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 …
Abstract: Nuclear fusion holds the promise of limitless clean energy and would solve many of the world’s grand challenges. The most promising approach to date uses tokamaks, but we have not yet been able to sustain plasmas at the temperatures, pressures, and durations needed to make fusion power viable. This …
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 …
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 …
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 …
Abstract: AI Models have been increasingly deployed to develop “Autonomous Agents” for decision-making, with prominent application examples including playing Go and video games, robotics, autonomous driving, healthcare, human-assistant, etc. Most such success stories naturally involve multiple AI-agents interacting dynamically with each other and humans. More importantly, these agents oftentimes operate with asymmetric …
Abstract: Applying AI to scientific problems like weather forecasting and aerodynamics is an active research area, promising to accelerate model development and enable faster scientific discovery and engineering design. In practice, these applications require learning spatiotemporal processes and solutions to partial differential equations on continuous domains at multiple scales – …
Abstract: Large language models can perform algorithmic tasks through test-time computation but struggle to generalize far beyond the task difficulty of the training distribution. These limitations manifest across even simple tasks like arithmetic, string manipulation, and maze solving, where transformers learn shortcuts rather than the underlying algorithms. While prior solutions …
Abstract: How efficiently can we search distributions? The problem is modeled as follows: we are given knowledge of k discrete distributions v_i for 1 <= i <= k over the domain [n] = {1,…,n} which we can preprocess. Then we get samples from an unknown discrete distribution p, also over …
Abstract: In this talk I will survey our recent efforts to develop a rigorous theory for understanding diffusion generative modeling. The first part will cover discretization analyses that prove that diffusion models can approximately sample from arbitrary probability distributions provided one can have a sufficiently accurate estimate for the score …