Martin Takac
47 papers · 2013–2026 · 13 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (57) 🌍 Conference Polyglot · Gold 🏃 Academic Marathon · Half 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Grand Slam
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Dynamic Duo
(10)
🏆
Keyword Champion
(2)
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Triple Crown
🔬
Deep Specialist
(27)
⚡
Prolific Year · Prolific
🔥
Unstoppable · Silver
❓
The Questioner
💎
Century Club · Bronze
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Conference Pioneer
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Trend Setter
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Keyword Collector
(198)
Venues
Top co-authors
Research topics
Machine Learning
(42)
Optimization & Theory
(38)
Mathematics & Optimization
(26)
Optimization
(26)
Artificial Intelligence
(12)
Deep Learning
(8)
Learning Types · Machine Learning
(8)
Core AI
(6)
Learning Paradigms · Artificial Intelligence
(5)
Natural Language Processing
(4)
Reinforcement Learning
(4)
Application Areas
(4)
Learning Paradigms · Machine Learning
(4)
Core Methods
(3)
Learning Types · Deep Learning
(3)
Keywords
stochastic optimization
(7)
distributed optimization
(7)
stochastic gradient descent
(6)
federated learning
(5)
convergence rate
(5)
stochastic gradient
(4)
empirical risk minimization
(4)
communication efficiency
(4)
reinforcement learning
(3)
distributed learning
(3)
gradient clipping
(3)
out-of-distribution detection
(3)
linear convergence
(3)
variational inequality
(3)
primal-dual optimization
(3)
parallel computing
(2)
machine learning
(2)
strongly convex
(2)
combinatorial optimization
(2)
convergence analysis
(2)
Papers
Bant: Byzantine Antidote via Trial Function and Trust Scores
AAAI 2026
Who to Trust? Aggregating Client Predictions in Federated Distillation.
UAI 2026
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
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
ICLR 2025
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
ICML 2025
Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles
ACL 2025
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
MICCAI 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
OPTAMI: Global Superlinear Convergence of High-order Methods
ICLR 2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
AISTATS 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
Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness
ICLR 2024
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
IJCAI 2024
Efficient Conformal Prediction under Data Heterogeneity
AISTATS 2024
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
NeurIPS 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
NeurIPS 2024
Robustly Train Normalizing Flows via KL Divergence Regularization
AAAI 2024
Self-Guiding Exploration for Combinatorial Problems
NeurIPS 2024
Algorithm for Constrained Markov Decision Process with Linear Convergence
AISTATS 2023
Byzantine-Tolerant Methods for Distributed Variational Inequalities
NeurIPS 2023
On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling
IJCAI 2023
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
ACML 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
NeurIPS 2023
SP2 : A Second Order Stochastic Polyak Method
ICLR 2023
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition
EACL 2023
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate
NeurIPS 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
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
JMLR 2020
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
AISTATS 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
Reinforcement Learning for Solving the Vehicle Routing Problem
NeurIPS 2018
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
ICML 2018
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
ICML 2017
A Multi-Batch L-BFGS Method for Machine Learning
NeurIPS 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
Primal-Dual Rates and Certificates
ICML 2016
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
ICML 2016
Adding vs. Averaging in Distributed Primal-Dual Optimization
ICML 2015
Communication-Efficient Distributed Dual Coordinate Ascent
NeurIPS 2014
Mini-Batch Primal and Dual Methods for SVMs
ICML 2013