Date/Time : 22 Feb 2023 15:00
Venue : S17 #04-05 (SR2)
Speaker : Tan Yan Fu, Vincent, National University of Singapore

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In this seminar, I will discuss two works of mine post-tenure, both in the study of multi-armed bandits (MABs), a popular model in online decision making.

In the first problem, we consider the cascading bandits model in the regret minimization setting. There are two major classes of learning algorithms for minimizing the regret in MABs, namely the upper confidence bound (UCB) and Thompson sampling (TS) algorithms. On one hand, the analysis of TS usually involves additional analytical challenges compared to UCB. On the other hand, the empirical performance of TS is usually better than UCB. Consequently, for MAB problems, it is desirable to derive a theoretical guarantee for the performance of TS, but this was unavailable for the cascading bandits model prior to our work. We prove a theoretical performance guarantee for a TS-based algorithm TS-Cascade that matches that of UCB.

In the second problem, we consider the best arm identification problem in linear MABs in the so-called fixed-horizon setting. By leveraging properties of the G-optimal design and incorporating it into the arm allocation rule, we design a parameter-free algorithm, Optimal Design-based Linear Best Arm Identification (OD-LinBAI). We provide a theoretical analysis of the failure probability of OD-LinBAI. Instead of all the optimality gaps, the performance of OD-LinBAI depends only on the gaps of the top d arms, where d is the effective dimension of the linear bandit instance. Complementarily, we present a minimax lower bound for this problem. The upper and lower bounds show that OD-LinBAI is minimax optimal up to constant multiplicative factors in the exponent, which is a significant improvement over existing methods (e.g., BayesGap, Peace, LinearExploration, and GSE), and settles the question of ascertaining the difficulty of learning the best arm in the fixed-horizon setting.

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Date/Time : 30 Nov 2022 14:00
Venue : S17 #04-06 (SR1)
Speaker : Ji Hui, National University of Singapore

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https://nus-sg.zoom.us/j/85864142504?pwd=aUUyaFRETE5hYit6WlYyMGhhdDZYUT09

Meeting ID: 858 6414 2504
Passcode: 764936

Imaging has been vital in advancing science, medicine, engineering, and industrial applications. Today’s imaging systems increasingly rely on computing power and algorithms to indirectly form images (visual interpretation) from only partial and noisy measurements. Most image reconstruction tasks are about solving a challenging linear or non-linear ill-posed inverse problem. For the past few decades, we have witnessed phenomenal developments in mathematical theories, models, and algorithms for solving inverse imaging problems. This talk will start by reviewing our works on prevailing model-driven mathematical approaches in computational imaging with manually-crafted model/transforms, including wavelets and sparsity-based regularization. Then we will describe how to advance these studies into the era of data-driven approaches that directly learn intrinsic structures from image data. Finally, we will discuss the connection between these data-driven models/algorithms and deep convolutional neural networks, which led to further advances in self-supervised deep learning for computational imaging.

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Date/Time : 26 Oct 2022 14:00
Venue : S17 #04-06 (SR1)
Speaker : Cai Zhenning, National University of Singapore

Abstract: The gas kinetic theory is usually employed to solve rarefied gas dynamics. The fundamental mathematical model for the gas kinetic theory is the Boltzmann equation, which describes the fluid states using the distribution function defined over the joint position-velocity space. Due to its high dimensionality and a complicated integral operator, straightforward discretization of the Boltzmann equation will lead to high computational cost. Therefore, we need either highly effective numerical schemes or reduced kinetic models to make simulations feasible. In this talk, I will introduce our improvement on the classical Fourier method to preserve all physical properties including the conservation laws and the entropy dissipation. Also, we will discuss methods for kinetic model reductions including the simplification of the collision operator and the moment methods. Our simplified collision operator includes a parameter that can be adjusted to balance the computational cost and the accuracy, and this parameter can be tuned adaptively during the simulation of the dynamics. As for the moment methods, we study the linear Boltzmann equation and introduce the regularized 13-moment equations with stable boundary conditions, which can be applied to solve slow-moving gases with moderate rarefaction effects.

