58 Results

Poster
Tue 7:00 Learning with Multiple Complementary Labels
LEI FENG, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
Poster
Tue 7:00 Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yufeng Li, Zhi-Hua Zhou
Poster
Tue 7:00 Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John Gregoire, Carla Gomes
Poster
Tue 7:00 Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Chaosheng Dong, Bo Zeng
Poster
Tue 7:00 Layered Sampling for Robust Optimization Problems
Hu Ding, Zixiu Wang
Poster
Tue 7:00 Individual Fairness for k-Clustering
Sepideh Mahabadi, Ali Vakilian
Poster
Tue 7:00 Training Binary Neural Networks through Learning with Noisy Supervision
Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu
Poster
Tue 7:00 Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
shuai zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
Poster
Tue 7:00 Laplacian Regularized Few-Shot Learning
Imtiaz Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed
Poster
Tue 7:00 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl, Kuan-Chieh Wang, Jörn Jacobsen, David Duvenaud, Richard Zemel
Poster
Tue 8:00 DROCC: Deep Robust One-Class Classification
Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain
Poster
Tue 8:00 Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
Chiru Bhattacharyya, Ravindran Kannan
Poster
Tue 9:00 Recovery of Sparse Signals from a Mixture of Linear Samples
Soumyabrata Pal, Arya Mazumdar
Poster
Tue 10:00 Topological Autoencoders
Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
Poster
Tue 10:00 Structured Prediction with Partial Labelling through the Infimum Loss
Vivien Cabannnes, Alessandro Rudi, Francis Bach
Poster
Tue 10:00 Explainable k-Means and k-Medians Clustering
Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost
Poster
Tue 11:00 Quantum Expectation-Maximization for Gaussian mixture models
Alessandro Luongo, Iordanis Kerenidis, Anupam Prakash
Poster
Tue 11:00 Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Yang Liu, Hongyi Guo
Poster
Tue 14:00 Simple and sharp analysis of k-means||
Vaclav Rozhon
Poster
Tue 14:00 Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
John Sipple
Poster
Tue 15:00 Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Edouard Oyallon
Poster
Tue 19:00 Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama
Poster
Wed 5:00 Label-Noise Robust Domain Adaptation
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao
Poster
Wed 5:00 On Learning Language-Invariant Representations for Universal Machine Translation
Han Zhao, Junjie Hu, Andrej Risteski
Poster
Wed 5:00 Median Matrix Completion: from Embarrassment to Optimality
Weidong Liu, Xiaojun Mao, Raymond K. W. Wong
Poster
Wed 5:00 Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang ZHANG, Masanori Koyama, Katsuhiko Ishiguro
Poster
Wed 8:00 Negative Sampling in Semi-Supervised learning
John Chen, Vatsal Shah, Tasos Kyrillidis
Poster
Wed 8:00 Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Poster
Wed 8:00 Sets Clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman
Poster
Wed 8:00 Strength from Weakness: Fast Learning Using Weak Supervision
Joshua Robinson, Stefanie Jegelka, Suvrit Sra
Poster
Wed 8:00 Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar, Tengyu Ma, Percy Liang
Poster
Wed 10:00 Correlation Clustering with Asymmetric Classification Errors
Jafar Jafarov, Sanchit Kalhan, Kostya Makarychev, Yury Makarychev
Poster
Wed 10:00 Time-Consistent Self-Supervision for Semi-Supervised Learning
Tianyi Zhou, Shengjie Wang, Jeff Bilmes
Poster
Wed 12:00 Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard Baraniuk
Poster
Wed 12:00 k-means++: few more steps yield constant approximation
Davin Choo, Christoph Grunau, Julian Portmann, Vaclav Rozhon
Poster
Wed 12:00 T-Basis: a Compact Representation for Neural Networks
Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, Luc Van Gool
Poster
Wed 16:00 Spread Divergence
Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
Poster
Thu 6:00 Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao
Poster
Thu 6:00 A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John P Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas
Poster
Thu 6:00 p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis, Di Wang, Shenghao Yang
Poster
Thu 6:00 Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang, Phillip Isola
Poster
Thu 6:00 Input-Sparsity Low Rank Approximation in Schatten Norm
Yi Li, David Woodruff
Poster
Thu 6:00 How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Chris Finlay, Jörn Jacobsen, Levon Nurbekyan, Adam Oberman
Poster
Thu 6:00 Bridging the Gap Between f-GANs and Wasserstein GANs
Jiaming Song, Stefano Ermon
Poster
Thu 7:00 Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder, Brendan Cook, Matthew Thorpe, Dejan Slepcev
Poster
Thu 7:00 On hyperparameter tuning in general clustering problemsm
Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang
Poster
Thu 7:00 Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
Karl Stratos, Sam Wiseman
Poster
Thu 8:00 Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
Poster
Thu 8:00 Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, LEI FENG, Gang Niu, Xin Geng, Masashi Sugiyama
Poster
Thu 9:00 Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Georgios Smyrnis, Petros Maragos
Poster
Thu 9:00 When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
Poster
Thu 12:00 Missing Data Imputation using Optimal Transport
Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi
Poster
Thu 12:00 Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier Henaff
Poster
Thu 13:00 Amortised Learning by Wake-Sleep
Kevin Wenliang, Theodore Moskovitz, Heishiro Kanagawa, Maneesh Sahani
Poster
Thu 13:00 Topic Modeling via Full Dependence Mixtures
Dan Fisher, Mark Kozdoba, Shie Mannor
Poster
Thu 13:00 On Efficient Low Distortion Ultrametric Embedding
Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde
Poster
Thu 15:00 Stochastic Optimization for Regularized Wasserstein Estimators
Marin Ballu, Quentin Berthet, Francis Bach
Poster
Thu 17:00 Sparse Subspace Clustering with Entropy-Norm
Liang Bai, Jiye Liang