Min-max optimization (used among other applications in GANs, and adversarial training more broadly) is empirically challenging. We show why min-max optimization is hard in the following paper with Stratis Skoulakis and Manolis Zampetakis: https://t.co/ATgkHqeo2r

If you work at the intersection of CS and economics (or think your work is of interest to those who do!) consider submitting to the ESIF Economics and AI+ML meeting this summer at Cornell:

Today marks an important milestone. I’m launching Percepta together with @htaneja, @hirshjain, @tmathew0309, Radha Jain, @marisbest2, @KonstDaskalakis and an incredible team, with the goal of bringing AI to the core industries that run our economy. For AI to deliver

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Hiring researchers and engineers for a stealth, applied research company with a focus on RL x foundation models. Folks on the team already are leading RL / learning researchers. If you think you'd be good at the research needed to get things working in practice, email me

🚀 🇬🇷 A year in the making! I’ve just completed a set of 21 lectures in Machine Learning, in Greek, designed for high school students. The course introduces key ML concepts, coding in Python & PyTorch, and real-world AI applications. #MachineLearning #AI #EdTech #Greece

Proud of my brother's research!

Looking forward to this event on Thursday!

💡Daskalakis & Ressler labs @McLeanHospital @harvardmed are extremely excited & proud 🥳to share that our collaborative work with Nemeroff @UTAustin and Kleinman @LieberInstitute labs on molecular underpinnings of #PTSD & #MDD is out @ScienceMagazine https://t.co/pcQXbtsXkv! 🧵👇

Can you train a generative model using only noisy data? If you can, this would alleviate the issue of training data memorization plaguing certain genAI models. In exciting work with @giannis_daras and @AlexGDimakis we show how to do this for diffusion-based generative models.

Stoked to announce an Agentic Markets workshop @agenticmarkets at #ICML 2024! @icmlconf 📇 Details: https://t.co/wOdgjzmXOb ✏️ Call for papers: due May 17th 📅 Conference: July 26/27th featuring GOAT speakers like Tuomas Sandholm, Gillian Hadfield, @drimgemp @KonstDaskalakis

The call for papers is coming soon… Delighted to confirm the participation of the Greek Prime Minister @kmitsotakis and the chair of Greece’s high level AI committee @KonstDaskalakis at this event. June 20th in Athens 🇬🇷

Note that concurrent work by Peng & Rubinstein establishes a similar set of results:

Check out our paper at: https://t.co/kQDihRbmh4.

At a technical level, our upper bound employs a tree structure on a collection of external regret learners. Each external regret learner updates its action distribution only sporadically, allowing us to avoid polynomial dependence on the number of experts.

The number of rounds in our upper bound depends exponentially on the inverse of the approximation parameter. We present a lower bound showing that this dependence is necessary, even when the adversary is oblivious.

They also provide algorithms for computing low-rank and sparse correlated equilibria.

As a consequence of our main results and the standard connection between no-swap regret learning and correlated equilibria, we obtain a polynomial-time algorithm for finding an approximate correlated equilibrium in extensive-form games, for constant approximation parameter.

More generally, we obtain that any class of finite Littlestone dimension (or seq fat shattering dimension) has a no swap regret learner. We also derive an extension to the bandit setting, for which we show a nearly-optimal swap regret bound for large action spaces.

We show that no-swap regret learning is possible for any function class which has a no-external regret learner. As an example, in the setting of learning with expert advice with N experts, only polylog(N) rounds are needed to obtain swap regret bounded by an arbitrary constant.

Specifically, we ask: can one improve upon classical bounds by Blum-Mansour and Stolz-Lugosi which suffer linear dependence on the #actions to achieve sublinear swap regret/correlated equilibrium computation with poly-logarithmic dependence on the #actions instead?