Aggregating Incomplete and Noisy Rankings. - Dimitris Fotakis 001; Alkis Kalavasis; Konstantinos Stavropoulos (AISTATS, 2021)

Efficient Algorithms for Learning from Coarse Labels. - Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (COLT, 2021)

Differentially Private Regression with Unbounded Covariates. - Jason Milionis; Alkis Kalavasis; Dimitris A. Fotakis 001; Stratis Ioannidis (AISTATS, 2022)

Linear Label Ranking with Bounded Noise. - Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (NeurIPS, 2022)

Perfect Sampling from Pairwise Comparisons. - Dimitris Fotakis 001; Alkis Kalavasis; Christos Tzamos (NeurIPS, 2022)

Learning and Covering Sums of Independent Random Variables with Unbounded Support. - Alkis Kalavasis; Konstantinos Stavropoulos; Emmanouil Zampetakis (NeurIPS, 2022)

Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes. - Alkis Kalavasis; Grigoris Velegkas; Amin Karbasi (NeurIPS, 2022)

Efficient Parameter Estimation of Truncated Boolean Product Distributions. - Dimitris Fotakis 001; Alkis Kalavasis; Christos Tzamos (Algorithmica, 2022)

Reproducible Bandits. - Hossein Esfandiari; Alkis Kalavasis; Amin Karbasi; Andreas Krause 001; Vahab Mirrokni; Grigoris Velegkas (CoRR, 2022)

Replicable Bandits. - Hossein Esfandiari; Alkis Kalavasis; Amin Karbasi; Andreas Krause 001; Vahab Mirrokni; Grigoris Velegkas (ICLR, 2023)

Statistical Indistinguishability of Learning Algorithms. - Alkis Kalavasis; Amin Karbasi; Shay Moran; Grigoris Velegkas (ICML, 2023)

Optimal Learners for Realizable Regression: PAC Learning and Online Learning. - Idan Attias; Steve Hanneke; Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas (NeurIPS, 2023)

Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method. - Constantine Caramanis; Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (NeurIPS, 2023)

Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods. - Constantine Caramanis; Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (CoRR, 2023)

On Sampling from Ising Models with Spectral Constraints. - Andreas Galanis; Alkis Kalavasis; Anthimos Vardis Kandiros (APPROX/RANDOM, 2024)

Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. - Idan Attias; Steve Hanneke; Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas (COLT, 2024)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract). - Alkis Kalavasis; Anay Mehrotra; Manolis Zampetakis (COLT, 2024)

Replicable Learning of Large-Margin Halfspaces. - Alkis Kalavasis; Amin Karbasi; Kasper Green Larsen; Grigoris Velegkas; Felix Zhou 002 (ICML, 2024)

On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games. - Ioannis Anagnostides; Alkis Kalavasis; Tuomas Sandholm; Manolis Zampetakis (ITCS, 2024)

Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models. - Alkis Kalavasis; Amin Karbasi; Argyris Oikonomou; Katerina Sotiraki; Grigoris Velegkas; Manolis Zampetakis (NeurIPS, 2024)

On the Computational Landscape of Replicable Learning. - Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas; Felix Zhou 002 (NeurIPS, 2024)

Learning Hard-Constrained Models with One Sample. - Andreas Galanis; Alkis Kalavasis; Anthimos Vardis Kandiros (SODA, 2024)

Transfer Learning Beyond Bounded Density Ratios. - Alkis Kalavasis; Ilias Zadik; Manolis Zampetakis (CoRR, 2024)

Injecting Undetectable Backdoors in Deep Learning and Language Models. - Alkis Kalavasis; Amin Karbasi; Argyris Oikonomou; Katerina Sotiraki; Grigoris Velegkas; Manolis Zampetakis (CoRR, 2024)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening. - Alkis Kalavasis; Anay Mehrotra; Manolis Zampetakis (CoRR, 2024)

Barriers to Welfare Maximization with No-Regret Learning. - Ioannis Anagnostides; Alkis Kalavasis; Tuomas Sandholm (CoRR, 2024)

Computational Lower Bounds for Regret Minimization in Normal-Form Games. - Ioannis Anagnostides; Alkis Kalavasis; Tuomas Sandholm (CoRR, 2024)

Characterizations of Language Generation With Breadth. - Alkis Kalavasis; Anay Mehrotra; Grigoris Velegkas (CoRR, 2024)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness (Extended Abstract). - Yang Cai 001; Alkis Kalavasis; Katerina Mamali; Anay Mehrotra; Manolis Zampetakis (COLT, 2025)

Computational Lower Bounds for No-Regret Learning in Normal-Form Games. - Ioannis Anagnostides; Alkis Kalavasis; Tuomas Sandholm (STOC, 2025)

On the Limits of Language Generation: Trade-Offs between Hallucination and Mode-Collapse. - Alkis Kalavasis; Anay Mehrotra; Grigoris Velegkas (STOC, 2025)

Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. - Kulin Shah; Alkis Kalavasis; Adam R. Klivans; Giannis Daras (CoRR, 2025)

DDPM Score Matching and Distribution Learning. - Sinho Chewi; Alkis Kalavasis; Anay Mehrotra; Omar Montasser (CoRR, 2025)

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond. - Alkis Kalavasis; Anay Mehrotra; Felix Zhou 002 (CoRR, 2025)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness. - Yang Cai 001; Alkis Kalavasis; Katerina Mamali; Anay Mehrotra; Manolis Zampetakis (CoRR, 2025)

Label Ranking Through Nonparametric Regression. - Dimitris Fotakis 001; Alkis Kalavasis; Eleni Psaroudaki (Theory Comput. Syst., 2025)