A Sober Look at Clustering Stability. - Shai Ben-David; Ulrike von Luxburg; Dávid Pál (COLT, 2006)

Stability of k -Means Clustering. - Shai Ben-David; Dávid Pál; Hans Ulrich Simon (COLT, 2007)

Edge-Colourings of Cubic Graphs and Universal Steiner Triple Systems. - Dávid Pál; Martin Skoviera (Electron. Notes Discret. Math., 2007)

Colouring Cubic Graphs by Small Steiner Triple Systems. - Dávid Pál; Martin Skoviera (Graphs Comb., 2007)

Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. - Shai Ben-David; Tyler Lu; Dávid Pál (COLT, 2008)

Agnostic Online Learning. - Shai Ben-David; Dávid Pál; Shai Shalev-Shwartz (COLT, 2009)

General auction mechanism for search advertising. - Gagan Aggarwal; S. Muthukrishnan 001; Dávid Pál; Martin Pál (WWW, 2009)

Learning Low Density Separators. - Shai Ben-David; Tyler Lu; Dávid Pál; Miroslava Sotáková (AISTATS, 2009)

Contributions to Unsupervised and Semi-Supervised Learning. - Dávid Pál (2009)

Toward a Classification of Finite Partial-Monitoring Games. - Gábor Bartók; Dávid Pál; Csaba Szepesvári (ALT, 2010)

Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. - Dávid Pál; Barnabás Póczos; Csaba Szepesvári (NIPS, 2010)

Impossibility Theorems for Domain Adaptation. - Shai Ben-David; Tyler Lu; Teresa Luu; Dávid Pál (AISTATS, 2010)

Contextual Multi-Armed Bandits. - Tyler Lu; Dávid Pál; Martin Pal (AISTATS, 2010)

Improved Algorithms for Linear Stochastic Bandits. - Yasin Abbasi-Yadkori; Dávid Pál; Csaba Szepesvári (NIPS, 2011)

Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems - Yasin Abbasi-Yadkori; Dávid Pál; Csaba Szepesvári (CoRR, 2011)

Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments. - Gábor Bartók; Dávid Pál; Csaba Szepesvári (COLT, 2011)

Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits. - Yasin Abbasi-Yadkori; Dávid Pál; Csaba Szepesvári (AISTATS, 2012)

Online problémy v strojovom učení. - Dávid Pál (ITAT, 2013)

Toward a classification of finite partial-monitoring games. - András Antos; Gábor Bartók; Dávid Pál; Csaba Szepesvári (Theor. Comput. Sci., 2013)

Partial Monitoring - Classification, Regret Bounds, and Algorithms. - Gábor Bartók; Dean P. Foster; Dávid Pál; Alexander Rakhlin; Csaba Szepesvári (Math. Oper. Res., 2014)

Scale-Free Algorithms for Online Linear Optimization. - Francesco Orabona; Dávid Pál (ALT, 2015)

Optimal Non-Asymptotic Lower Bound on the Minimax Regret of Learning with Expert Advice. - Francesco Orabona; Dávid Pál (CoRR, 2015)

Open Problem: Parameter-Free and Scale-Free Online Algorithms. - Francesco Orabona; Dávid Pál (COLT, 2016)

Parameter-Free Convex Learning through Coin Betting. - Francesco Orabona; Dávid Pál (AutoML@ICML, 2016)

Hardness of Online Sleeping Combinatorial Optimization Problems. - Satyen Kale; Chansoo Lee; Dávid Pál (NIPS, 2016)

Coin Betting and Parameter-Free Online Learning. - Francesco Orabona; Dávid Pál (NIPS, 2016)

From Coin Betting to Parameter-Free Online Learning. - Francesco Orabona; Dávid Pál (CoRR, 2016)

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. - Satyen Kale; Zohar S. Karnin; Tengyuan Liang; Dávid Pál (ICML, 2017)

Scale-free online learning. - Francesco Orabona; Dávid Pál (Theor. Comput. Sci., 2018)

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. - Alina Beygelzimer; Dávid Pál; Balázs Szörényi; Devanathan Thiruvenkatachari; Chen-Yu Wei; Chicheng Zhang (ICML, 2019)

The information-theoretic value of unlabeled data in semi-supervised learning. - Alexander Golovnev; Dávid Pál; Balázs Szörényi (ICML, 2019)

Learning to Crawl. - Utkarsh Upadhyay; Róbert Busa-Fekete; Wojciech Kotlowski; Dávid Pál; Balázs Szörényi (AAAI, 2020)

Parameter-free Stochastic Optimization of Variationally Coherent Functions. - Francesco Orabona; Dávid Pál (CoRR, 2021)

Auction for Double-Wide Ads. - Jonathan Gu; Dávid Pál; Kevin Ryan (CoRR, 2022)