Scribing Assignment

	A	B	C	D
1	Date	Lecture (Tentative Schedule)	Scribe #1	Scribe #2
2	Module I: Background Knowledge
3	08/23	Optimization: convex preliminary	Aryaman	Aditya S.
4	08/28	Optimization: convex set and functions	Lu Li	Santusht S.
5	08/30	Optimization: conjugate and gradient descent	Abhinav Gullapalli	Mahdi G
6	09/06	Sampling: basic sampling method	Feng Zhao	Vedaant Shah
7	09/11	Sampling: Acceptance-Rejection & Importance Sampling	Shiva Ramaswami	Kartik Narang
8	09/13	Sampling: MCMC (MH, Gibbs & Hamiltonian)	Hangtian Zhu	Wenbo Chen
9	09/18	Density Parametrization	Bharat Goyal	Chetan R
10	09/25	Neural Network Revisit	Zhaoyu Xu	Mian Wu
11	Module II: Deep Generative Models
12	09/27	EBM (CD, Score Matching)	TA
13	10/02	Autoregressive Model	Weihan Li	Shiqin Zeng
14	10/04	VAE from dual view	Eshani Chauk	Harsha Karanth
15	10/11	EBM and Diffusion		Ozgur Kara
16	10/16	Decision-Focused Learning	Ronit	Sumanth K
17	10/18	Foundation Models for Decision Making: Problems, Methods, and Applications	Rishi Banerjee
18	10/23	Generative Adversarial Nets (GAN)	Jiahong Zhang	Aditya Akula
19	10/25	Normalizing Flow Models	Ruomeng Ding
20	Module III: Differentiable Programming
21	10/30	Differentiable Algorithm I: differentiable optimizer/dynamic programming	Yifeng Yu	Yilong Tang
22	11/01	Differentiable Algorithm II: top-K/sorting layer	Zack Aristei
23	Module IV: Reinforcement Learning
24	11/06	MDP: Bellman Recursion	YanTong Lin	Uzair Akbar
25	11/08	DP: Value and Policy Iteration	Ignat Georgiev	Chloe Saleh
26	11/13	MDP: Linear Programming	TA
27	11/15	Offline Reinforcement Learning	Rishi Goswami	Hiren Kumawat
28	11/20	Imitation Learning	Manthan Joshi	Albert Wilcox
29	11/27	Policy Gradient	Amish Saini	Ninaad Lakshman
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