Martin Takác - RankMe

220 papers A* 27A 9C 2Journal 165Unranked 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accelerated Adaptive Cubic Regularized Quasi-Newton Methods. J. Optim. Theory Appl. Dmitry Kamzolov, Klea Ziu, Artem Agafonov, Martin Takác
2026	A*	`conf`	Bant: Byzantine Antidote via Trial Function and Trust Scores. AAAI Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC. CoRR Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov, Aleksandr Beznosikov, Samuel Horváth, Martin Takác
2026	J	`jnl`	Byzantine-Robust Optimization under (L CoRR Arman Bolatov, Samuel Horváth, Martin Takác, Eduard Gorbunov
2026	J	`jnl`	CORE: Collaborative Reasoning via Cross Teaching. CoRR Kshitij Mishra, Mirat Aubakirov, Martin Takác, Nils Lukas, Salem Lahlou
2026	J	`jnl`	Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation. CoRR Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy, Mikhail Rudakov, Ruslan Nazykov, Alexander Rogozin, Anton Vakhrushev, Andrey V. Savchenko, Martin Takác, Alexander V. Gasnikov
2026	J	`jnl`	Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation. J. Optim. Theory Appl. Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy, Mikhail Rudakov, Ruslan Nazykov, Alexander Rogozin, Anton Vakhrushev, Andrey V. Savchenko, Martin Takác, Alexander V. Gasnikov
2026	J	`jnl`	Guess & Guide: Gradient-Free Zero-Shot Diffusion Guidance. CoRR Abduragim Shtanchaev, Albina Ilina, Yazid Janati, Arip Asadulaev, Martin Takác, Eric Moulines
2026	J	`jnl`	Thinking Like a CHEMIST: Combined Heterogeneous Embedding Model Integrating Structure and Tokens. IEEE Access Nikolai Rekut, Alexey A. Orlov, Klea Ziu, Elizaveta Starykh, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention. CoRR Ruben Solozabal, Velibor Bojkovic, Hilal AlQuabeh, Klea Ziu, Kentaro Inui, Martin Takác
2026	J	`jnl`	Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers. CoRR Artem Riabinin, Andrey Veprikov, Arman Bolatov, Martin Takác, Aleksandr Beznosikov
2026	J	`jnl`	Zero-Shot Off-Policy Learning. CoRR Arip Asadulaev, Maksim Bobrin, Salem Lahlou, Dmitry V. Dylov, Fakhri Karray, Martin Takác
2025	J	`jnl`	Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features. CoRR Nikolai Rekut, Alexey A. Orlov, Klea Ziu, Elizaveta Starykh, Martin Takác, Aleksandr Beznosikov
2025	A*	`conf`	Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed. ICML Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov
2025	—	`conf`	Collaborative and Efficient Personalization with Mixtures of Adaptors. CPAL Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
2025	J	`jnl`	Deep Improvement Supervision. CoRR Arip Asadulaev, Rayan Banerjee, Fakhri Karray, Martin Takác
2025	J	`jnl`	Distributed learning with compressed gradient differences. Optim. Methods Softw. Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik
2025	J	`jnl`	Expert or not? assessing data quality in offline reinforcement learning. CoRR Arip Asadulaev, Fakhri Karray, Martin Takác
2025	J	`jnl`	FLECS: a federated learning second-order framework via compression and sketching. Optim. Methods Softw. Artem Agafonov, Dmitry Kamzolov, Rachael Tappenden, Alexander V. Gasnikov, Martin Takác
2025	A*	`conf`	FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. ICML Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth
2025	J	`jnl`	Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update. CoRR Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov, Aleksandr Beznosikov, Samuel Horváth, Martin Takác
2025	—	`conf`	FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning. CPAL Nurbek Tastan, Samuel Horváth, Martin Takác, Karthik Nandakumar
2025	J	`jnl`	Fishing For Cheap And Efficient Pruners At Initialization. CoRR Ivo Gollini Navarrete, Nicolas Mauricio Cuadrado, Jose Renato Restom, Martin Takác, Samuel Horváth
2025	A*	`conf`	From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation. ICLR Nikita Kotelevskii, Vladimir Kondratyev, Martin Takác, Eric Moulines, Maxim Panov
