Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design. - Peter Richtárik; Martin Takác 001 (OR, 2011)

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes - Peter Richtárik; Martin Takác 001; Selin Damla Ahipasaoglu (CoRR, 2012)

Mini-Batch Primal and Dual Methods for SVMs. - Martin Takác 001; Avleen Singh Bijral; Peter Richtárik; Nati Srebro (ICML (3), 2013)

TOP-SPIN: TOPic discovery via Sparse Principal component INterference. - Martin Takác 001; Selin Damla Ahipasaoglu; Ngai-Man Cheung; Peter Richtárik (CoRR, 2013)

Fast distributed coordinate descent for non-strongly convex losses. - Olivier Fercoq; Zheng Qu 001; Peter Richtárik; Martin Takác 001 (MLSP, 2014)

Communication-Efficient Distributed Dual Coordinate Ascent. - Martin Jaggi; Virginia Smith; Martin Takác 001; Jonathan Terhorst; Sanjay Krishnan; Thomas Hofmann 001; Michael I. Jordan (NIPS, 2014)

mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (CoRR, 2014)

Inequality-Constrained Matrix Completion: Adding the Obvious Helps! - Martin Takác 001; Jakub Marecek; Peter Richtárik (CoRR, 2014)

Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. - Peter Richtárik; Martin Takác 001 (Math. Program., 2014)

Adding vs. Averaging in Distributed Primal-Dual Optimization. - Chenxin Ma; Virginia Smith; Martin Jaggi; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (ICML, 2015)

Dual Free SDCA for Empirical Risk Minimization with Adaptive Probabilities. - Xi He 004; Martin Takác 001 (CoRR, 2015)

Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization? - Chenxin Ma; Martin Takác 001 (CoRR, 2015)

Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption. - Chenxin Ma; Rachael Tappenden; Martin Takác 001 (CoRR, 2015)

Distributed Mini-Batch SDCA. - Martin Takác 001; Peter Richtárik; Nathan Srebro (CoRR, 2015)

Primal-Dual Rates and Certificates. - Celestine Dünner; Simone Forte; Martin Takác 001; Martin Jaggi (ICML, 2016)

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. - Zheng Qu 001; Peter Richtárik; Martin Takác 001; Olivier Fercoq (ICML, 2016)

A Multi-Batch L-BFGS Method for Machine Learning. - Albert S. Berahas; Jorge Nocedal; Martin Takác 001 (NIPS, 2016)

Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. - Xi He 004; Dheevatsa Mudigere; Mikhail Smelyanskiy; Martin Takác 001 (CoRR, 2016)

Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption. - Jie Liu 036; Martin Takác 001 (CoRR, 2016)

Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing. - Chenxin Ma; Martin Takác 001 (CoRR, 2016)

Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. - Chenxin Ma; Rachael Tappenden; Martin Takác 001 (J. Mach. Learn. Res., 2016)

Distributed Coordinate Descent Method for Learning with Big Data. - Peter Richtárik; Martin Takác 001 (J. Mach. Learn. Res., 2016)

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (IEEE J. Sel. Top. Signal Process., 2016)

Parallel coordinate descent methods for big data optimization. - Peter Richtárik; Martin Takác 001 (Math. Program., 2016)

On optimal probabilities in stochastic coordinate descent methods. - Peter Richtárik; Martin Takác 001 (Optim. Lett., 2016)

Distributed Hessian-Free Optimization for Deep Neural Network. - Xi He 004; Dheevatsa Mudigere; Mikhail Smelyanskiy; Martin Takác 001 (AAAI Workshops, 2017)

Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing. - Chenxin Ma; Martin Takác 001 (AAAI Workshops, 2017)

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. - Lam M. Nguyen; Jie Liu 036; Katya Scheinberg; Martin Takác 001 (ICML, 2017)

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. - Lam M. Nguyen; Jie Liu 036; Katya Scheinberg; Martin Takác 001 (CoRR, 2017)

A Deep Q-Network for the Beer Game with Partial Information. - Afshin Oroojlooyjadid; MohammadReza Nazari; Lawrence V. Snyder; Martin Takác 001 (CoRR, 2017)

Stock-out Prediction in Multi-echelon Networks. - Afshin Oroojlooyjadid; Lawrence V. Snyder; Martin Takác 001 (CoRR, 2017)

Underestimate Sequences via Quadratic Averaging. - Chenxin Ma; Naga Venkata C. Gudapati; Majid Jahani; Rachael Tappenden; Martin Takác 001 (CoRR, 2017)

