Events

DH Asia Webinar Series: New Horizon of Digital Humanities? Computational Social Science and its Application by Dr. Hsuanlei Shao

Consider the following two-person mixed strategy game of a probabilist against Nature with respect to the parameters (f,B,π), where f is a convex function satisfying certain regularity conditions, B is either the set {L_i}_{i=1}^{n} or its convex hull with each L_i being a Markov infinitesimal generator on a finite state space X and π is a given positive discrete distribution on X. The probabilist chooses a prior measure μ within the set of probability measures on B denoted by P(B) and picks a L∈B at random according to μ, whereas Nature follows a pure strategy to select M∈L(π), the set of ππ-reversible Markov generators on X. Nature pays an amount D_f(M||L), the f-divergence from L to M, to the probabilist. We prove that a mixed strategy Nash equilibrium always exists, and establish a minimax result on the expected payoff of the game. This also contrasts with the pure strategy version of the game where we show a Nash equilibrium may not exist. To find approximately a mixed strategy Nash equilibrium, we propose and develop a simple projected subgradient algorithm that provably converges with a rate of O(1/sqrt{t}), where t is the number of iterations. In addition, we elucidate the relationships of Nash equilibrium with other seemingly disparate notions such as weighted information centroid, Chebyshev center and Bayes risk. This talk highlights the powerful interplay and synergy between modern Markov chains theory and geometry, information theory, game theory, optimization and mathematical statistics. This is based on a joint work with Geoffrey Wolfer (RIKEN AIP), and the paper can be found in https://arxiv.org/abs/2310.04115

BUILDSomething 23/24′

VIE @ SWITCH 2023

In this talk we introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle we obtain a local-to-global paradigm, namely solving a local, i.e., a one time-step robust optimization problem leads to an optimizer of the global (i.e. infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure.

ARISE: Venture – Pitching for Investment

ARISE: Venture – Panel Discussion: Foundation understanding of shareholder’s (Founder) rights and responsibilities

Brian Lim (Primer AI) – NLP/LLM Applications in Industry

ARISE: Venture – Business Modeling, Plans and Raising Finance

ISTD Pillar Sharing and Industry Engagement Day