2025	J	`jnl`	Knowledge Distillation from Large Language Models for Household Energy Modeling. CoRR Mohannad Takrouri, Nicolas M. Cuadrado, Martin Takác
2025	J	`jnl`	LLM-BABYBENCH: Understanding and Evaluating Grounded Planning and Reasoning in LLMs. CoRR Omar Choukrani, Idriss Malek, Daniil Orel, Zhuohan Xie, Zangir Iklassov, Martin Takác, Salem Lahlou
2025	A	`conf`	Learning Confident Classifiers in the Presence of Label Noise. SDM Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii, Artem Agafonov, Mohammad Yaqub, Maxim Panov, Martin Takác
2025	—	`conf`	Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles. ACL (1) Munachiso Nwadike, Zangir Iklassov, Toluwani Aremu, Tatsuya Hiraoka, Benjamin Heinzerling, Velibor Bojkovic, Hilal AlQuabeh, Martin Takác, Kentaro Inui
2025	J	`jnl`	LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning. CoRR Nurbek Tastan, Stefanos Laskaridis, Martin Takác, Karthik Nandakumar, Samuel Horváth
2025	—	`conf`	MedNNS: Supernet-Based Medical Task-Adaptive Neural Network Search. MICCAI (6) Lotfi Abdelkrim Mecharbat, Ibrahim Almakky, Martin Takác, Mohammad Yaqub
2025	J	`jnl`	MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search. CoRR Lotfi Abdelkrim Mecharbat, Ibrahim Almakky, Martin Takác, Mohammad Yaqub
2025	A*	`conf`	Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity. ICLR Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác
2025	A*	`conf`	Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. ICLR Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horváth, Martin Takác, Peter Richtárik, Eduard Gorbunov
2025	A*	`conf`	OPTAMI: Global Superlinear Convergence of High-order Methods. ICLR Dmitry Kamzolov, Artem Agafonov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Martin Takác
2025	J	`jnl`	Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods. CoRR Andrey Veprikov, Arman Bolatov, Samuel Horváth, Aleksandr Beznosikov, Martin Takác, Slavomír Hanzely
2025	J	`jnl`	RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles. CoRR Munachiso Nwadike, Zangir Iklassov, Toluwani Aremu, Tatsuya Hiraoka, Velibor Bojkovic, Benjamin Heinzerling, Hilal Alqaubeh, Martin Takác, Kentaro Inui
2025	A	`conf`	Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. AISTATS Ruichen Luo, Sebastian U. Stich, Samuel Horváth, Martin Takác
2025	J	`jnl`	Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. CoRR Ruichen Luo, Sebastian U. Stich, Samuel Horváth, Martin Takác
2025	J	`jnl`	SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem. CoRR Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takác, Salem Lahlou, Zangir Iklassov
2025	J	`jnl`	Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees. CoRR Artem Agafonov, Vladislav Ryspayev, Samuel Horváth, Alexander V. Gasnikov, Martin Takác, Slavomír Hanzely
2025	J	`jnl`	The AI Data Scientist. CoRR Farkhad Akimov, Munachiso Nwadike, Zangir Iklassov, Martin Takác
2025	J	`jnl`	Tractable Probabilistic Models for Investment Planning. CoRR Nicolas M. Cuadrado, Mohannad Takrouri, Jiri Nemecek, Martin Takác, Jakub Marecek
2025	J	`jnl`	Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy. CoRR Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takác, Aleksandr Beznosikov
2025	J	`jnl`	Uncovering the Spectral Bias in Diagonal State Space Models. CoRR Ruben Solozabal, Velibor Bojkovic, Hilal AlQuabeh, Kentaro Inui, Martin Takác
2025	J	`jnl`	Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning. CoRR Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horváth, Martin Takác
2025	J	`jnl`	Y-shaped Generative Flows. CoRR Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev, Eric Moulines, Fakhri Karray, Martin Takác
2024	J	`jnl`	AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size. CoRR Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang, Alexander V. Gasnikov, Martin Takác, Dmitry Kamzolov
2024	A*	`conf`	Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness. ICLR Artem Agafonov, Dmitry Kamzolov, Alexander V. Gasnikov, Ali Kavis, Kimon Antonakopoulos, Volkan Cevher, Martin Takác