Matrix completion under interval uncertainty. - Jakub Marecek; Peter Richtárik; Martin Takác 001 (Eur. J. Oper. Res., 2017)

CoCoA: A General Framework for Communication-Efficient Distributed Optimization. - Virginia Smith; Simone Forte; Chenxin Ma; Martin Takác 001; Michael I. Jordan; Martin Jaggi (J. Mach. Learn. Res., 2017)

Distributed optimization with arbitrary local solvers. - Chenxin Ma; Jakub Konecný; Martin Jaggi; Virginia Smith; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2017)

A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow. - Jakub Marecek; Martin Takác 001 (Optim. Methods Softw., 2017)

Hybrid Methods in Solving Alternating-Current Optimal Power Flows. - Jie Liu 036; Alan C. Liddell Jr.; Jakub Marecek; Martin Takác 001 (IEEE Trans. Smart Grid, 2017)

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. - Lam M. Nguyen; Phuong Ha Nguyen; Marten van Dijk; Peter Richtárik; Katya Scheinberg; Martin Takác 001 (ICML, 2018)

Reinforcement Learning for Solving the Vehicle Routing Problem. - MohammadReza Nazari; Afshin Oroojlooy; Lawrence V. Snyder; Martin Takác 001 (NeurIPS, 2018)

Matrix Completion Under Interval Uncertainty: Highlights. - Jakub Marecek; Peter Richtárik; Martin Takác 001 (ECML/PKDD (3), 2018)

Deep Reinforcement Learning for Solving the Vehicle Routing Problem. - MohammadReza Nazari; Afshin Oroojlooy; Lawrence V. Snyder; Martin Takác 001 (CoRR, 2018)

Active Metric Learning for Supervised Classification. - Krishnan Kumaran; Dimitri J. Papageorgiou; Yutong Chang; Minhan Li; Martin Takác 001 (CoRR, 2018)

On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches. - Jie Liu 036; Yu Rong 001; Martin Takác 001; Junzhou Huang (CoRR, 2018)

Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization. - Xi He 004; Rachael Tappenden; Martin Takác 001 (Frontiers Appl. Math. Stat., 2018)

On the complexity of parallel coordinate descent. - Rachael Tappenden; Martin Takác 001; Peter Richtárik (Optim. Methods Softw., 2018)

Entropy-Penalized Semidefinite Programming. - Mikhail Krechetov; Jakub Marecek; Yury Maximov; Martin Takác 001 (IJCAI, 2019)

Multi-Agent Image Classification via Reinforcement Learning. - Hossein K. Mousavi; MohammadReza Nazari; Martin Takác 001; Nader Motee (IROS, 2019)

Distributed Learning with Compressed Gradient Differences. - Konstantin Mishchenko; Eduard Gorbunov; Martin Takác 001; Peter Richtárik (CoRR, 2019)

Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. - Albert S. Berahas; Majid Jahani; Martin Takác 001 (CoRR, 2019)

Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework. - MohammadReza Nazari; Majid Jahani; Lawrence V. Snyder; Martin Takác 001 (CoRR, 2019)

A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning. - Hossein K. Mousavi; Guangyi Liu 004; Weihang Yuan; Martin Takác 001; Héctor Muñoz-Avila; Nader Motee (CoRR, 2019)

FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods. - Nur Sila Gulgec; Zheng Shi; Neil Deshmukh; Shamim N. Pakzad; Martin Takác 001 (CoRR, 2019)

Distributed Fixed Point Methods with Compressed Iterates. - Sélim Chraibi; Ahmed Khaled 001; Dmitry Kovalev; Peter Richtárik; Adil Salim; Martin Takác 001 (CoRR, 2019)

Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization. - Nur Sila Gulgec; Martin Takác 001; Shamim N. Pakzad (J. Comput. Civ. Eng., 2019)

New Convergence Aspects of Stochastic Gradient Algorithms. - Lam M. Nguyen; Phuong Ha Nguyen; Peter Richtárik; Katya Scheinberg; Martin Takác 001; Marten van Dijk (J. Mach. Learn. Res., 2019)

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. - Majid Jahani; Xi He 004; Chenxin Ma; Aryan Mokhtari; Dheevatsa Mudigere; Alejandro Ribeiro; Martin Takác 001 (AISTATS, 2020)

Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. - Zheng Shi; Nur Sila Gulgec; Albert S. Berahas; Shamim N. Pakzad; Martin Takác 001 (ICMLA, 2020)

Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1. - Majid Jahani; MohammadReza Nazari; Sergey Rusakov 001; Albert S. Berahas; Martin Takác 001 (LOD (1), 2020)

Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment. - Nur Sila Gulgec; Martin Takác 001; Shamim N. Pakzad (Comput. Aided Civ. Infrastructure Eng., 2020)

Constrained Combinatorial Optimization with Reinforcement Learning. - Ruben Solozabal; Josu Ceberio; Martin Takác 001 (CoRR, 2020)

DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction. - Soheil Sadeghi Eshkevari; Martin Takác 001; Shamim N. Pakzad; Majid Jahani (CoRR, 2020)

Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure. - Guangyi Liu 004; Arash Amini; Martin Takác 001; Héctor Muñoz-Avila; Nader Motee (CoRR, 2020)

Applying deep learning to the newsvendor problem. - Afshin Oroojlooyjadid; Lawrence V. Snyder; Martin Takác 001 (IISE Trans., 2020)

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. - Aryan Mokhtari; Alec Koppel; Martin Takác 001; Alejandro Ribeiro (J. Mach. Learn. Res., 2020)

A robust multi-batch L-BFGS method for machine learning. - Albert S. Berahas; Martin Takác 001 (Optim. Methods Softw., 2020)

Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. - Peter Richtárik; Martin Takác 001 (SIAM J. Matrix Anal. Appl., 2020)

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. - Majid Jahani; MohammadReza Nazari; Rachael Tappenden; Albert S. Berahas; Martin Takác 001 (AISTATS, 2021)

Improving Text-to-Image Synthesis Using Contrastive Learning. - Hui Ye; Xiulong Yang; Martin Takác 001; Rajshekhar Sunderraman; Shihao Ji 001 (BMVC, 2021)

Classification-Aware Path Planning of Network of Robots. - Guangyi Liu 004; Arash Amini; Martin Takác 001; Nader Motee (DARS, 2021)

Active metric learning for supervised classification. - Krishnan Kumaran; Dimitri J. Papageorgiou; Martin Takác 001; Laurens Lueg; Nicolas V. Sahinidis (Comput. Chem. Eng., 2021)

Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences. - Majid Jahani; Naga Venkata C. Gudapati; Chenxin Ma; Rachael Tappenden; Martin Takác 001 (Comput. Optim. Appl., 2021)

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. - Zheng Shi; Nicolas Loizou; Peter Richtárik; Martin Takác 001 (CoRR, 2021)

An accelerated communication-efficient primal-dual optimization framework for structured machine learning. - Chenxin Ma; Martin Jaggi; Frank E. Curtis; Nathan Srebro; Martin Takác 001 (Optim. Methods Softw., 2021)

Inexact SARAH algorithm for stochastic optimization. - Lam M. Nguyen; Katya Scheinberg; Martin Takác 001 (Optim. Methods Softw., 2021)

Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. - Zhengqing Gao; Huimin Wu; Martin Takác 001; Bin Gu 001 (CIKM, 2022)

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. - Majid Jahani; Sergey Rusakov 001; Zheng Shi; Peter Richtárik; Michael W. Mahoney; Martin Takác 001 (ICLR, 2022)

The power of first-order smooth optimization for black-box non-smooth problems. - Alexander V. Gasnikov; Anton Novitskii; Vasilii Novitskii; Farshed Abdukhakimov; Dmitry Kamzolov; Aleksandr Beznosikov; Martin Takác 001; Pavel E. Dvurechensky; Bin Gu 001 (ICML, 2022)

Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. - Naif Alkhunaizi; Dmitry Kamzolov; Martin Takác 001; Karthik Nandakumar (MICCAI (8), 2022)

A Damped Newton Method Achieves Global $ mathcal O left( frac{1}{k^2} right)$ and Local Quadratic Convergence Rate. - Slavomír Hanzely; Dmitry Kamzolov; Dmitry Pasechnyuk; Alexander V. Gasnikov; Peter Richtárik; Martin Takác 001 (NeurIPS, 2022)

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input. - Guangyi Liu 004; Arash Amini; Martin Takác 001; Nader Motee (CoRR, 2022)

Learning to Control under Time-Varying Environment. - Yuzhen Han; Ruben Solozabal; Jing Dong; Xingyu Zhou; Martin Takác 001; Bin Gu 001 (CoRR, 2022)

