Accepted Papers

We are proud to have 23 papers accepted to this year’s conference, in addition to four papers accepted at various NeurIPS workshops. See below for the full list of papers with information to read the full paper.

Main Conference Papers

Generative Causal Explanations of Black-box Classifiers (Poster)

By Matt O’Shaughnessy (Georgia Tech); Greg Canal (Georgia Tech); Marissa Connor (Georgia Tech); Mark Davenport (Georgia Tech); Christopher Rozell (Georgia Tech)


Uncertainty Quantification for Inferring Hawkes Networks

By Haoyun Wang (Georgia Tech); Liyan Xie (Georgia Tech); Alex Cuozzo (Duke University); Simon Mak (Duke University); Yao Xie (Georgia Tech)


Dialog without Dialog: Learning Image-Discriminative Dialog Policies from Single-Shot Question Answering Data

By Michael Cogswell* (SRI International); Jiasen Lu* (Allen Institute for AI); Rishabh Jain (Georgia Tech); Stefan Lee (Oregon State University); Devi Parikh (Georgia Tech and Facebook AI Research); Dhruv Batra (Georgia Tech and Facebook AI Research) * equal contribution


Network Diffusions via Neural Mean-Field Dynamics

By Shushan He (Georgia State University); Hongyuan Zha (Georgia Tech); Xiaojing Ye (Georgia State University)


Learning to Incentivize Other Learning Agents

By Jiachen Yang (Georgia Tech); Ang Li* (DeepMind); Mehrdad Farajtabar* (DeepMind); Peter Sunehag* (DeepMind); Edward Hughes* (DeepMind); Hongyuan Zha (Georgia Tech and AIRS and Chinese University of Hong Kong, Shenzhen) (*external collaborators at DeepMind)


Learning Strategic Network Emergence Games

By R. Trivedi (Georgia Tech); Hongyuan Zha (Georgia Tech and AIRS and Chinese University of Hong Kong, Shenzhen)


Sample Complexity and Effective Dimension for Regression on Manifolds (Poster)

By Andrew McRae (Georgia Tech); Justin Romberg (Georgia Tech); Mark Davenport (Georgia Tech)


Simultaneous Preference and Metric Learning from Paired Comparisons (Spotlight)

By Austin Xu (Georgia Tech); Mark Davenport (Georgia Tech)


A Robust Functional EM Algorithm for Incomplete Panel Count Data (Poster)

By Alexander Moreno (Georgia Tech); Zhenke Wu (University of Michigan); Jamie Yap (University of Michigan); Cho Lam (University of Utah); David Wetter (University of Utah); Inbal Nahum-Shani (University of Michigan); Walter Dempsey (University of Michigan); James M. Rehg (Georgia Tech)


Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function

By Lingkai Kong (Georgia Tech); Molei Tao (Georgia Tech)


Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -A Neural Tangent Kernel Perspective

By Kaixuan Huang+ (Peking University); Yuqing Wang+ (Georgia Tech); Molei Tao (Georgia Tech); Tuo Zhao (Georgia Tech) (+ joint 1st author)


Bayesian Optimization of Risk Measures (Poster)

By Sait Cakmak (Georgia Tech); Rahul Austdillo (Cornell University); Peter Frazier (Cornell University); Enlu Zhou (Georgia Tech)


Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks (Poster)

By Amir Rahimi (ANU, ACRV); Amirreza Shaban (Georgia Tech); Ching-An Cheng (Microsoft Research); Byron Boots (University of Washington); Richard Hartley (Google Research and ANU, ACRV)


Walking in the Shadow: A New Perspective on Feasible Descent Directions

By Hassan Mortagy (Georgia Tech); Swati Gupta (Georgia Tech); Sebastian Pokutta (Zuse Institute Berlin)


Online Learning for Fair Resource Allocation

By Semih Cayci (University of Illinois at Urbana-Champaign); Swati Gupta (Georgia Tech); Atilla Ermilyaz (Ohio State University)


Posterior Re-calibration for Imbalanced Datasets

By Junjiao Tian (Georgia Tech); Yen-Cheng Liu (Georgia Tech); Nathaniel Glaser (Georgia Tech); Yen-Chang Hsu (Georgia Tech); Zsolt Kira (Georgia Tech)