2024	J	`jnl`	Collaborative and Efficient Personalization with Mixtures of Adaptors. CoRR Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
2024	A*	`conf`	Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. IJCAI Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
2024	A	`conf`	Efficient Conformal Prediction under Data Heterogeneity. AISTATS Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
2024	J	`jnl`	Enhance Hyperbolic Representation Learning via Second-order Pooling. CoRR Kun Song, Ruben Solozabal, Li Hao, Lu Ren, Moloud Abdar, Qing Li, Fakhri Karray, Martin Takác
2024	J	`jnl`	Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác
2024	A*	`conf`	Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations. NeurIPS Artem Agafonov, Petr Ostroukhov, Roman Mozhaev, Konstantin Yakovlev, Eduard Gorbunov, Martin Takác, Alexander V. Gasnikov, Dmitry Kamzolov
2024	J	`jnl`	FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. CoRR Nicolas Mauricio Cuadrado, Roberto Alejandro Gutiérrez Guillén, Martin Takác
2024	J	`jnl`	FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. CoRR Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth
2024	J	`jnl`	FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning. CoRR Nurbek Tastan, Samuel Horváth, Martin Takác, Karthik Nandakumar
2024	J	`jnl`	Federated Learning Can Find Friends That Are Beneficial. CoRR Nazarii Tupitsa, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	J	`jnl`	Generalized Policy Learning for Smart Grids: FL TRPO Approach. CoRR Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takác
2024	J	`jnl`	Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO. CoRR Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takác
2024	J	`jnl`	Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. CoRR Saveliy Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	J	`jnl`	Inexact tensor methods and their application to stochastic convex optimization. Optim. Methods Softw. Artem Agafonov, Dmitry Kamzolov, Pavel E. Dvurechensky, Alexander V. Gasnikov, Martin Takác
2024	J	`jnl`	Latency-aware placement of vehicular metaverses using virtual network functions. Simul. Model. Pract. Theory Fatima A. Alkhoori, Latif U. Khan, Mohsen Guizani, Martin Takác
2024	J	`jnl`	Local Methods with Adaptivity via Scaling. CoRR Saveliy Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Aleksandr Beznosikov, Martin Takác, Yaroslav Kholodov, Alexander V. Gasnikov
2024	J	`jnl`	Methods for Convex (L CoRR Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury, Alen Aliev, Peter Richtárik, Samuel Horváth, Martin Takác
2024	J	`jnl`	Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. CoRR Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horváth, Martin Takác, Peter Richtárik, Eduard Gorbunov
2024	J	`jnl`	MirrorCheck: Efficient Adversarial Defense for Vision-Language Models. CoRR Samar Fares, Klea Ziu, Toluwani Aremu, Nikita Durasov, Martin Takác, Pascal Fua, Karthik Nandakumar, Ivan Laptev
2024	J	`jnl`	PaDPaF: Partial Disentanglement with Partially-Federated GANs. Trans. Mach. Learn. Res. Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
2024	J	`jnl`	Preconditioning meets biased compression for efficient distributed optimization. Comput. Manag. Sci. Vitali Pirau, Aleksandr Beznosikov, Martin Takác, Vladislav Matyukhin, Alexander V. Gasnikov
2024	J	`jnl`	Random-reshuffled SARAH does not need full gradient computations. Optim. Lett. Aleksandr Beznosikov, Martin Takác
2024	J	`jnl`	Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows. CoRR Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác
2024	A*	`conf`	Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. NeurIPS Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	J	`jnl`	Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. CoRR Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov
2024	A*	`conf`	Robustly Train Normalizing Flows via KL Divergence Regularization. AAAI Kun Song, Ruben Solozabal, Hao Li, Martin Takác, Lu Ren, Fakhri Karray
2024	A*	`conf`	Self-Guiding Exploration for Combinatorial Problems. NeurIPS Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takác
2024	J	`jnl`	Self-Guiding Exploration for Combinatorial Problems. CoRR Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takác
2024	J	`jnl`	Stochastic Gradient Methods with Preconditioned Updates. J. Optim. Theory Appl. Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác
2024	J	`jnl`	ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. CoRR Klea Ziu, Slavomír Hanzely, Loka Li, Kun Zhang, Martin Takác, Dmitry Kamzolov
2023	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. Trans. Mach. Learn. Res. Zheng Shi, Abdurakhmon Sadiev, Nicolas Loizou, Peter Richtárik, Martin Takác
2023	A	`conf`	Algorithm for Constrained Markov Decision Process with Linear Convergence. AISTATS Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác
2023	A*	`conf`	Byzantine-Tolerant Methods for Distributed Variational Inequalities. NeurIPS Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
2023	J	`jnl`	Byzantine-Tolerant Methods for Distributed Variational Inequalities. CoRR Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
2023	J	`jnl`	Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
2023	J	`jnl`	Efficient Conformal Prediction under Data Heterogeneity. CoRR Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
2023	—	`conf`	FRESCO: Federated Reinforcement Energy System for Cooperative Optimization. Tiny Papers @ ICLR Nicolas M. Cuadrado, Roberto Alejandro Gutiérrez Guillén, Martin Takác
2023	J	`jnl`	In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise. CoRR Asma Ahmed Hashmi, Artem Agafonov, Aigerim Zhumabayeva, Mohammad Yaqub, Martin Takác
2023	J	`jnl`	MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids. CoRR Nicolas M. Cuadrado, Roberto A. Gutiérrez, Yongli Zhu, Martin Takác
2023	A*	`conf`	On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling. IJCAI Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal Ochoa de Retana, Martin Takác
2023	—	`conf`	Regularization of the Policy Updates for Stabilizing Mean Field Games. PAKDD (2) Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác
2023	J	`jnl`	Regularization of the policy updates for stabilizing Mean Field Games. CoRR Talal Algumaei, Ruben Solozabal, Réda Alami, Hakim Hacid, Mérouane Debbah, Martin Takác
2023	J	`jnl`	Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands. IEEE Access Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác
2023	C	`conf`	Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. ACML Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác
2023	J	`jnl`	Reinforcement Learning for Solving Stochastic Vehicle Routing Problem. CoRR Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takác
2023	J	`jnl`	SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. CoRR Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Robert M. Gower, Martin Takác
2023	A*	`conf`	SP2 : A Second Order Stochastic Polyak Method. ICLR Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower
2023	A*	`conf`	Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. NeurIPS Aleksandr Beznosikov, Martin Takác, Alexander V. Gasnikov
2023	J	`jnl`	Stochastic Gradient Descent with Preconditioned Polyak Step-size. CoRR Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Martin Takác
2022	A*	`conf`	A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate. NeurIPS Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander V. Gasnikov, Peter Richtárik, Martin Takác
2022	J	`jnl`	A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization. Manuf. Serv. Oper. Manag. Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V. Snyder, Martin Takác
2022	J	`jnl`	Algorithm for Constrained Markov Decision Process with Linear Convergence. CoRR Egor Gladin, Maksim Lavrik-Karmazin, Karina Zainullina, Varvara Rudenko, Alexander V. Gasnikov, Martin Takác