Learning to generalize Dispatching rules on the Job Shop Scheduling. - Zangir Iklassov; Dmitrii Medvedev; Ruben Solozabal; Martin Takác 001 (CoRR, 2022)

On Scaled Methods for Saddle Point Problems. - Aleksandr Beznosikov; Aibek Alanov; Dmitry Kovalev; Martin Takác 001; Alexander V. Gasnikov (CoRR, 2022)

FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences. - Artem Agafonov; Brahim Erraji; Martin Takác 001 (CoRR, 2022)

Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points. - Rachael Tappenden; Martin Takác 001 (CoRR, 2022)

Optimal Power Flow Pursuit in the Alternating Current Model. - Jie Liu 036; Antonio Bellon; Andrea Simonetto; Martin Takác 001; Jakub Marecek (CoRR, 2022)

Partial Disentanglement with Partially-Federated GANs (PaDPaF). - Abdulla Jasem Almansoori; Samuel Horváth; Martin Takác 001 (CoRR, 2022)

Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. - Abdurakhmon Sadiev; Ekaterina Borodich; Aleksandr Beznosikov; Darina Dvinskikh; Saveliy Chezhegov; Rachael Tappenden; Martin Takác 001; Alexander V. Gasnikov (EURO J. Comput. Optim., 2022)

Distributed Learning With Sparsified Gradient Differences. - Yicheng Chen 001; Rick S. Blum; Martin Takác 001; Brian M. Sadler (IEEE J. Sel. Top. Signal Process., 2022)

A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization. - Afshin Oroojlooyjadid; MohammadReza Nazari; Lawrence V. Snyder; Martin Takác 001 (Manuf. Serv. Oper. Manag., 2022)

Quasi-Newton methods for machine learning: forget the past, just sample. - Albert S. Berahas; Majid Jahani; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2022)

Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. - Zangir Iklassov; Ikboljon Sobirov; Ruben Solozabal; Martin Takác 001 (ACML, 2023)

Algorithm for Constrained Markov Decision Process with Linear Convergence. - Egor Gladin; Maksim Lavrik-Karmazin; Karina Zainullina; Varvara Rudenko; Alexander V. Gasnikov; Martin Takác 001 (AISTATS, 2023)

SP2 : A Second Order Stochastic Polyak Method. - Shuang Li 003; William J. Swartworth; Martin Takác 001; Deanna Needell; Robert M. Gower (ICLR, 2023)

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. - Nicolas M. Cuadrado; Roberto Alejandro Gutiérrez Guillén; Martin Takác 001 (Tiny Papers @ ICLR, 2023)

On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling. - Zangir Iklassov; Dmitrii Medvedev; Ruben Solozabal Ochoa de Retana; Martin Takác 001 (IJCAI, 2023)

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. - Aleksandr Beznosikov; Martin Takác 001; Alexander V. Gasnikov (NeurIPS, 2023)

Byzantine-Tolerant Methods for Distributed Variational Inequalities. - Nazarii Tupitsa; Abdulla Jasem Almansoori; Yanlin Wu; Martin Takác 001; Karthik Nandakumar; Samuel Horváth; Eduard Gorbunov (NeurIPS, 2023)

Regularization of the Policy Updates for Stabilizing Mean Field Games. - Talal Algumaei; Ruben Solozabal; Réda Alami; Hakim Hacid; Mérouane Debbah; Martin Takác 001 (PAKDD (2), 2023)

Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands. - Zangir Iklassov; Ikboljon Sobirov; Ruben Solozabal; Martin Takác 001 (IEEE Access, 2023)

In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise. - Asma Ahmed Hashmi; Artem Agafonov; Aigerim Zhumabayeva; Mohammad Yaqub; Martin Takác 001 (CoRR, 2023)

MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids. - Nicolas M. Cuadrado; Roberto A. Gutiérrez 001; Yongli Zhu; Martin Takác 001 (CoRR, 2023)

Stochastic Gradient Descent with Preconditioned Polyak Step-size. - Farshed Abdukhakimov; Chulu Xiang; Dmitry Kamzolov; Martin Takác 001 (CoRR, 2023)

SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. - Farshed Abdukhakimov; Chulu Xiang; Dmitry Kamzolov; Robert M. Gower; Martin Takác 001 (CoRR, 2023)

AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. - Zheng Shi; Abdurakhmon Sadiev; Nicolas Loizou; Peter Richtárik; Martin Takác 001 (Trans. Mach. Learn. Res., 2023)

Robustly Train Normalizing Flows via KL Divergence Regularization. - Kun Song; Ruben Solozabal; Hao Li; Martin Takác 001; Lu Ren; Fakhri Karray (AAAI, 2024)

Efficient Conformal Prediction under Data Heterogeneity. - Vincent Plassier; Nikita Kotelevskii; Aleksandr Rubashevskii; Fedor Noskov; Maksim Velikanov; Alexander Fishkov; Samuel Horváth; Martin Takác 001; Eric Moulines; Maxim Panov (AISTATS, 2024)

Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness. - Artem Agafonov; Dmitry Kamzolov; Alexander V. Gasnikov; Ali Kavis; Kimon Antonakopoulos; Volkan Cevher; Martin Takác 001 (ICLR, 2024)

Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. - Nikita Kotelevskii; Samuel Horváth; Karthik Nandakumar; Martin Takác 001; Maxim Panov (IJCAI, 2024)

Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations. - Artem Agafonov; Petr Ostroukhov; Roman Mozhaev; Konstantin Yakovlev; Eduard Gorbunov; Martin Takác 001; Alexander V. Gasnikov; Dmitry Kamzolov (NeurIPS, 2024)

Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. - Sayantan Choudhury; Nazarii Tupitsa; Nicolas Loizou; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (NeurIPS, 2024)

Self-Guiding Exploration for Combinatorial Problems. - Zangir Iklassov; Yali Du 001; Farkhad Akimov; Martin Takác 001 (NeurIPS, 2024)

Federated Learning Can Find Friends That Are Beneficial. - Nazarii Tupitsa; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (CoRR, 2024)

AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size. - Petr Ostroukhov; Aigerim Zhumabayeva; Chulu Xiang; Alexander V. Gasnikov; Martin Takác 001; Dmitry Kamzolov (CoRR, 2024)

Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows. - Zangir Iklassov; Ikboljon Sobirov; Ruben Solozabal; Martin Takác 001 (CoRR, 2024)

Generalized Policy Learning for Smart Grids: FL TRPO Approach. - Yunxiang Li; Nicolas Mauricio Cuadrado; Samuel Horváth; Martin Takác 001 (CoRR, 2024)

Enhancing Policy Gradient with the Polyak Step-Size Adaption. - Yunxiang Li; Rui Yuan; Chen Fan; Mark Schmidt 001; Samuel Horváth; Robert M. Gower; Martin Takác 001 (CoRR, 2024)

Local Methods with Adaptivity via Scaling. - Saveliy Chezhegov; Sergey Skorik; Nikolas Khachaturov; Danil Shalagin; Aram Avetisyan; Aleksandr Beznosikov; Martin Takác 001; Yaroslav Kholodov; Alexander V. Gasnikov (CoRR, 2024)

Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. - Saveliy Chezhegov; Yaroslav Klyukin; Andrei Semenov; Aleksandr Beznosikov; Alexander V. Gasnikov; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (CoRR, 2024)

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models. - Samar Fares; Klea Ziu; Toluwani Aremu; Nikita Durasov; Martin Takác 001; Pascal Fua; Karthik Nandakumar; Ivan Laptev (CoRR, 2024)

Methods for Convex (L0,L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (CoRR, 2024)

Enhance Hyperbolic Representation Learning via Second-order Pooling. - Kun Song; Ruben Solozabal; Li hao; Lu Ren; Moloud Abdar; Qing Li 038; Fakhri Karray; Martin Takác 001 (CoRR, 2024)

ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. - Klea Ziu; Slavomír Hanzely; Loka Li; Kun Zhang 001; Martin Takác 001; Dmitry Kamzolov (CoRR, 2024)

FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. - Philip Zmushko; Aleksandr Beznosikov; Martin Takác 001; Samuel Horváth (CoRR, 2024)

Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO. - Nicolas Mauricio Cuadrado; Samuel Horváth; Martin Takác 001 (CoRR, 2024)

Inexact tensor methods and their application to stochastic convex optimization. - Artem Agafonov; Dmitry Kamzolov; Pavel E. Dvurechensky; Alexander V. Gasnikov; Martin Takác 001 (Optim. Methods Softw., 2024)

Latency-aware placement of vehicular metaverses using virtual network functions. - Fatima A. Alkhoori; Latif U. Khan; Mohsen Guizani; Martin Takác 001 (Simul. Model. Pract. Theory, 2024)

PaDPaF: Partial Disentanglement with Partially-Federated GANs. - Abdulla Jasem Almansoori; Samuel Horváth; Martin Takác 001 (Trans. Mach. Learn. Res., 2024)