Towards Understanding Hierarchical Learning: Benefits of Neural Representations

By Minshuo Chen (Georgia Tech); Yu Bai (Salesforce Research); Jason Lee (Princeton University); Tuo Zhao (Georgia Tech); Huan Wang (Salesforce Research); Caiming Xiong (Salesforce Research); Richard Socher (Salesforce Research)


Differentiable Top-k Operator with Optimal Transport

By Yujia Xie (Georgia Tech); Hanjun Dai (Google), Minshuo Chen (Georgia Tech); Bo Dai (Google), Tuo Zhao (Georgia Tech); Hongyuan Zha (Georgia Tech); Wei Wei (Google); Tomas Pfister (Google)


Learning Sparse Codes from Compressed Representations with Biologically Plausible Local Wiring Constraint (Poster)

By Kion Fallah* (Georgia Tech); Adam A Willats* (Georgia Tech); Ninghao Liu (Texas A&M University); Christopher J Rozell (Georgia Tech) (*These authors contributed equally to the work)


Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory (Oral)

By Yufeng Zhang (Northwestern University); Qi Cai (Northwestern University); Zhuoran Yang (Princeton University); Yongxin Chen (Georgia Tech); Zhaoran Wang (Northwestern University)


Understanding Deep Architectures with Reasoning Layer

By Xinshi Chen (Georgia Tech); Yufei Zhang (University of Oxford); Christoph Reisinger (University of Oxford); Le Song (Georgia Tech)


Auxiliary Task Reweighting for Minimum-data Learning

By Baifeng Shi (Peking University); Judy Hoffman (Georgia Tech); Kate Saenko (Boston University and MIT-IBM Watson AI Lab); Trevor Darrell (University of California, Berkeley); Huijuan Xu (University of California, Berkeley)


Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes

By Zaiwei Chen (Georgia Tech); Siva Theja Maguluri (Georgia Tech); Sanjay Shakkottai (The University of Texas at Austin); Karthikeyan Shanmugam (IBM Research NY)


Workshop Papers

NetReAct: Interactive Learning for Network Summarization

By Sorour E Amiri (Google); Bijaya Adhikari (University of Iowa); John Wenskovitch (Virginia Tech); Alexander Rodriguez (Georgia Tech); Michelle Dowling (KBI Biopharma); Chris North (Virginia Tech); B. Aditya Prakash (Georgia Tech)

  • Accepted at the NeurIPS 2020 HAMLETS Workshop on Human And Model in the Loop Evaluation and Training Strategie


Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

By Alexander Rodriguez (Georgia Tech); Nikhil Muralidhar (Virginia Tech); Bijaya Adhikari (University of Iowa); Anika Tabassum (Georgia Tech and Virginia Tech); Naren Ramakrishnan (Virginia Tech); B. Aditya Prakash (Georgia Tech)

  • Accepted to the NeurIPS 2020 MLPH Workshop on Machine Learning in Public Health


Using Connectivity Queries to Map Network States

By Alexander Rodriguez (Georgia Tech); Bijaya Adhikari (University of Iowa); Andres D. Gonzalez (University of Oklahoma); Charles Nicholson (University of Oklahoma); Anil Vullikanti (University of Virginia); B. Aditya Prakash (Georgia Tech)

  • Accepted to the NeurIPS 2020 AI+HADR Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response


Estimating Mass Distribution of Articulated Objects Using Non-Prehensile Manipulation

By K. Niranjan Kumar, Irfan Essa, Sehoon Ha, C. Karen Liu

  • Accepted to NeurIPS NeurIPS Object Representations for Learning and Reasoning workshop as an oral presentation


ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision

By David Kartchner, Wendi Ren, Davi Nakajima An, Chao Zhang, Cassie Mitchell

  • Accepted to NeurIPS 2020 HAMLETS workshop on Human and Model in the Loop Evaluation and Training Strategies.


Accelerating Inverse Design of Nanostructures Using Manifold Learning

By Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Hossein Maleki, Omid Hemmatyar, Sajjad Abdollahramezani, Reza Pourabolghasem, and Ali Adibi∗

  • Accepted to the workshop of Machine Learning for Engineering Modeling, Simulation, and Design