In recent years, many novel methods and directions have emerged in reinforcement learning and control. A particularly exciting development is the use of online optimization and statistical learning techniques in control theory. This has led to novel methods and guarantees in various contexts, including in stochastic and adversarial environments, system identification, iterative planning and sequence prediction. Other topics we will cover include new connections between control and both model-free and model-based reinforcement learning, as well as learning dynamical systems. We aim to bring together researchers to facilitate progress along these lines of investigation, and discuss important future directions in reinforcement learning, control, learning dynamical systems and applications to sequence prediction.
Poster Session and Lightning Talks
This workshop will include a poster sessionand lightning talks for early career researchers (including graduate students). In order to propose a poster or a lightning talk, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. You can request to do one, or both. The registration form should not be used to propose a posteror a lightning talk.
The deadline for proposing is Sunday, March 15, 2026. If your proposal is accepted, you should plan to attend the event in-person.
In-Person Registration
Seats are limited at the venue, which means that in-person registration may be capped prior to the workshop start date. If capacity is reached, a waitlist will be imposed, which the registration form will reflect. Early registration is strongly encouraged.
All in-person registrants must wait to receive an invitation to attend in-person from IMSI before traveling, which generally begin to be sent out 4-6 weeks in advance.
All registrants (online and in-person) will receive zoom links and are welcome to attend online.
We study the problem of preconditioning in sequential prediction. From the theoretical lens of linear dynamical systems, we show that convolving the target sequence corresponds to applying a polynomial to the hidden transition matrix. Building on this insight, we propose a universal preconditioning method that convolves the target with coefficients from orthogonal polynomials such as Chebyshev or Legendre. We prove that this approach reduces regret for two distinct prediction algorithms and yields the first ever sublinear and hidden-dimension-independent regret bounds (up to logarithmic factors) that hold for systems with marginally table and asymmetric transition matrices. Finally, extensive synthetic and real-world experiments show that this simple preconditioning strategy improves the performance of a diverse range of algorithms, including recurrent neural networks, and generalizes to signals beyond linear dynamical systems.
10:50-11:05 CDT
Q&A
11:05-11:35 CDT
Coffee Break
11:35-12:20 CDT
TBA
Speaker: Nikolai Matni (University of Pennsylvania)
12:20-12:35 CDT
Q&A
12:35-13:35 CDT
Lunch break
13:35-14:20 CDT
TBA
Speaker: Vasileios Tzoumas (University of Michigan)
14:20-14:35 CDT
Q&A
14:35-15:00 CDT
Coffee Break
15:00-15:45 CDT
Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success
Speaker: Daniel Russo (Columbia University)
15:45-16:00 CDT
Q&A
Wednesday, May 13, 2026
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:45 CDT
TBA
Speaker: Sarah Dean (Cornell University)
9:45-10:00 CDT
Q&A
10:00-10:05 CDT
Tech Break
10:05-10:50 CDT
Controlled dynamical systems on the space of probability measures
Speaker: Max Raginsky (University of Illinois at Urbana-Champaign)
10:50-11:05 CDT
Q&A
11:05-11:35 CDT
Coffee Break
11:35-12:20 CDT
Large Language Models and Computation
Speaker: Dale Schuurmans (University of Alberta)
The ability of large generative models to respond naturally to text, image and audio inputs has created significant excitement. Particularly interesting is the ability of these models to generate outputs that resemble coherent reasoning and computational sequences. I will discuss the inherent computational capability of large language models and show that autoregressive decoding supports universal computation, even without pre-training. The co-existence of informal and formal computational systems in the same model does not change what is computable, but does provide new means for eliciting desired behaviour. I will then discuss how post-training, in an attempt to make a model more directable, faces severe computational limits on what can be achieved, but that accounting for these limits can improve outcomes.
12:20-12:35 CDT
Q&A
12:35-13:35 CDT
Lunch break
13:35-14:20 CDT
Latent Representations for Control Design with Provable Stability and Safety Guarantees
Speaker: Stephen Tu (University of Southern California (USC))
We initiate a formal study on the use of low-dimensional latent representations of dynamical systems for verifiable control synthesis. Our main goal is to enable the application of verification techniques -- such as Lyapunov or barrier functions -- that might otherwise be computationally prohibitive when applied directly to the full state representation. Towards this goal, we first provide dynamics-aware approximate conjugacy conditions which formalize the notion of reconstruction error necessary for systems analysis. We then utilize our conjugacy conditions to transfer the stability and invariance guarantees of a latent certificate function (e.g., a Lyapunov or barrier function) for a latent space controller back to the original system. Importantly, our analysis contains several important implications for learning latent spaces and dynamics, by highlighting the necessary geometric properties which need to be preserved by the latent space, in addition to providing concrete loss functions for dynamics reconstruction that are directly related to control design.
14:20-14:35 CDT
Q&A
14:35-15:00 CDT
Coffee Break
15:00-15:45 CDT
Some fundamental limitations of learning for dynamics and control
Speaker: Necmiye Ozay (University of Michigan)
Data-driven and learning-based methods have attracted considerable attention in recent years both for the analysis of dynamical systems and for control design. While there are many interesting and exciting results in this direction, our understanding of fundamental limitations of learning for control is lagging. This talk will focus on the question of when learning can be hard or impossible in the context of dynamical systems and control. In the first part of the talk, I will discuss a new observation on immersions and how it reveals some potential limitations in learning Koopman embeddings. In the second part of the talk, I will show what makes it hard to learn to stabilize linear systems from a sample-complexity perspective. While these results might seem negative, I will conclude the talk with some thoughts on how they can inspire interesting future directions.
15:45-16:00 CDT
Q&A
Thursday, May 14, 2026
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:45 CDT
A mathematical basis for Moravec’s paradox
Speaker: Max Simchowitz (Carnegie Mellon University)
9:45-10:00 CDT
Q&A
10:00-10:05 CDT
Tech Break
10:05-10:50 CDT
TBA
Speaker: TBA (TBA)
10:50-11:05 CDT
Q&A
11:05-11:35 CDT
Coffee Break
11:35-12:20 CDT
TBA
Speaker: Tri Dao (Princeton University)
12:20-12:35 CDT
Q&A
12:35-13:35 CDT
Lunch break
13:35-14:20 CDT
TBA
Speaker: Na Li (Harvard)
14:20-14:35 CDT
Q&A
14:35-15:00 CDT
Coffee Break
15:00-15:45 CDT
TBA
Speaker: Jacob Abernethy (Georgia Tech)
15:45-16:00 CDT
Q&A
Friday, May 15, 2026
8:30-9:00 CDT
Breakfast/Check-in
9:00-9:45 CDT
TBA
Speaker: Nadav Cohen (Tel Aviv University)
We study the problem of preconditioning in sequential prediction. From the theoretical lens of linear dynamical systems, we show that convolving the target sequence corresponds to applying a polynomial to the hidden transition matrix. Building on this insight, we propose a universal preconditioning method that convolves the target with coefficients from orthogonal polynomials such as Chebyshev or Legendre. We prove that this approach reduces regret for two distinct prediction algorithms and yields the first ever sublinear and hidden-dimension-independent regret bounds (up to logarithmic factors) that hold for systems with marginally table and asymmetric transition matrices. Finally, extensive synthetic and real-world experiments show that this simple preconditioning strategy improves the performance of a diverse range of algorithms, including recurrent neural networks, and generalizes to signals beyond linear dynamical systems.
9:45-10:00 CDT
Q&A
10:00-10:30 CDT
Coffee Break
10:30-11:15 CDT
TBA
Speaker: Ben Van Roy (Stanford University)
11:15-11:30 CDT
Q&A
11:30-11:45 CDT
Workshop Survey and Closing Remarks
Registration
IMSI is committed to making all of our programs and events inclusive and accessible.
Contact [email protected] to request
disability-related accommodations.
In order to register for this workshop, you must have an IMSI account and be logged in.