location: Orchard View Room
SILO: Automating Statistical Inference for Modern Probabilistic Models
Abstract: How do you reconstruct an image of a black hole using only noisy telescope measurements in the Fourier domain? How do you predict the cure state of a carbon fibre aircraft wing as it cures in an autoclave using a few faulty thermocouples? In theory, Bayesian probabilistic models are …
SILO: Variational inference – reconciling statistical and convergence guarantees
Abstract: As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming increasingly popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable efficacy and superior efficiency. Several recent works provide theoretical justifications of VI by proving its statistical optimality for parameter …
SILO: On counterfactual inference with unobserved confounding via exponential family
Abstract: We are interested in the problem of unit-level counterfactual inference in the presence of unobserved confounders owing to the increasing importance of personalized decision-making in many domains: consider a recommender system interacting with a user over time where each user is provided recommendations based on observed demographics, prior engagement …
SILO: Polynomial Graph Neural Networks: Theoretical Limits and Graph Noise Impact
Abstract: This talk examines the theoretical foundations of Graph Neural Networks (GNNs), focusing on polynomial GNNs (Poly-GNNs). We start with empirical evidence challenging the need for complex GNN architectures in semi-supervised node classification, showing simpler methods often perform comparably. We then analyze Poly-GNNs within a contextual stochastic block model, addressing …
SILO: Towards Secure Large Language Models: From Model to System
Abstract: We are witnessing a paradigm shift in AI, transitioning from deep learning models to the era of Large Language Models (LLMs). This shift signifies a transformative advancement in AI, enabling it to be applied to diverse real-world safety-critical applications. Despite these impressive achievements, a fundamental question remains: are …