2022	J	`jnl`	Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. EURO J. Comput. Optim. Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takác, Alexander V. Gasnikov
2022	J	`jnl`	Distributed Learning With Sparsified Gradient Differences. CoRR Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler
2022	J	`jnl`	Distributed Learning With Sparsified Gradient Differences. IEEE J. Sel. Top. Signal Process. Yicheng Chen, Rick S. Blum, Martin Takác, Brian M. Sadler
2022	A*	`conf`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. ICLR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2022	J	`jnl`	FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences. CoRR Artem Agafonov, Brahim Erraji, Martin Takác
2022	J	`jnl`	Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points. CoRR Rachael Tappenden, Martin Takác
2022	J	`jnl`	Learning to Control under Time-Varying Environment. CoRR Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takác, Bin Gu
2022	J	`jnl`	Learning to generalize Dispatching rules on the Job Shop Scheduling. CoRR Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takác
2022	J	`jnl`	On Scaled Methods for Saddle Point Problems. CoRR Aleksandr Beznosikov, Aibek Alanov, Dmitry Kovalev, Martin Takác, Alexander V. Gasnikov
2022	J	`jnl`	Optimal Power Flow Pursuit in the Alternating Current Model. CoRR Jie Liu, Antonio Bellon, Andrea Simonetto, Martin Takác, Jakub Marecek
2022	J	`jnl`	Partial Disentanglement with Partially-Federated GANs (PaDPaF). CoRR Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
2022	J	`jnl`	Quasi-Newton methods for machine learning: forget the past, just sample. Optim. Methods Softw. Albert S. Berahas, Majid Jahani, Peter Richtárik, Martin Takác
2022	J	`jnl`	Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input. CoRR Guangyi Liu, Arash Amini, Martin Takác, Nader Motee
2022	J	`jnl`	SP2: A Second Order Stochastic Polyak Method. CoRR Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower
2022	J	`jnl`	Stochastic Gradient Methods with Preconditioned Updates. CoRR Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori, Dmitry Kamzolov, Rachael Tappenden, Martin Takác
2022	—	`conf`	Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. MICCAI (8) Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar
2022	J	`jnl`	Suppressing Poisoning Attacks on Federated Learning for Medical Imaging. CoRR Naif Alkhunaizi, Dmitry Kamzolov, Martin Takác, Karthik Nandakumar
2022	A*	`conf`	The power of first-order smooth optimization for black-box non-smooth problems. ICML Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu
2022	A	`conf`	Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints. CIKM Zhengqing Gao, Huimin Wu, Martin Takác, Bin Gu
2021	J	`jnl`	AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods. CoRR Zheng Shi, Nicolas Loizou, Peter Richtárik, Martin Takác
2021	J	`jnl`	Active metric learning for supervised classification. Comput. Chem. Eng. Krishnan Kumaran, Dimitri J. Papageorgiou, Martin Takác, Laurens Lueg, Nicolas V. Sahinidis
2021	J	`jnl`	An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
2021	—	`conf`	Classification-Aware Path Planning of Network of Robots. DARS Guangyi Liu, Arash Amini, Martin Takác, Nader Motee
2021	J	`jnl`	Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information. CoRR Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takác
2021	J	`jnl`	Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences. Comput. Optim. Appl. Majid Jahani, Naga Venkata C. Gudapati, Chenxin Ma, Rachael Tappenden, Martin Takác
2021	A	`conf`	Improving Text-to-Image Synthesis Using Contrastive Learning. BMVC Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji
2021	J	`jnl`	Improving Text-to-Image Synthesis Using Contrastive Learning. CoRR Hui Ye, Xiulong Yang, Martin Takác, Rajshekhar Sunderraman, Shihao Ji
2021	J	`jnl`	Inexact SARAH algorithm for stochastic optimization. Optim. Methods Softw. Lam M. Nguyen, Katya Scheinberg, Martin Takác
2021	J	`jnl`	Random-reshuffled SARAH does not need a full gradient computations. CoRR Aleksandr Beznosikov, Martin Takác
