Poster presenters: There will be numbers on the poster boards, and please post your poster on the board with the corresponding number. All posters will be presented in all poster sessions. You can put up your poster at 2:50pm on Thursday (before the first poster session), and take it down at 1:30pm on Friday (at the end of the last poster session).

Poster Award Candidates

1. Understanding Learned Models by Identifying Important Features at the Right Resolution. Akshay Sood, Kyubin Lee and Mark Craven.

2. Random sampling and efficient algorithms for multiscale PDEs. Ke Chen, Qin Li, Jianfeng Lu and Stephen Wright.

3. Learning to Control Renewal Processes with Bandit Feedback. Semih Cayci, Atilla Eryilmaz and R. Srikant.

4. Speeding up Distributed Computing through Coding. Konstantinos Konstantinidis and Aditya Ramamoorthy.

5. On the Optimal Risk and Optimal Classifier in the Presence of an Adversary. Muni Sreenivas Pydi and Varun Jog.

6. All Nearest Neighbors from Noisy Distances. Blake Mason, Ardhendu Tripathy and Robert Nowak.

7. Substituting ReLUs with Hermite Polynomials gives faster convergence for SSL. Vishnu Suresh Lokhande, Sathya Ravi, Songwong Tasneeyapant, Abhay Venkatesh and Vikas Singh.

8. Learning to Solve Inverse Problems with Neumann Networks. Davis Gilton, Greg Ongie and Rebecca Willett.

Remaining Posters

9. Target-Based Temporal-Difference Learning. Donghwan Lee and Niao He.

10. Convergence and Margin of Adversarial Training on Linearly Separable Data. Shashank Rajput, Zachary Charles, Dimitris Papailiopoulos and Stephen Wright.

11. Supervised Principal Component Analysis via Manifold Optimization. Alex Ritchie, Clayton Scott and Laura Balzano.

12. Robust Converter: A Retrofit Strategy to Improve the Robustness of Neural Networks. Yufei Wang and Yingyu Liang.

13. End-to-end Model Compression in Convolutional Neural Networks. Yiyou Sun, Sathya Ravi and Vikas Sigh.

14. Unsupervised feature selection for manifold alignment of scRNA-seq data. Yutong Wang, Tasha Thong, Justin Colacino, Venkatesh Saligrama, Laura Balzano and Clayton Scott.

15. Mean estimation for entangled single-sample distributions. Ankit Pensia, Varun Jog and Po-Ling Loh.

16. Regularizing Black-box Models for Improved Interpretability. Gregory Plumb, Maruan Al-Shedivat, Eric Xing and Ameet Talwalkar.

17. N-Gram Graph: A Simple and Effective Representation for Molecules. Shengchao Liu, Thevaa Chandereng and Yingyu Liang.

18. Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning. Shengchao Liu, Yingyu Liang and Anthony Gitter.

19. Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models. Yuqi Gu and Gongjun Xu.

20. Predictive Modeling of Sorghum Biomass using Time Series UAV Image Features. Zhou Zhang, Ali Masjedi, Jieqiong Zhao and Melba Crawford.

21. Does Data Augmentation Lead to Positive Margin?. Zhili Feng, Shashank Rajput, Zachary Charles, Po-Ling Loh and Dimitris Papailiopoulos.

22. Draco-Lite: Strong Byzantine resilience through low computational redundancy. Shashank Rajput, Hongyi Wang, Zachary Charles and Dimitris Papailiopoulos.

23. Diffusive Optical Tomography in the Bayesian Framework. Kit Newton, Qin Li and Andrew Stuart.

24. Inter-Language Relationships in Cross-Lingual Embedding Spaces. Shubham Toshniwal, Allyson Ettinger and Karen Livescu.

25. The Scattering Transform of Mallat and its Variations. Michael Perlmutter.

26. On the Convergence Rate of Stochastic Mirror Descent for Nonsmooth Nonconvex Optimization. Siqi Zhang and Niao He.

27. Semantic Adversarial Attacks. Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar and Chinmay Hegde.

28. Energy Monitoring using Machine Learning. Priyabrata Sundaray and Bernard Lesieutre.

29. Adaptive batch sizes for stochastic gradient descent. Scott Sievert.

30. Test-time Attacks on Regression: Fooling Computer Vision into Inferring the Wrong Body Mass Index. Owen Levin, Zihang Meng, Xiaojin Zhu and Vikas Singh.

31. Optimization Algorithms for Dynamic Latent Variable Problems. Sungho Shin, Alexander Smith, S. Joe Qin and Victor Zavala.

32. High Dimensional Chance Constrained Optimization. Bhumesh Kumar and Vivek Borkar.

33. Endotoxin Sensors using Liquid Crystals and Machine Learning. Shengli Jiang, Junghyun Noh, Alexander Smith, Nicholas Abbott and Victor Zavala.

34. Learning Poisson Intensities with Pseudo Mirror Descent. Yingxiang Yang, Negar Kiyavash and Niao He.

35. Multi-modal sentiment analysis using Deep Canonical Correlation Method. Zhongkai Sun, Prathusha Sarma and William Sethares.

36. Adopting Linear Model to Accelerate Neural Network Training. Yin Liu, Junyi Wei, Zijun Ma, Hanying Jiang and Yihan Zhang.

