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:
My research focuses on Machine Learning Theory and its interplay with Statistics & High-Dimensional Probability,
Optimization and Computational Complexity.
Currently, I am interested in questions regarding the following topics:
- Robustness in Learning Systems: Transfer Learning, Inference from Biased, Dependent and Strategic Data
- Stability in Learning Systems: Replicability, Differential Privacy
- Statistical Learning Theory: PAC Learning, Universal Rates, Gradient Descent Dynamics
- Safety for Learning Systems: Backdoor Attacks
Contact me at: alkis.kalavasis [at] yale.edu
My amazing collaborators (in roughly chronological order):
Dimitris Fotakis,
Christos Tzamos,
Konstantinos Stavropoulos,
Vasilis Kontonis,
Manolis Zampetakis,
Jason Milionis,
Stratis Ioannidis,
Eleni Psaroudaki,
Grigoris Velegkas,
Amin Karbasi,
Hossein Esfandiari,
Andreas Krause,
Vahab Mirrokni,
Constantine Caramanis,
Shay Moran,
Idan Attias,
Steve Hanneke,
Andreas Galanis,
Anthimos Vardis Kandiros,
Ioannis Anagnostides,
Tuomas Sandholm,
Felix Zhou,
Kasper Green Larsen,
Ilias Zadik,
Anay Mehrotra,
Argyris Oikonomou,
Katerina Sotiraki.
Publications
Preprints
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Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
with Amin Karbasi, Argyris Oikonomou, Katerina Sotiraki, Grigoris Velegkas, Manolis Zampetakis
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On the Computational Landscape of Replicable Learning
with Amin Karbasi, Grigoris Velegkas and Felix Zhou
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Transfer Learning Beyond Bounded Density Ratios
with Ilias Zadik and Manolis Zampetakis
Journal Publications
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Efficient Parameter Estimation of Truncated Boolean Product Distributions
with Dimitris Fotakis and Christos Tzamos
Algorithmica, 2022
Conference Publications
2024
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On Sampling from Ising Models with Spectral Constraints
with Andreas Galanis and Anthimos Vardis Kandiros
RANDOM 2024
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Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
with Anay Mehrotra and Manolis Zampetakis
COLT 2024
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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
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Replicable Learning of Large-Margin Halfspaces
with Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas and Felix Zhou
ICML 2024
Selected as Spotlight
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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
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Learning Hard-Constrained Models with One Sample
with Andreas Galanis and Anthimos Vardis Kandiros
SODA 2024
2023
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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
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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
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Replicable Bandits
with Hossein Esfandiari, Amin Karbasi, Andreas Krause, Vahab Mirrokni and Grigoris Velegkas
ICLR 2023
2022
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Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
with Grigoris Velegkas and Amin Karbasi
NeurIPS 2022
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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
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Linear Label Ranking with Bounded Noise
with Dimitris Fotakis, Vasilis Kontonis and Christos Tzamos
NeurIPS 2022
Selected as Oral
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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
2021
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Efficient Algorithms for Learning from Coarse Labels
with Dimitris Fotakis, Vasilis Kontonis and Christos Tzamos
COLT 2021
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Aggregating Incomplete and Noisy Rankings
with Dimitris Fotakis and Konstantinos Stavropoulos
AISTATS 2021
2020
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Efficient Parameter Estimation of Truncated Boolean Product Distributions
with Dimitris Fotakis and Christos Tzamos
COLT 2020