Random-reshuffled SARAH does not need full gradient computations. - Aleksandr Beznosikov; Martin Takác 001 (Optim. Lett., 2024)

Stochastic Gradient Methods with Preconditioned Updates. - Abdurakhmon Sadiev; Aleksandr Beznosikov; Abdulla Jasem Almansoori; Dmitry Kamzolov; Rachael Tappenden; Martin Takác 001 (J. Optim. Theory Appl., 2024)

Preconditioning meets biased compression for efficient distributed optimization. - Vitali Pirau; Aleksandr Beznosikov; Martin Takác 001; Vladislav Matyukhin; Alexander V. Gasnikov (Comput. Manag. Sci., 2024)

Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles. - Munachiso Nwadike; Zangir Iklassov; Toluwani Aremu; Tatsuya Hiraoka; Benjamin Heinzerling; Velibor Bojkovic; Hilal AlQuabeh; Martin Takác 001; Kentaro Inui (ACL (1), 2025)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. - Ruichen Luo; Sebastian U. Stich; Samuel Horváth; Martin Takác 001 (AISTATS, 2025)

Collaborative and Efficient Personalization with Mixtures of Adaptors. - Abdulla Jasem Almansoori; Samuel Horváth; Martin Takác 001 (CPAL, 2025)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning. - Nurbek Tastan; Samuel Horváth; Martin Takác 001; Karthik Nandakumar (CPAL, 2025)

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. - Yury Demidovich; Petr Ostroukhov; Grigory Malinovsky; Samuel Horváth; Martin Takác 001; Peter Richtárik; Eduard Gorbunov (ICLR, 2025)

Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. - Eduard Gorbunov; Nazarii Tupitsa; Sayantan Choudhury; Alen Aliev; Peter Richtárik; Samuel Horváth; Martin Takác 001 (ICLR, 2025)

OPTAMI: Global Superlinear Convergence of High-order Methods. - Dmitry Kamzolov; Artem Agafonov; Dmitry Pasechnyuk; Alexander V. Gasnikov; Martin Takác 001 (ICLR, 2025)

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation. - Nikita Kotelevskii; Vladimir Kondratyev; Martin Takác 001; Eric Moulines; Maxim Panov (ICLR, 2025)

Learning Confident Classifiers in the Presence of Label Noise. - Asma Ahmed Hashmi; Aigerim Zhumabayeva; Nikita Kotelevskii; Artem Agafonov; Mohammad Yaqub; Maxim Panov; Martin Takác 001 (SDM, 2025)

RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles. - Munachiso Nwadike; Zangir Iklassov; Toluwani Aremu; Tatsuya Hiraoka; Velibor Bojkovic; Benjamin Heinzerling; Hilal Alqaubeh; Martin Takác 001; Kentaro Inui (CoRR, 2025)

Knowledge Distillation from Large Language Models for Household Energy Modeling. - Mohannad Takrouri; Nicolas M. Cuadrado; Martin Takác 001 (CoRR, 2025)

Fishing For Cheap And Efficient Pruners At Initialization. - Ivo Gollini Navarrete; Nicolas Mauricio Cuadrado; Jose Renato Restom; Martin Takác 001; Samuel Horváth (CoRR, 2025)

Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features. - Nikolai Rekut; Alexey Orlov; Klea Ziu; Elizaveta Starykh; Martin Takác 001; Aleksandr Beznosikov (CoRR, 2025)

MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search. - Lotfi Abdelkrim Mecharbat; Ibrahim Almakky; Martin Takác 001; Mohammad Yaqub (CoRR, 2025)

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy. - Gleb Molodtsov; Daniil Medyakov; Sergey Skorik; Nikolas Khachaturov; Shahane Tigranyan; Vladimir Aletov; Aram Avetisyan; Martin Takác 001; Aleksandr Beznosikov (CoRR, 2025)

LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs. - Omar Choukrani; Idriss Malek; Daniil Orel; Zhuohan Xie; Zangir Iklassov; Martin Takác 001; Salem Lahlou (CoRR, 2025)

LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning. - Nurbek Tastan; Stefanos Laskaridis; Martin Takác 001; Karthik Nandakumar; Samuel Horváth (CoRR, 2025)

SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem. - Ahmed Heakl; Yahia Salaheldin Shaaban; Martin Takác 001; Salem Lahlou; Zangir Iklassov (CoRR, 2025)