8 citations · 16 across the 18 of their papers we have counts for
18 papers
Multiplicative Optimism for Constant Regret in Games
Ashkan Soleymani, Georgios Piliouras
We introduce Multiplicatively Optimistic Regret Matching (MORM), an uncoupled learning rule for finite general-sum games. Under simultaneous full-information self-play, every playe…
Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader
Ashkan Soleymani, Gabriele Farina, Patrick Jaillet
Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that first-order methods control richer ac…
Efficient Learning and Symmetry Discovery under Exact Invariances
Ashkan Soleymani, Behrooz Tahmasebi, Patrick Jaillet +1
Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understood. Even for classical superv…
Algorithmic Optimality Guarantees for Nonsmooth Output-Feedback Policy Search
Ashkan Soleymani, Patrick Jaillet
We study continuous-time full-order dynamic output-feedback policy search, a nonconvex and nonsmooth problem. Direct policy search is a central paradigm in reinforcement…
Data Generation without Function Estimation
Hadi Daneshmand, Ashkan Soleymani
Estimating the score function (or other population-density-dependent functions) is a fundamental component of most generative models. However, such function estimation is computati…
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest t…
Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We introduce Cautious Optimism, a framework for substantially faster regularized learning in general games. Cautious Optimism, as a variant of Optimism, adaptively controls the lea…
Faster Rates for No-Regret Learning in General Games via Cautious Optimism
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We establish the first uncoupled learning algorithm that attains per-player regret in multi-player general-sum games, where is the number of players, …
Learning with Exact Invariances in Polynomial Time
Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka +1
We study the statistical-computational trade-offs for learning with exact invariances (or symmetries) using kernel regression. Traditional methods, such as data augmentation, group…
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental v…
A Universal Class of Sharpness-Aware Minimization Algorithms
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri +2
Recently, there has been a surge in interest in developing optimization algorithms for overparameterized models as achieving generalization is believed to require algorithms with s…
Double Machine Learning Based Structure Identification from Temporal Data
Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani +2
Learning the causes of time-series data is a fundamental task in many applications, spanning from finance to earth sciences or bio-medical applications. Common approaches for this…
DRCFS: Doubly Robust Causal Feature Selection
Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet +2
Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science. Existing approaches are often…
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Arash Mehrjou, Ashkan Soleymani, Annika Buchholz +3
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. This offers great opportunities in the healthcare sector, where l…
Physical Derivatives: Computing policy gradients by physical forward-propagation
Arash Mehrjou, Ashkan Soleymani, Stefan Bauer +1
Model-free and model-based reinforcement learning are two ends of a spectrum. Learning a good policy without a dynamic model can be prohibitively expensive. Learning the dynamic mo…
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that s…
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh +3
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating…
Causal Feature Selection via Orthogonal Search
Ashkan Soleymani, Anant Raj, Stefan Bauer +2
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. However, e…