Ryan D'Orazio
@RyanDOrazio
PhD Student at Mila Quebec AI Institute, and Université de Montréal.
I am at ICCOPT this week, at USC. Let's meet if you want to chat about optimization for machine learning and/or you are interested in working as a post-doc with me
Polyak’s step is magical, it converges in iterate to a min of a cvx fun w/o Lipschitz assumptions. But, it is finicky, small changes might not converge. Our paper: arxiv.org/abs/2505.20219 provides new insight on the magic (stubbornness) via local (approx) curvature of a surrogate
Happy to share that Compositional Risk Minimization has been accepted at #ICML2025 📌Extensive theoretical analysis along with a practical approach for extrapolating classifiers to novel compositions! 📜 arxiv.org/abs/2410.06303
This week I'll be at #ICLR25. If you like fundamental optimization results, I'll be presenting our work on surrogate losses for non-convex-concave min-max problems and learning value functions in deep RL (VIs more generally). Poster: #377 Thursday April 24 10am-12:30pm
I'll be at #NeurIPS24 until Sunday. If you're interested in solving variational inequality problems with deep learning (e.g. min-max and projected Bellman error), come and checkout our poster on surrogate losses at the opt ml workshop. arxiv.org/abs/2411.05228
I'll be at #NeurIPS24 until Sunday. If you're interested in solving variational inequality problems with deep learning (e.g. min-max and projected Bellman error), come and checkout our poster on surrogate losses at the opt ml workshop. arxiv.org/abs/2411.05228
I'll be hiring 1-2 students for graduate positions in my group at Mila and the University of Montreal. Deadline to express your interest is December 1st! mila.quebec/en/supervision…
Excited to share that I was selected as a 2023 @BorealisAI fellow and honored to be awarded alongside 9 other great researchers. This would not have been possible without support from my advisors, and many collaborators. I feel privileged to continue my research in optimization.
We are thrilled to introduce the 2023 @BorealisAI Fellowship recipients - exceptional researchers advancing #AI & #ML. Supported by Canada’s top institutions and leading researchers, they aim to make significant contributions to the field. Learn more: borealisai.com/news/the-borea…
Central to RL is dynamic programming – breaking down a sequential decision problem to a sequence of simpler single-step problems. Imperfect information in zero-sum games does not easily admit dynamic programming. Check out our work to see how we can fix it with regularization!
Imperfect information makes RL and search hard. In our ICML paper, we give an equilibrium-preserving reduction from imperfect information adversarial games to fully observable games, opening the door to new RL methods and simpler search techniques. 1/N arxiv.org/abs/2301.09159
Major update to my Online Learning monograph arxiv.org/abs/1912.13213 - Added Optimistic OMD - Added chapter on saddle-point optimization using OCO algorithms - Added chapter on universal portfolio - More proofs - Fixed bugs&typos - Overall, I added 40+ pages
If you're at ICLR and interested in fast learning in games then make sure to reach out to @ssokota. We provide some new results at the intersection of optimization, algorithmic game theory, and reinforcement learning. With contributions pushing the frontier in all three domains!
A longstanding disappointment in multi-agent research has been that naive self-play RL fails in adversarial games. In our ICLR paper, we present a simple principled approach that enables self-play RL to succeed in such settings. 1/N arxiv.org/abs/2206.05825
Going to Neurips. Feel free to reach-out I'd you want to talk about postdoc or Ph.D. opportunities in my group at @Mila_Quebec exploring the interface between game theory, deep learning and optimization.