Alkis Kalavasis
Hi! I am an FDS Postdoctoral Fellow at Yale University. Before that, I was a PhD student in the Computer Science Department of the National Technical University of Athens (NTUA) working with Dimitris Fotakis and Christos Tzamos. I completed my undergraduate studies in the School of Electrical and Computer Engineering Department of the NTUA, where I was advised by Dimitris Fotakis.
Research: I work on statistical and computational learning theory. My research focuses on the design of algorithms with rigorous guarantees for machine learning problems. I am interested in the design of algorithms that are robust to data corruptions (adversarial manipulations, censoring and systematic errors), algorithms that are stable to changes in the training data (replicability and differential privacy) and provable guarantees for generative models (diffusion and language models).Contact me at: alkis.kalavasis [at] yale.edu
Teaching
Stability in Machine Learning: Generalization, Privacy & Replicability
[Lecture Notes]
Publications
Preprints-
DDPM Score Matching and Distribution Learning
with Sinho Chewi, Anay Mehrotra and Omar Montasser -
Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond
with Anay Mehrotra and Felix Zhou -
Characterization of Language Generation with Breadth
with Anay Mehrotra and Grigoris Velegkas -
Transfer Learning Beyond Bounded Density Ratios
with Ilias Zadik and Manolis Zampetakis
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What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
with Yang Cai, Katerina Mamali, Anay Mehrotra and Manolis Zampetakis
COLT 2025 -
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
with Kulin Shah, Adam Klivans and Giannis Daras
ICML 2025 -
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
with Anay Mehrotra and Grigoris Velegkas
STOC 2025 -
Computational Lower Bounds for No-Regret Learning in Normal-Form Games
with Ioannis Anagnostides and Tuomas Sandholm
STOC 2025
[PDF1] Barriers to Welfare Maximization with No-Regret Learning
[PDF2] Computational Lower Bounds for Regret Minimization in Normal-Form Games -
Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
with Amin Karbasi, Argyris Oikonomou, Katerina Sotiraki, Grigoris Velegkas, Manolis Zampetakis
NeurIPS 2024 -
On the Computational Landscape of Replicable Learning
with Amin Karbasi, Grigoris Velegkas and Felix Zhou
NeurIPS 2024 -
On Sampling from Ising Models with Spectral Constraints
with Andreas Galanis and Anthimos Vardis Kandiros
RANDOM 2024 -
Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
with Anay Mehrotra and Manolis Zampetakis
COLT 2024 -
Universal Rates for Real-Valued Regression: Separations between Cut-Off and Absolute Loss
with Idan Attias, Steve Hanneke, Amin Karbasi and Grigoris Velegkas
COLT 2024 -
Replicable Learning of Large-Margin Halfspaces
with Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas and Felix Zhou
ICML 2024 Selected as Spotlight -
On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games
with Ioannis Anagnostides, Tuomas Sandholm and Manolis Zampetakis
ITCS 2024 -
Learning Hard-Constrained Models with One Sample
with Andreas Galanis and Anthimos Vardis Kandiros
SODA 2024 -
Optimizing Solution-Samplers for Combinatorial Problems:
The Landscape of Policy-Gradient Methods
with Constantine Caramanis, Dimitris Fotakis, Vasilis Kontonis and Christos Tzamos
NeurIPS 2023 Selected as Oral
[Code] -
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
with Idan Attias, Steve Hanneke, Amin Karbasi and Grigoris Velegkas
NeurIPS 2023 Selected as Oral -
Statistical Indistinguishability of Learning Algorithms
with Amin Karbasi, Shay Moran and Grigoris Velegkas
ICML 2023 -
Replicable Bandits
with Hossein Esfandiari, Amin Karbasi, Andreas Krause, Vahab Mirrokni and Grigoris Velegkas
ICLR 2023 -
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
with Grigoris Velegkas and Amin Karbasi
NeurIPS 2022 -
Learning and Covering Sums of Independent Random Variables with Unbounded Support
with Konstantinos Stavropoulos and Manolis Zampetakis
NeurIPS 2022 Selected as Oral -
Perfect Sampling from Pairwise Comparisons
with Dimitris Fotakis and Christos Tzamos
NeurIPS 2022 -
Linear Label Ranking with Bounded Noise
with Dimitris Fotakis, Vasilis Kontonis and Christos Tzamos
NeurIPS 2022 Selected as Oral -
Label Ranking through Nonparametric Regression
with Dimitris Fotakis and Eleni Psaroudaki
ICML 2022 Selected for Long Presentation -
Differentially Private Regression with Unbounded Covariates
with Jason Milionis, Dimitris Fotakis and Stratis Ioannidis
AISTATS 2022 -
Efficient Algorithms for Learning from Coarse Labels
with Dimitris Fotakis, Vasilis Kontonis and Christos Tzamos
COLT 2021 -
Aggregating Incomplete and Noisy Rankings
with Dimitris Fotakis and Konstantinos Stavropoulos
AISTATS 2021 -
Efficient Parameter Estimation of Truncated Boolean Product Distributions
with Dimitris Fotakis and Christos Tzamos
COLT 2020
Algorithmica 2022