2021	A	`conf`	SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. AISTATS Majid Jahani, MohammadReza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takác
2020	J	`jnl`	A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro
2020	J	`jnl`	A robust multi-batch L-BFGS method for machine learning. Optim. Methods Softw. Albert S. Berahas, Martin Takác
2020	J	`jnl`	Applying deep learning to the newsvendor problem. IISE Trans. Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác
2020	J	`jnl`	Constrained Combinatorial Optimization with Reinforcement Learning. CoRR Ruben Solozabal, Josu Ceberio, Martin Takác
2020	J	`jnl`	DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction. CoRR Soheil Sadeghi Eshkevari, Martin Takác, Shamim N. Pakzad, Majid Jahani
2020	A	`conf`	Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. AISTATS Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
2020	C	`conf`	Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. ICMLA Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takác
2020	J	`jnl`	Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations. CoRR Zheng Shi, Nur Sila Gulgec, Albert S. Berahas, Shamim N. Pakzad, Martin Takác
2020	J	`jnl`	Reinforcement Learning based Multi-Robot Classification via Scalable Communication Structure. CoRR Guangyi Liu, Arash Amini, Martin Takác, Héctor Muñoz-Avila, Nader Motee
2020	—	`conf`	Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1. LOD (1) Majid Jahani, MohammadReza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takác
2020	J	`jnl`	Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. SIAM J. Matrix Anal. Appl. Peter Richtárik, Martin Takác
2020	J	`jnl`	Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment. Comput. Aided Civ. Infrastructure Eng. Nur Sila Gulgec, Martin Takác, Shamim N. Pakzad
2019	J	`jnl`	A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning. CoRR Hossein K. Mousavi, Guangyi Liu, Weihang Yuan, Martin Takác, Héctor Muñoz-Avila, Nader Motee
2019	J	`jnl`	Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization. J. Comput. Civ. Eng. Nur Sila Gulgec, Martin Takác, Shamim N. Pakzad
2019	J	`jnl`	Distributed Fixed Point Methods with Compressed Iterates. CoRR Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takác
2019	J	`jnl`	Distributed Learning with Compressed Gradient Differences. CoRR Konstantin Mishchenko, Eduard Gorbunov, Martin Takác, Peter Richtárik
2019	J	`jnl`	Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework. CoRR MohammadReza Nazari, Majid Jahani, Lawrence V. Snyder, Martin Takác
2019	A*	`conf`	Entropy-Penalized Semidefinite Programming. IJCAI Mikhail Krechetov, Jakub Marecek, Yury Maximov, Martin Takác
2019	J	`jnl`	FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods. CoRR Nur Sila Gulgec, Zheng Shi, Neil Deshmukh, Shamim N. Pakzad, Martin Takác
2019	A	`conf`	Multi-Agent Image Classification via Reinforcement Learning. IROS Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee
2019	J	`jnl`	Multi-Agent Image Classification via Reinforcement Learning. CoRR Hossein K. Mousavi, MohammadReza Nazari, Martin Takác, Nader Motee
2019	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. J. Mach. Learn. Res. Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2019	J	`jnl`	Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample. CoRR Albert S. Berahas, Majid Jahani, Martin Takác
2018	J	`jnl`	Active Metric Learning for Supervised Classification. CoRR Krishnan Kumaran, Dimitri J. Papageorgiou, Yutong Chang, Minhan Li, Martin Takác
2018	J	`jnl`	Deep Reinforcement Learning for Solving the Vehicle Routing Problem. CoRR MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác
2018	J	`jnl`	Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization. Frontiers Appl. Math. Stat. Xi He, Rachael Tappenden, Martin Takác
2018	J	`jnl`	Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. CoRR Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
