Events

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

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.

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

In 1960, Wigner published an article famously titled “The Unreasonable Effectiveness of Mathematics in the Natural Sciences”. In this talk we will, in a small way, follow the spirit of Wigner’s coinage, and explore the unreasonable effectiveness of negatively associated (i.e., self-repelling) stochastic systems far beyond their context of origin.

Aadhaar Chaturvedi (The University of Auckland Business School) – Contracting and Information Sharing in Supply Chains with a Common Retailer & Competing Manufacturers

HASS Conversations in Design, Technology, and Society Series | Hazardous Data: The Entanglements of Digital Debt in India

In this talk, first, I will present an efficient data-driven computational solution and bounding approach for emergency medical service (EMS) fleet allocation and redeployment, i.e., (re-)positioning ambulances to bases to maximize the system’s service level. Using a discrete-event simulator at the core, we first model ambulance allocation as an approximately-submodular-maximization problem, and devise a simple and efficient greedy algorithm that is applicable for both static allocation and dynamic redeployment. Second, to better evaluate the efficiency of dynamic redeployment, we develop novel penalized information-relaxation bounds that avoid incurring the curse of dimensionality common to such approaches. Our bounding methods solve a hitherto open problem and help tighten regret bounds for large classes of online resource allocation problems beyond the EMS setting.

Traditional AI algorithms entail resourceful and reliable computation infrastructure, whereas their efficacy cannot be ensured in many emerging distributed systems (e.g., sensor networks, vehicular networks, edge networks etc.). Deploying AI algorithms in these distributed systems entail many new concerns including fault tolerance, resource efficiency, privacy preservation etc. Furthermore, recent years have witnessed the development of AI algorithms, which provides a new way to improve the intellectuality of the distributed systems such that the efficacy of the systems can be guaranteed in application scenarios with uncertainty. In this talk, by following the above roadmap, I will highlight several research projects conducted by myself and my collaborators in the areas of distributed intelligence, mainly from an algorithmic perspective.

Eugene Lim (GOVTECH) – Reverse Engineering Cryptographic Implementations in Software and Teo Sui Guan (GOVTECH) – Cryptography and its use in Government

HASS Colloquium Series: Intersecting research and pedagogy: Structures of control and empowerment by Dr Roslynn Ang