I am an IFML Postdoctoral Fellow at UT Austin working with Adam Klivans and Raghu Meka. I received my PhD from Computer Science Department of University of Wisconsin-Madison, where I was very fortunate to be advised by Professor Christos Tzamos. Before coming to UW-Madison, I studied Electrical and Computer Engineering at the National Technical University of Athens where I was very lucky to be advised by Professor Dimitris Fotakis.
Here is a more complete CV.
Optimizing Solution-Samplers for Combinatorial Problems:
The Landscape of Policy Gradient Methods
C. Caramanis, D. Fotakis, A. Kalavasis, C. Tzamos
Selected for Oral Presentation
NeurIPS 2023
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
w/ F. Iliopoulos, K. Trinh, C. Baykal, G. Menghani, V. Erik
NeurIPS 2023
The Gain from Ordering in Online Learning
w/ M. Ma, C. Tzamos
NeurIPS 2023
Efficient Testable Learning of Halfspaces with Adversarial Label Noise
w/ I. Diakonikolas, D M. Kane, [S. Liu], N. Zarifis
NeurIPS 2023
Self Directed Linear Classification
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
COLT 2023
Weighted Distillation with Unlabeled Examples
w/ F. Iliopoulos, C. Baykal, G. Menghani, K. Trinh, V. Erik
NeurIPS 2022
Linear Label Ranking with Bounded Noise
w/ D. Fotakis, A. Kalavasis, C. Tzamos
Selected for Oral Presentation
NeurIPS 2022
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
ICML 2022
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
COLT 2022
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution
w/ I. Diakonikolas, D. Kane, C. Tzamos, N. Zarifis
STOC 2022
A Statistical Taylor Theorem and Extrapolation of Truncated Densities
w/ C. Daskalakis, C. Tzamos, M. Zampetakis
COLT 2021
Agnostic Proper Learning of Halfspaces under Gaussian Marginals
w/ I. Diakonikolas, D. Kane, C. Tzamos, N. Zarifis
COLT 2021
Efficient Algorithms for Learning from Coarse Labels
w/ D. Fotakis, A. Kalavasis, C. Tzamos
COLT 2021
Learning Online Algorithms with Distributional Advice
w/ I. Diakonikolas, C. Tzamos, A. Vakilian, N. Zarifis
ICML 2021
A Polynomial Time Algorithm For Learning Halfspaces with Tsybakov Noise
w/ I. Diakonikolas, D. Kane, C. Tzamos, N. Zarifis
STOC 2021
Learning Halfspaces with Tsybakov Noise
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
STOC 2021
Conference version merged with the above paper
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
NeurIPS 2020
Learning Halfspaces with Massart Noise Under Structured Distributions
w/ I. Diakonikolas, C. Tzamos, N. Zarifis
COLT 2020
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
w/ I. Diakonikolas, D. Kane, N. Zarifis
COLT 2020
Efficient Truncated Statistics with Unknown Truncation
w/ C. Tzamos, M. Zampetakis
FOCS 2019
Removing Bias in Maching Learning via Truncated Statistics
w/ C. Daskalakis, C. Tzamos, M. Zampetakis
Manuscript
Opinion Dynamics with Limited Information
w/ D. Fotakis, V. Kandiros, S. Skoulakis
WINE 2018
Learning Powers of Poisson Binomial Distributions
w/ D. Fotakis, P. Krysta, P. Spirakis
Manuscript
Reviewer: STOC, SODA, WINE, ICML, EC, MFCS, TCS, ALT
Learning General Halfspaces with General Massart Noise, STOC 2022
A Statistical Taylor’s Theorem and Extrapolation of Truncated Densities, COLT 2021
Agnostic Proper Learning of Halfspaces under Gaussian Marginals COLT 2021
Efficient Algorithms for Learning Halfspaces with Tsybakov Noise, STOC 2021
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise, NeurIPS 2020
Learning Halfspaces with Massart Noise Under Structured Distributions, COLT 2020
Efficient Truncated Statistics with Unknown Truncation, FOCS 2019, Video
Learning PBD Powers, ECCO Research Seminar 2017, University of Liverpool