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: 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: Mixed integer quadratic programming is the problem of minimizing a quadratic polynomial over points in a polyhedral region with some integer components. It is a natural extension of mixed integer linear programming, and it has a wide array of applications. In this talk, I will survey some recent theoretical …
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 …