Dan Guralnik

Yixuan Wang, Dan Guralnik, Warren Dixon

Safety-critical autonomy in adversarial settings demands more than Lyapunov stability of tracking error signals. An agent executing a goal-directed trajectory is intrinsically legible to a passive observer running online Bayesian inference, because the contractive dynamics of any Lyapunov basin of attraction concentrates posterior belief over the latent intent parameters. We initiates the study of intent privacy over a continuous state space as a joint control problem on the physical state combined with the latent belief state of a putative observer. With the main challenges concentrated around the analysis of the belief-state dynamics, the agent dynamics is assumed to be simple, modeled by the differential inclusion $\dot{x}\in u+\bar{d}\mathbb{B}$. That is, the agent is fully actuated with bounded unknown disturbance to the control input. The observer's intent inference process is modeled as a discrete-time stochastic dynamical system evolving over the belief state space of a Rao Blackwellized particle filter reasoning over large random samples of possible agent goals. The agent's control input is modeled as a piecewise constant signal, with jumps matching the RBPF update times. Building on a prior intent-inference framework and its KL-based information leakage measurement, a privacy constraint is imposed, which amounts to maintaining information leakage above a prescribed threshold with high probability, using probabilistic discrete-time control barrier functions. A key technical contribution is the derivation of separate PCBF results for the Bayesian update step and the resampling step of the RBPF, enabling a PCBF result for the full update as well as integration of the privacy constraint with the agent's task-side tracking requirement. Finally, a joint feasibility analysis is carried out by examining the interplay between the privacy constraint and the tracking envelope.

Yixuan Wang, Dan Guralnik, Saiedeh Akbari et al.

We introduce Effective Model Pruning (EMP), a context-agnostic, parameter-free rule addressing a fundamental question about pruning: how many entries to keep. EMP does not prescribe how to score the parameters or prune the models; instead, it supplies a universal adaptive threshold that can be applied to any pruning criterion: weight magnitude, attention score, KAN importance score, or even feature-level signals such as image pixel, and used on structural parts or weights of the models. Given any score vector s, EMP maps s to a built-in effective number N_eff which is inspired by the Inverse Simpson index of contributors. Retaining the N_eff highest scoring entries and zeroing the remainder yields sparse models with performance comparable to the original dense networks across MLPs, CNNs, Transformers/LLMs, and KAN, in our experiments. By leveraging the geometry of the simplex, we derive a tight lower bound on the preserved mass s_eff (the sum of retained scores) over the corresponding ordered probability simplex associated with the score vector s. We further verify the effectiveness of N_eff by pruning the model with a scaled threshold \b{eta}*N_eff across a variety of criteria and models. Experiments suggest that the default \b{eta} = 1 yields a robust threshold for model pruning while \b{eta} not equal to 1 still serves as an optional adjustment to meet specific sparsity requirements.