37. Mode Clustering on Markovian Hybrid Model. Zhe Du, Necmiye Ozay and Laura Balzano.

38. Fingerspelling recognition in the wild with iterative visual attention. Bowen Shi, Aurora Martinez del Rio, Jonathan Keane, Diane Brentari, Greg Shakhnarovich and Karen Livescu.

39. FAST: Speeding up Adversarial Training with Stale Updates. Yang Guo, Saurabh Agarwal, Zachary Charles, Stephen Wright and Dimitris Papailiopoulos.

40. A Comparison of Classical and First-Order Methods for Covariance Selection. Liming Wang.

41. Improved Average Performance of VIME Algorithm Using Conditional EVaR. Allan Axelrod and Girish Chowdhary.

42. Finite Time Analysis of Potential-based Reward Shaping. Zhongtian Dai and Matthew Walter.

43. Distributed SGD Generalizes Well Under Asynchrony. Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta and Yingbin Liang.

44. Learning from latent factors in high dimensional data for improved classification accuracy. Yujia Pan and Johann Gagnon-Bartsch.

45. Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization. Xinyan Li, Qilong Gu, Yingxue Zhou, Tiancong Chen and Arindam Banerjee.

46. Sample Average Approximation for Conditional Stochastic Optimization. Yifan Hu, Niao He and Xin Chen.

47. Risk-Averse Explore-Then-Commit Algorithms for Finite-Time Bandits. Ali Yekkehkhany, Ebrahim Arian, Mohammad Hajiesmaili and Rakesh Nagi.

48. Towards Near-imperceptible Steganographic Text. Zhongtian Dai and Zheng Cai.

49. embComp: A System for the Visual Comparison of Vector Embeddings. Florian Heimerl, Christoph Kralj, Torsten Moeller and Michael Gleicher.

50. TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks. Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Doga Gursoy, Francesco Carlo and Ian Foster.

51. Active learning for text classification using rationales. Teja Kanchinadam, Glenn Fung, Qian You and Rick Lentz.

52. Weighted Gradient Coding and Leverage Score Sampling. Neophytos Charalambides, Alfred Hero and Mert Pilanci.

53. Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach. Anindya Bijoy Das and Aditya Ramamoorthy.

54. Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements. Seyedehsara Nayer.

55. Online Asynchronous Coded Caching. Hooshang Ghasemi.

56. An OpenCL-based Hyperdimensional Classifier on CPU and FPGA Platforms. Zheming Jin.

57. Hunting for Dark Matter Substructure in Strong lensing with Neural networks. Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar, Jian Peng and Gilbert Holder.

58. HyperNetwork Re-parameterizatations for Few-Shot Learning. Sudarshan Babu, Pedro Savarese and Michael Maire.

59. Benefits beyond interpretability for disentangled VAE. Iain Campbell and Tim Rogers.


61. Proving Robustness to Training Set Poisoning Attacks. Samuel Drews.

62. Inferring Effective Connectivity From High-Dimensional ECoG Recordings. Christopher Endemann, Declan Campbell, Bryan Krause, Kirill Nourski, Barry Van Veen and Matthew Banks.

63. Efficient Inference of CNNs via Channel Pruning. Boyu Zhang, Azadeh Davoodi and Yu Hen Hu.

64. Panoramic Video Separation with Fast Grassmannian Robust Subspace Tracking. Kyle Gilman and Laura Balzano.

65. An Optimal Control Approach to Sequential Machine Teaching. Laurent Lessard, Xuezhou Zhang and Jerry Zhu.

66. Leveraging large ensemble climate simulations and graph-guided regularization for improving seasonal hydroclimatic forecasting. Abby Stevens, Rebecca Willett, Antonios Mamalakis and Efi Foufoula-Georgiou.

67. Sample Complexity of Species Tree Estimation From a Linear Combination of Internode Distances. Harrison Rosenberg and Sebastien Roch.

68. Nonconvex Distributed Optimization. Bryan Van Scoy and Laurent Lessard.

69. Anomaly Detection via Forecasting and PCA. Aman Lunia and Matthew Malloy.

70. Diversify or Specialize: Voting with Context!. Benjamin Kaufman and Bhumesh Kumar.

71. Learning from small data-sets pertaining to one-class only. Sanjan Gupta, Parameswaran Ramanathan and Jennifer Reed.

72. Fourier Bases for Reinforcement Learning on Combinatorial Puzzles. Horace Pan and Risi Kondor.

73. Mixed Membership Poisson Factorization. Shannon Sequeira and Aakhila Shaheen.

74. Optimal Adversarial Attack on Autoregressive Models. Yiding Chen and Xiaojin Zhu.

75. Gradient-Based Features for Representation Learning. Fangzhou Mu, Yingyu Liang and Yin Li.

76. Deep Adaptive Inference CNNs for Face Recognition. Siddhant Garg, Goutham Ramakrishnan and Varun Thumbe.

77. Fault-Tolerant All-Reduce for Distributed Deep Learning. Yunang Chen and Shivaram Venkataraman.

78. Stochastic Bandits with Delayed Composite Anonymous Feedback. Siddhant Garg and Aditya Kumar Akash.

79. Regularized Image Segmentation for Videos. Adarsh Kumar, Aditya Kumar Akash and Varun Batra.

80. Interpretability via Perceptual Grouping. Zixuan Huang and Yin Li.