| 2026 | COLT | DDPM Score Matching and Distribution Learning (Extended Abstract). | Sinho Chewi, Alkis Kalavasis, Anay Mehrotra, Omar Montasser |
| 2026 | COLT | Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases (Extended Abstract). | Alkis Kalavasis, Anay Mehrotra, Felix Zhou |
| 2026 | STOC | On the Learning Curves of Revenue Maximization. | Steve Hanneke, Alkis Kalavasis, Shay Moran, Grigoris Velegkas |
| 2026 | STOC | Learning Mixture Models via Efficient High-Dimensional Sparse Fourier Transforms. | Alkis Kalavasis, Pravesh K. Kothari, Shuchen Li, Manolis Zampetakis |
| 2025 | COLT | What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness (Extended Abstract). | Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis |
| 2025 | ICML | Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. | Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras |
| 2025 | STOC | Computational Lower Bounds for No-Regret Learning in Normal-Form Games. | Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm |
| 2025 | STOC | On the Limits of Language Generation: Trade-Offs between Hallucination and Mode-Collapse. | Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas |
| 2024 | COLT | Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. | Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas |
| 2024 | COLT | Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening (Extended Abstract). | Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis |
| 2024 | ICML | Replicable Learning of Large-Margin Halfspaces. | Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou |
| 2024 | SODA | Learning Hard-Constrained Models with One Sample. | Andreas Galanis, Alkis Kalavasis, Anthimos Vardis Kandiros |
| 2023 | ICLR | Replicable Bandits. | Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas |
| 2023 | ICML | Statistical Indistinguishability of Learning Algorithms. | Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas |
| 2022 | AISTATS | Differentially Private Regression with Unbounded Covariates. | Jason Milionis, Alkis Kalavasis, Dimitris A. Fotakis, Stratis Ioannidis |
| 2022 | ICML | Label Ranking through Nonparametric Regression. | Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki |
| 2021 | AISTATS | Aggregating Incomplete and Noisy Rankings. | Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos |
| 2021 | COLT | Efficient Algorithms for Learning from Coarse Labels. | Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos |
| 2020 | COLT | Efficient Parameter Estimation of Truncated Boolean Product Distributions. | Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos |