Portrait de Aidan Li

Aidan Li

aidan.li@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Sarath Chandar
Co-supervisor
Ross Goroshin
Sujets de recherche
Apprentissage continu
Apprentissage par renforcement
Grands modèles de langage (LLM)
Mémoire
Site web
Google Scholar
LinkedIn
GitHub

Publications

Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
Aidan Li
Uday Kiran Reddy Tadipatri
Mahan Fathi
Sarath Chandar
Ross Goroshin
Koopman autoencoders (KAEs) seek a higher-dimensional latent representation in which nonlinear dynamics evolve linearly. However, many inter… (voir plus)esting systems have multiple basins of attraction, and both theoretical and empirical work has shown these multibasin systems cannot generally admit a single finite-dimensional global Koopman embedding under standard assumptions. We posit that encoders with a sparsity-inducing objective encouraging few active latent coefficients will provide latent supports as an inspectable basin-modeling principle for Koopman autoencoders. We use these encoders producing sparse latents in training Sparse Koopman Autoencoders (SKAEs) without basin labels or other regime annotations, and treat the learned latent supports as model-produced regime variables after training. Across a range of procedurally generated multibasin systems and chaotic flows, we show that SKAEs have superior forecasting performance compared to dense-latent KAEs. We also perform a mechanistic study that shows latent supports produced by SKAEs are both essential for the quality of the representation and useful for identifying basins on held-out basin interior states, whereas dense-latent KAEs collapse to an uninformative single family. These results identify sparse latents and their corresponding supports as label-free, interpretable regime variables for Koopman learning in nonlinear systems with multiple local dynamical laws.
2026-08-28
arXiv (prépublication)
doi.org
arxiv.org
Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
Stephen Zhao
Aidan Li
Rob Brekelmans
Roger Grosse
Reinforcement learning (RL) has become a predominant technique to align language models (LMs) with human preferences or promote outputs whic… (voir plus)h are deemed to be desirable by a given reward function. Standard RL approaches optimize average reward, while methods explicitly focused on reducing the probability of undesired outputs typically come at a cost to average-case performance. To improve this tradeoff, we introduce RePULSe, a new training method that augments the standard RL loss with an additional loss that uses learned proposals to guide sampling low-reward outputs, and then reduces those outputs’ probability. We run experiments demonstrating that RePULSe produces a better tradeoff of expected reward versus the probability of undesired outputs and is more adversarially robust, compared to standard RL alignment approaches and alternatives.
2025-12-02
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net