← Authors

András György

54 papers · 2007–2025 · 8 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (6) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (18) 🗺️ Taxonomy Completionist (26) 🌟 Keyword Trendsetter Combo (4) 🤝 Dynamic Duo (33) 👑 Triple Crown 🔬 Deep Specialist (19) 🏆 Keyword Champion (2) 📈 Trend Setter 🔥 Unstoppable (14) 🚀 Conference Pioneer ⚡ Prolific Year (5) 💎 Century Club (54) 🗃️ Keyword Collector (63)

Conferences

ICML (15) NIPS (14) AISTATS (9) ICLR (5) JMLR (5) COLT (3) ALT (2) IJCAI (1)

Top co-authors

Csaba Szepesvári (33) Gellert Weisz (7) Pooria Joulani (7) Tor Lattimore (6) Dale Schuurmans (3) Claire Vernade (3) Ruitong Huang (3) Sven Gowal (3) Yifan Wu (2) Kiarash Shaloudegi (2)

Keywords

regret bound (16) online learning (14) stochastic optimization (8) multi-armed bandit (7) mirror descent (5) markov decision process (5) convex optimization (4) algorithm configuration (3) bayesian inference (3) online algorithm (3) delayed feedback (3) combinatorial optimization (2) importance weighting (2) regret analysis (2) bandit feedback (2) query complexity (2) adversarial learning (2) regret minimization (2) optimal control (2) hyperparameter optimization (2)

Papers

Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits AISTATS 2025
Learning Continually by Spectral Regularization ICLR 2025
Toward Understanding In-context vs. In-weight Learning ICLR 2025
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset NIPS 2024
To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty NIPS 2024
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL NIPS 2023
Optimistic Meta-Gradients NIPS 2023
Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore NIPS 2023
Understanding Self-Predictive Learning for Reinforcement Learning ICML 2023
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost ICML 2023
A Second-Order Method for Stochastic Bandit Convex Optimisation COLT 2023
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits JMLR 2023
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning AISTATS 2022
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs NIPS 2022
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions ALT 2022
On the Role of Neural Collapse in Transfer Learning ICLR 2022
Defending Against Image Corruptions Through Adversarial Augmentations ICLR 2022
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling JMLR 2022
Adapting to Delays and Data in Adversarial Multi-Armed Bandits ICML 2021
Improved Regret for Zeroth-Order Stochastic Convex Bandits COLT 2021
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting AISTATS 2021
Mirror Descent and the Information Ratio COLT 2021
A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES ICLR 2020
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool NIPS 2020
Non-Stationary Delayed Bandits with Intermediate Observations ICML 2020
A simpler approach to accelerated optimization: iterative averaging meets optimism ICML 2020
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems ICML 2019
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration ICML 2019
Detecting Overfitting via Adversarial Examples NIPS 2019
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging NIPS 2019
Adaptive MCMC via Combining Local Samplers AISTATS 2019
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration ICML 2018
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities JMLR 2017
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds ALT 2017
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NIPS 2016
Shifting Regret, Mirror Descent, and Matrices ICML 2016
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles AISTATS 2016
SDP Relaxation with Randomized Rounding for Energy Disaggregation NIPS 2016
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM AISTATS 2015
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments ICML 2015
Deterministic Independent Component Analysis ICML 2015
Online Learning with Gaussian Payoffs and Side Observations NIPS 2015
Fast Cross-Validation for Incremental Learning IJCAI 2015
Near-optimal max-affine estimators for convex regression AISTATS 2015
Online Learning in Markov Decision Processes with Changing Cost Sequences ICML 2014
Adaptive Monte Carlo via Bandit Allocation ICML 2014
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning ICML 2013
Online Learning with Costly Features and Labels NIPS 2013
Online Learning under Delayed Feedback ICML 2013
The adversarial stochastic shortest path problem with unknown transition probabilities AISTATS 2012
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping AISTATS 2010
On-Line Sequential Bin Packing JMLR 2010
Online Markov Decision Processes under Bandit Feedback NIPS 2010
The On-Line Shortest Path Problem Under Partial Monitoring JMLR 2007