On August 2931, 2019 the Center of Mathematical Sciences and Applications will be hosting a workshop on Foundations of Computational Science. The workshop will be held in room G10 of the CMSA, located at 20 Garden Street, Cambridge, MA.
Speakers:
Thursday, August 29
Time 
Speaker 
Title/Abstract 
8:30 – 9:00am 
Breakfast 

9:00 – 9:15am 
Opening 

9:15 – 9:40am 
Bo Zhang 
Title: AI and Mathematics 
9:40 – 10:05am 
Tat Seng Chua 

10:05 – 10:35am 
Group Photo and Coffee Break 

10:35 – 10:50am 
Maosong Sun 
Title: Deep Learningbased Chinese Language Computation at Tsinghua University: Progress and Challenges 
10:50 – 11:05am 
Minlie Huang 
Title: Controllable Text Generation 
11:05 – 11:20am 
Yang Liu 
Title: Natural Language Translation 
11:20 – 11:45am 
Yike Guo 
Title: Data Efficiency in Machine Learning 
11:45 – 12:10pm 
Zuowei Shen 

12:10 – 1:45pm 
Lunch 

1:45 – 2:00pm 
Wenwu Zhu 
Title: Explainable media and network representation 
2:00 – 2:15pm 
Wee Sun Lee 

2:15 – 2:30pm 
Jun Zhu 
Title: Particlebased Inference for Bayesian deep learning 
2:30 – 3:00pm 
Coffee Break 

3:00 – 3:15pm 
Hang Su 
Title: Adversarial attacks in deep learning 
3:15 – 3:30pm 
Ke Deng 
Title: understanding complicated patterns of Chinese texts with very weak training 
3:30 – 4:00pm 
David Gu 
Title: A Geometric View to Optimal Transportation and Generative Adversarial Models 
4:00 – 4:30pm 
Donald Rubin 
Title: Relevant Statistical Evaluations When Comparing Procedures for Analyzing Data 
Friday, August 30
Time 
Speaker 
Title/Abstract 
8:30 – 9:00am 
Breakfast 

9:009:25am 
Qianxiao Li 

9:2510:15am 
Sarah Adel Bargal 
Title: Grounding Deep Models for Improved Decision Making Abstract: Deep models are stateoftheart for many computer vision tasks including object classification, action recognition, and captioning. As Artificial Intelligence systems that utilize deep models are becoming ubiquitous, it is becoming crucial to explain (ground) why they make certain decisions, and utilize such explanations (grounding) to further improve model performance. In this talk, I will present: (1) Frameworks in which grounding guides decisionmaking on the fly at test time by questioning whether the utilized evidence is ‘reasonable’, and during learning through the exploitation of new pathways in deep models. (2) A formulation that simultaneously grounds evidence in space and time, in a single pass, using topdown saliency. This visualizes the spatiotemporal cues that contribute to a deep recurrent neural network’s classification/captioning output. Based on these spatiotemporal cues, segments within a video that correspond with a specific action, or phrase from a caption, could be localized without explicitly optimizing/training for these tasks. 
10:1510:40am 
Xiaoqin Wang 
Title: Encoding and decoding auditory information by the brain 
10:4011:00am 
Coffee Break 

11:0011:15am 
Yuanchun Shi 
Title: From Human Action Data To User Input Intention 
11:1511:30am 
Bin Xu 
Title: AI Practice for Gaokao: Knowledge Graph Construction for Chinese K12 Education 
11:30 – 11:45am 
Peng Cui 
Title: Stable Learning: The Convergence of Causal Inference and Machine Learning 
11:45am – 12:00pm 
Liu Hanzhong 
Title: Penalized regressionadjusted average treatment effect estimates in randomized experiments 
12:00 – 1:30pm 
Lunch 

1:30 – 2:15pm 
Cegiz Pehlevan 

2:15 – 3:00pm 
Sergiy Verstyuk 

3:00 – 3:30pm 
Coffee 

3:30 – 4:15pm 
XiaoLi Meng 
Title: Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox Abstract: Advances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed, this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman, 2013). We first use the modelingwhatweignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory, which is essentially the same as for a standard nonparametric probability/density estimation (Vardi, 1985); though via using group theory, it provides a richer framework for modeling the tradeoff between statistical efficiency and computational efficiency. But there is a real Bayesian paradox: Bayesian analysis cannot be applied exactly for solving Bayesian computation, because to perform the exact Bayesian Monte Carlo integration would require more computation than needed to solve the original Monte Carlo problem. We then show that there is a practical resolution to this paradox using the profile likelihood obtained in Kong et al. (2006) and that this approximation is secondorder valid asymptotically. We also investigate a more computationally efficient approximation via an artificial likelihood of Geyer (1994). This artificial likelihood approach is only firstorder valid, but there is a computationally trivial adjustment to render its secondorder validity. We demonstrate empirically the efficiency of these approximated Bayesian estimators, compared to the usual frequentistbased Monte Carlo estimators, such as bridge sampling estimators (Meng and Wong, 1996). [This is a joint work with Masatoshi Uehara.] 
Saturday, August 31
Time 
Speaker 
Title/Abstract 
8:30 – 9:00am 
Breakfast 

9:00 – 9:45am 
Brian Kulis 

9:45 – 10:30am 
Justin Solomon 
Title: Linking the Theory and Practice of Optimal Transport
Abstract: Optimal transport is a theory linking probability to geometry, with applications across computer graphics, machine learning, and scientific computing. While transport has long been recognized as a valuable theoretical tool, only recently have we developed the computational machinery needed to apply it to practical computational problems. In this talk, I will discuss efforts with my students to scale up transport and related computations, showing that the best algorithm and model for this task depends on details of the application scenario. In particular, we will consider settings in representation learning using entropicallyregularized transport, Bayesian inference using semidiscrete transport, and graphics/PDE using dynamical Eulerian models. 
10:30 – 11:00am 
Coffee 

11:00 – 11:45am 
Mirac Suzgun 

11:45am – 12:30pm 
Jiafeng Chen & Suproteem Sarkar 
Title: Robust and Extensible Deep Learning for Economic and Financial Applications 
12:30 – 2:00pm 
Lunch 

2:00 – 2:45pm 
Scott Kominers 