2018	J	`jnl`	Inexact SARAH Algorithm for Stochastic Optimization. CoRR Lam M. Nguyen, Katya Scheinberg, Martin Takác
2018	—	`conf`	Matrix Completion Under Interval Uncertainty: Highlights. ECML/PKDD (3) Jakub Marecek, Peter Richtárik, Martin Takác
2018	J	`jnl`	New Convergence Aspects of Stochastic Gradient Algorithms. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk
2018	J	`jnl`	On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches. CoRR Jie Liu, Yu Rong, Martin Takác, Junzhou Huang
2018	J	`jnl`	On the complexity of parallel coordinate descent. Optim. Methods Softw. Rachael Tappenden, Martin Takác, Peter Richtárik
2018	A*	`conf`	Reinforcement Learning for Solving the Vehicle Routing Problem. NeurIPS MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác
2018	A*	`conf`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. ICML Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2018	J	`jnl`	SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. CoRR Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác
2017	J	`jnl`	A Deep Q-Network for the Beer Game with Partial Information. CoRR Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V. Snyder, Martin Takác
2017	J	`jnl`	A Robust Multi-Batch L-BFGS Method for Machine Learning. CoRR Albert S. Berahas, Martin Takác
2017	J	`jnl`	A low-rank coordinate-descent algorithm for semidefinite programming relaxations of optimal power flow. Optim. Methods Softw. Jakub Marecek, Martin Takác
2017	J	`jnl`	An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
2017	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. J. Mach. Learn. Res. Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2017	—	`conf`	Distributed Hessian-Free Optimization for Deep Neural Network. AAAI Workshops Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
2017	—	`conf`	Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing. AAAI Workshops Chenxin Ma, Martin Takác
2017	J	`jnl`	Distributed optimization with arbitrary local solvers. Optim. Methods Softw. Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2017	J	`jnl`	Hybrid Methods in Solving Alternating-Current Optimal Power Flows. IEEE Trans. Smart Grid Jie Liu, Alan C. Liddell Jr., Jakub Marecek, Martin Takác
2017	J	`jnl`	Matrix completion under interval uncertainty. Eur. J. Oper. Res. Jakub Marecek, Peter Richtárik, Martin Takác
2017	A*	`conf`	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. ICML Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2017	J	`jnl`	SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient. CoRR Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2017	J	`jnl`	Stochastic Recursive Gradient Algorithm for Nonconvex Optimization. CoRR Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác
2017	J	`jnl`	Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory. CoRR Peter Richtárik, Martin Takác
2017	J	`jnl`	Stock-out Prediction in Multi-echelon Networks. CoRR Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác
2017	J	`jnl`	Underestimate Sequences via Quadratic Averaging. CoRR Chenxin Ma, Naga Venkata C. Gudapati, Majid Jahani, Rachael Tappenden, Martin Takác
2016	—	`conf`	A Multi-Batch L-BFGS Method for Machine Learning. NIPS Albert S. Berahas, Jorge Nocedal, Martin Takác
2016	J	`jnl`	A Multi-Batch L-BFGS Method for Machine Learning. CoRR Albert S. Berahas, Jorge Nocedal, Martin Takác
2016	J	`jnl`	Applying Deep Learning to the Newsvendor Problem. CoRR Afshin Oroojlooyjadid, Lawrence V. Snyder, Martin Takác
2016	J	`jnl`	CoCoA: A General Framework for Communication-Efficient Distributed Optimization. CoRR Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, Martin Jaggi
2016	J	`jnl`	Distributed Coordinate Descent Method for Learning with Big Data. J. Mach. Learn. Res. Peter Richtárik, Martin Takác
2016	J	`jnl`	Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing. CoRR Chenxin Ma, Martin Takác
2016	J	`jnl`	Large Scale Distributed Hessian-Free Optimization for Deep Neural Network. CoRR Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takác
