Hello! I am a fifth yeah PhD student in the Computer Science department at Yale University. My advisor is Amin Karbasi. My research focuses on learning theory and mechanism design. In summer 2023, I was a research intern at Google Research in Mountain View, hosted by Andres Perlroth and Gagan Aggarwal. Currently, I am a student researcher at Google Research hosted by Yuan Deng.

Before coming to Yale, I completed my undergrad studies at NTUA majoring in computer science and electrical engineering, where I worked with Dimitris Fotakis.

email: grigoris.velegkas AT yale DOT edu

Conference Publications

Authors are listed in alphabetical order, unless denoted by (*).

Replicable Learning of Large-Margin Halfspaces
Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou
[Preprint] [Arxiv]
User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization
Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas
[WWW 2024]
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas
[NeurIPS 2023 (Oral Presentation)] [Arxiv]
Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
[NeurIPS 2023] [Arxiv]
Replicability in Reinforcement Learning
Amin Karbasi, Grigoris Velegkas, Lin F. Yang, Felix Zhou
[NeurIPS 2023] [Arxiv]
Statistical Indistinguishability of Learning Algorithms
Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas
[ICML 2023] [Arxiv]
Replicable Bandits
Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas
[ICLR 2023] [Arxiv]
Universal Rates for Interactive Learning
Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas
[NeurIPS 2022 (Oral Presentation)] [NeurIPS Proceedings]
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Alkis Kalavasis(*), Grigoris Velegkas(*), Amin Karbasi
[NeurIPS 2022] [Arxiv]
Reinforcement Learning with Logarithmic Regret and Policy Switches
Grigoris Velegkas(*), Zhuoran Yang, Amin Karbasi
[NeurIPS 2022] [NeurIPS Proceedings]
Is Selling Complete Information (Approximately) Optimal?
Dirk Bergemann, Yang Cai, Grigoris Velegkas, Mingfei Zhao
[EC 2022] [Arxiv]
An Efficient ε-BIC to BIC Transformation and Its Application to Black-Box Reduction in Revenue Maximization
Yang Cai, Argyris Oikonomou, Grigoris Velegkas, Mingfei Zhao
[SODA 2021] [Arxiv]
How to Sell Information Optimally: an Algorithmic Study
Yang Cai, Grigoris Velegkas
[ITCS 2021] [Arxiv]

Awards

Onassis Foundation Scholarship for Academic Excellence, 2020-2024
Bodossaki Foundation Scholarship for Academic Excellence, 2020-2024
Gerondelis Foundation Scholarship for Academic Excellence, 2019-2020
Eurobank "A great moment for education" Award, 2013