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Date/Time : 04 Oct 2022 17:00
Venue : S17 #04-06 (SR1)
Speaker : Sun Rongfeng, National University of Singapore

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https://nus-sg.zoom.us/j/85460997100?pwd=S2FVVXUzSGU0U0tSN3Z4MURVSGhVUT09

Meeting ID: 854 6099 7100
Passcode: 504631

Abstract: The stochastic heat equation (SHE) and the Kardar-Parisi-Zhang (KPZ) equation are singular stochastic partial differential equations, for which robust solution theories have been developed in dimension 1. But in the critical dimension 2 for SHE and KPZ, no robust solution theory exists. I will review joint work with Caravenna and Zygouras on the scaling limit of the 2-dimensional directed polymer model, a space-time discretisation of the SHE, which implies that the 2-dimensional SHE and KPZ undergo a phase transition. Below the critical point, both SHE and KPZ have Gaussian fluctuations. At the critical point, we recently showed that the directed polymer model has a unique limit, which can be interpreted as the solution of the critical 2-dimensional SHE.

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Date/Time : 11 Apr 2022 14:00
Venue : via Zoom
Speaker : Tong Xin, National University of Singapore

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https://nus-sg.zoom.us/j/82203277015?pwd=R1o1cHlTc1FueUM0c3ZETEtZQngzUT09

Meeting ID: 822 0327 7015
Passcode: 485328

Abstract: In many areas, stochastic algorithms are the main computational workhorses. In order to handle modern-day problems, we need these algorithms to scale well with model dimension and data size. How to design such algorithms with theoretical guarantees is important for both practical and theoretical interests.

In this talk, I will discuss how to use stochastic algorithms to solve problems in data assimilation, inverse problems and statistical machine learning. I will focus on how to exploit different problem structures to make the algorithms computationally efficient.

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Date/Time : 11 Dec 2020 14:00
Venue : via Zoom
Speaker : Zhang Lei, National University of Singapore

Join Zoom Meeting https://nus-sg.zoom.us/j/82676230389?pwd=UmJtUTN2dzhycTJ2UTFrWHpZVXpTZz09 Meeting ID: 826 7623 0389 Password: 654751 Abstract: Fourier coefficients of automorphic representations play a fundamental role in number theory and representation theory, the theory of automorphic forms in particular. For instance, they are used to count the number of representations of a natural number as the sum of four squares and now they play a crucial role in our approach to the celebrated Gan-Gross-Prasad Conjecture. One of my main goals is to justify the Local-Global principle of those Fourier coefficients. Motivated by this, I investigated various cases and connections with other topics in the past several years. I will report my results and my future research plan in this talk.

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Date/Time : 14 Aug 2019 15:00
Venue : S17 #04-04 SR3
Speaker : Zhou Chao, National University of Singapore

After the last financial crisis, model uncertainty attracted academics and practitioners’ attention. New theories have been established to improve the way of quantifying model uncertainty. And innovative work has been done on the research of optimal portfolio strategy under model uncertainty. Stochastic control techniques have been widely used in these recent works. In this talk, I will present my contribution to several problems under model uncertainty in Quantitative Finance. Moreover, I will provide the necessary background to understand the results.

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Date/Time : 18 Jan 2019 10:00
Venue : S17 #04-04 SR3
Speaker : Tan Kai Meng, National University of Singapore

Despite its rich structure where combinatorics play an important role, the modular representation theory of the symmetric group is still not at all well understood. Many fundamental questions remain open to this day. Perhaps the most famous among them is the complete determination of its decomposition numbers. The advent and development of the Khovanov-Lauda-Rouquier algebras in the last decade shows that the symmetric group algebras can be non-trivially Z-graded, giving rise to graded decomposition numbers and providing new understanding to the old decomposition numbers. On the other hand, the announcement of counterexamples to James’s conjecture by Williamson ensures that the decomposition numbers remain as mysterious as ever, if not more. The talk will briefly survey the developments and describe my contributions to this elusive open problem.

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Date/Time : 23 Nov 2018 15:00
Venue : S17 #04-06 SR1
Speaker : Pang Chin How, Jeffrey, National University of Singapore

I will talk about my work on designing a distributed optimization algorithm based on dual ascent (or Dykstra’s splitting) that is asynchronous, decentralized, and has deterministic convergence. We also discuss the extension to the case of directed unreliable edges, and on using majorizations so that one can use subgradients approximations or outer approximations of constraint sets to simplify subproblems to be solved. We also studying convergence rates. I will then describe how I had started working on this area after noticing a heuristic for improving the convergence of the method of alternating projections.

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