2016	J	`jnl`	Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption. J. Mach. Learn. Res. Chenxin Ma, Rachael Tappenden, Martin Takác
2016	J	`jnl`	Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. IEEE J. Sel. Top. Signal Process. Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2016	J	`jnl`	On optimal probabilities in stochastic coordinate descent methods. Optim. Lett. Peter Richtárik, Martin Takác
2016	J	`jnl`	Parallel coordinate descent methods for big data optimization. Math. Program. Peter Richtárik, Martin Takác
2016	A*	`conf`	Primal-Dual Rates and Certificates. ICML Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi
2016	J	`jnl`	Primal-Dual Rates and Certificates. CoRR Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi
2016	J	`jnl`	Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption. CoRR Jie Liu, Martin Takác
2016	A*	`conf`	SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. ICML Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq
2015	A*	`conf`	Adding vs. Averaging in Distributed Primal-Dual Optimization. ICML Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Adding vs. Averaging in Distributed Primal-Dual Optimization. CoRR Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Distributed Mini-Batch SDCA. CoRR Martin Takác, Peter Richtárik, Nathan Srebro
2015	J	`jnl`	Distributed Optimization with Arbitrary Local Solvers. CoRR Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác
2015	J	`jnl`	Dual Free SDCA for Empirical Risk Minimization with Adaptive Probabilities. CoRR Xi He, Martin Takác
2015	J	`jnl`	Linear Convergence of the Randomized Feasible Descent Method Under the Weak Strong Convexity Assumption. CoRR Chenxin Ma, Rachael Tappenden, Martin Takác
2015	J	`jnl`	Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2015	J	`jnl`	Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization? CoRR Chenxin Ma, Martin Takác
2015	J	`jnl`	SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization. CoRR Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq
2014	—	`conf`	Communication-Efficient Distributed Dual Coordinate Ascent. NIPS Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2014	J	`jnl`	Communication-Efficient Distributed Dual Coordinate Ascent. CoRR Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
2014	J	`jnl`	Fast Distributed Coordinate Descent for Non-Strongly Convex Losses. CoRR Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác
2014	—	`conf`	Fast distributed coordinate descent for non-strongly convex losses. MLSP Olivier Fercoq, Zheng Qu, Peter Richtárik, Martin Takác
2014	J	`jnl`	Inequality-Constrained Matrix Completion: Adding the Obvious Helps! CoRR Martin Takác, Jakub Marecek, Peter Richtárik
2014	J	`jnl`	Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Program. Peter Richtárik, Martin Takác
2014	J	`jnl`	mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. CoRR Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác
2013	J	`jnl`	Distributed Coordinate Descent Method for Learning with Big Data. CoRR Peter Richtárik, Martin Takác
2013	J	`jnl`	Mini-Batch Primal and Dual Methods for SVMs CoRR Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nathan Srebro
2013	—	`conf`	Mini-Batch Primal and Dual Methods for SVMs. ICML (3) Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nati Srebro
2013	J	`jnl`	On Optimal Probabilities in Stochastic Coordinate Descent Methods. CoRR Peter Richtárik, Martin Takác
2013	J	`jnl`	TOP-SPIN: TOPic discovery via Sparse Principal component INterference. CoRR Martin Takác, Selin Damla Ahipasaoglu, Ngai-Man Cheung, Peter Richtárik
2012	J	`jnl`	Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes CoRR Peter Richtárik, Martin Takác, Selin Damla Ahipasaoglu
2012	J	`jnl`	Parallel Coordinate Descent Methods for Big Data Optimization CoRR Peter Richtárik, Martin Takác
2011	—	`conf`	Efficient Serial and Parallel Coordinate Descent Methods for Huge-Scale Truss Topology Design. OR Peter Richtárik, Martin Takác