Martin Takac

47 papers · 2013–2026 · 13 conferences · across top CS/AI conferences

Achievements

+14 more ↓

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (14) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (13)

🗺️ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (10) 👑 Triple Crown 🏆 Grand Slam 🔬 Deep Specialist (17) 🏆 Keyword Champion 🚀 Conference Pioneer ⚡ Prolific Year (7) 🗃️ Keyword Collector (147) 📈 Trend Setter 💎 Century Club (46) 🔥 Unstoppable (13)

Conferences

ICML (9) NIPS (9) ICLR (7) AISTATS (5) JMLR (5) IJCAI (3) AAAI (2) ACL (2) AACL (1) ACML (1) EACL (1) IJCNLP (1) MICCAI (1)

Top co-authors

Peter Richtarik (10) Samuel Horváth (9) Alexander Gasnikov (8) Eduard Gorbunov (6) Aleksandr Beznosikov (5) Dmitry Kamzolov (5) Martin Jaggi (4) Zangir Iklassov (4) Marek Suppa (4) Chenxin Ma (4)

Research topics

Reinforcement Learning (1)

Keywords

stochastic optimization (7) distributed optimization (6) stochastic gradient descent (5) stochastic gradient (4) communication efficiency (4) empirical risk minimization (4) distributed learning (3) linear convergence (3) variational inequality (3) primal-dual optimization (3) low-resource language (3) federated learning (3) parallel computing (2) reinforcement learning (2) continuous optimization (2) machine learning (2) convergence analysis (2) active learning (2) sentiment analysis (2) uncertainty quantification (2)

Papers

Bant: Byzantine Antidote via Trial Function and Trust Scores AAAI 2026 Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages AACL 2025 Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles ACL 2025 When the Dictionary Strikes Back: A Case Study on Slovak Migration Location Term Extraction and NER via Rule-Based vs. LLM Methods ACL 2025 MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search MICCAI 2025 FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training ICML 2025 Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed ICML 2025 Enhancing BERT Fine-Tuning for Sentiment Analysis in Lower-Resourced Languages IJCNLP 2025 Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis AISTATS 2025 OPTAMI: Global Superlinear Convergence of High-order Methods ICLR 2025 Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity ICLR 2025 Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization ICLR 2025 From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation ICLR 2025 Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness ICLR 2024 Robustly Train Normalizing Flows via KL Divergence Regularization AAAI 2024 Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks IJCAI 2024 Efficient Conformal Prediction under Data Heterogeneity AISTATS 2024 Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad NIPS 2024 Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations NIPS 2024 Self-Guiding Exploration for Combinatorial Problems NIPS 2024 Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities NIPS 2023 Byzantine-Tolerant Methods for Distributed Variational Inequalities NIPS 2023 Reinforcement Learning for Solving Stochastic Vehicle Routing Problem ACML 2023 Algorithm for Constrained Markov Decision Process with Linear Convergence AISTATS 2023 WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition EACL 2023 SP2 : A Second Order Stochastic Polyak Method ICLR 2023 On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling IJCAI 2023 A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate NIPS 2022 Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information ICLR 2022 The power of first-order smooth optimization for black-box non-smooth problems ICML 2022 SONIA: A Symmetric Blockwise Truncated Optimization Algorithm AISTATS 2021 Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy AISTATS 2020 A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning JMLR 2020 Entropy-Penalized Semidefinite Programming IJCAI 2019 New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019 CoCoA: A General Framework for Communication-Efficient Distributed Optimization JMLR 2018 SGD and Hogwild! Convergence Without the Bounded Gradients Assumption ICML 2018 Reinforcement Learning for Solving the Vehicle Routing Problem NIPS 2018 SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient ICML 2017 A Multi-Batch L-BFGS Method for Machine Learning NIPS 2016 Primal-Dual Rates and Certificates ICML 2016 SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016 Distributed Coordinate Descent Method for Learning with Big Data JMLR 2016 Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption JMLR 2016 Adding vs. Averaging in Distributed Primal-Dual Optimization ICML 2015 Communication-Efficient Distributed Dual Coordinate Ascent NIPS 2014 Mini-Batch Primal and Dual Methods for SVMs ICML 2013