sequential Learning. - Mahtab Alizadeh Vandchali; Fangshuo Liao; Anastasios Kyrillidis (CoRR, 2025)

Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization. - Zhiwei Zhang 001; Samy Wu Fung; Anastasios Kyrillidis; Stanley J. Osher; Moshe Y. Vardi (CoRR, 2025)