More from the Same Authors

• 2021 : On the Role of Pre-training for Meta Few-Shot Learning »
  Chia-You Chen · Hsuan-Tien Lin · Masashi Sugiyama · Gang Niu
• 2021 : Hamiltonian prior to Disentangle Content and Motion in Image Sequences »
  Asif Khan · Amos Storkey
• 2022 Poster: Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses »
  Yuzhou Cao · Tianchi Cai · Lei Feng · Lihong Gu · Jinjie GU · Bo An · Gang Niu · Masashi Sugiyama
• 2022 Poster: Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks »
  Jianan Zhou · Jianing Zhu · Jingfeng ZHANG · Tongliang Liu · Gang Niu · Bo Han · Masashi Sugiyama
• 2022 : Parity in predictive performance is neither necessary nor sufficient for fairness »
  Justin Engelmann · Miguel Bernabeu · Amos Storkey
• 2022 : Deep Class-Conditional Gaussians for Continual Learning »
  Thomas Lee · Amos Storkey
• 2023 Poster: Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization »
  Xilie Xu · Jingfeng ZHANG · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli
• 2023 Poster: On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective »
  Zeke Xie · Zhiqiang Xu · Jingzhao Zhang · Issei Sato · Masashi Sugiyama
• 2023 Poster: Learning Pareto-Optimal Policies for Multi-Objective Joint Distribution »
  Xin-Qiang Cai · Pushi Zhang · Li Zhao · Jiang Bian · Masashi Sugiyama · Ashley Llorens
• 2023 Poster: Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost »
  Yu-Jie Zhang · Masashi Sugiyama
• 2023 Poster: Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection »
  Xilie Xu · Jingfeng ZHANG · Feng Liu · Masashi Sugiyama · Mohan Kankanhalli
• 2023 Poster: Adapting to Continuous Covariate Shift via Online Density Ratio Estimation »
  Yu-Jie Zhang · Zhen-Yu Zhang · Peng Zhao · Masashi Sugiyama
• 2023 Poster: Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning »
  Ming-Kun Xie · Jiahao Xiao · Hao-Zhe Liu · Gang Niu · Masashi Sugiyama · Sheng-Jun Huang
• 2023 Poster: Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation »
  Jianing Zhu · Yu Geng · Jiangchao Yao · Tongliang Liu · Gang Niu · Masashi Sugiyama · Bo Han
• 2023 Poster: Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems »
  Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama
• 2023 Poster: Imitation Learning from Vague Feedback »
  Xin-Qiang Cai · Yu-Jie Zhang · Chao-Kai Chiang · Masashi Sugiyama
• 2023 Poster: Binary Classification with Confidence Difference »
  Wei Wang · Lei Feng · Yuchen Jiang · Gang Niu · Min-Ling Zhang · Masashi Sugiyama
• 2023 Workshop: Workshop on Distribution Shifts: New Frontiers with Foundation Models »
  Rebecca Roelofs · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Pang Wei Koh · Shiori Sagawa · Tatsunori Hashimoto · Yoonho Lee
• 2022 Spotlight: Lightning Talks 6A-4 »
  Xiu-Shen Wei · Konstantina Dritsa · Guillaume Huguet · ABHRA CHAUDHURI · Zhenbin Wang · Kevin Qinghong Lin · Yutong Chen · Jianan Zhou · Yongsen Mao · Junwei Liang · Jinpeng Wang · Mao Ye · Yiming Zhang · Aikaterini Thoma · H.-Y. Xu · Daniel Sumner Magruder · Enwei Zhang · Jianing Zhu · Ronglai Zuo · Massimiliano Mancini · Hanxiao Jiang · Jun Zhang · Fangyun Wei · Faen Zhang · Ioannis Pavlopoulos · Zeynep Akata · Xiatian Zhu · Jingfeng ZHANG · Alexander Tong · Mattia Soldan · Chunhua Shen · Yuxin Peng · Liuhan Peng · Michael Wray · Tongliang Liu · Anjan Dutta · Yu Wu · Oluwadamilola Fasina · Panos Louridas · Angel Chang · Manik Kuchroo · Manolis Savva · Shujie LIU · Wei Zhou · Rui Yan · Gang Niu · Liang Tian · Bo Han · Eric Z. XU · Guy Wolf · Yingying Zhu · Brian Mak · Difei Gao · Masashi Sugiyama · Smita Krishnaswamy · Rong-Cheng Tu · Wenzhe Zhao · Weijie Kong · Chengfei Cai · WANG HongFa · Dima Damen · Bernard Ghanem · Wei Liu · Mike Zheng Shou
• 2022 Spotlight: Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks »
  Jianan Zhou · Jianing Zhu · Jingfeng ZHANG · Tongliang Liu · Gang Niu · Bo Han · Masashi Sugiyama
• 2022 Workshop: Workshop on Distribution Shifts: Connecting Methods and Applications »
  Chelsea Finn · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Jonas Peters · Rebecca Roelofs · Shiori Sagawa · Pang Wei Koh · Yoonho Lee
• 2022 Poster: Adapting to Online Label Shift with Provable Guarantees »
  Yong Bai · Yu-Jie Zhang · Peng Zhao · Masashi Sugiyama · Zhi-Hua Zhou
• 2022 Poster: Fast and Robust Rank Aggregation against Model Misspecification »
  YUANGANG PAN · Ivor W. Tsang · Weijie Chen · Gang Niu · Masashi Sugiyama
• 2022 Poster: Hamiltonian Latent Operators for content and motion disentanglement in image sequences »
  Asif Khan · Amos Storkey
• 2022 Poster: Synergy-of-Experts: Collaborate to Improve Adversarial Robustness »
  Sen Cui · Jingfeng ZHANG · Jian Liang · Bo Han · Masashi Sugiyama · Changshui Zhang
• 2022 Poster: Learning Contrastive Embedding in Low-Dimensional Space »
  Shuo Chen · Chen Gong · Jun Li · Jian Yang · Gang Niu · Masashi Sugiyama
• 2021 Workshop: Second Workshop on Quantum Tensor Networks in Machine Learning »
  Xiao-Yang Liu · Qibin Zhao · Ivan Oseledets · Yufei Ding · Guillaume Rabusseau · Jean Kossaifi · Khadijeh Najafi · Anwar Walid · Andrzej Cichocki · Masashi Sugiyama
• 2021 : Discussion: Chelsea Finn, Masashi Sugiyama »
  Chelsea Finn · Masashi Sugiyama
• 2021 : Importance Weighting for Transfer Learning »
  Masashi Sugiyama
• 2021 Poster: Loss function based second-order Jensen inequality and its application to particle variational inference »
  Futoshi Futami · Tomoharu Iwata · naonori ueda · Issei Sato · Masashi Sugiyama
• 2021 Poster: Probabilistic Margins for Instance Reweighting in Adversarial Training »
  qizhou wang · Feng Liu · Bo Han · Tongliang Liu · Chen Gong · Gang Niu · Mingyuan Zhou · Masashi Sugiyama
• 2021 Poster: Gradient-based Hyperparameter Optimization Over Long Horizons »
  Paul Micaelli · Amos Storkey
• 2020 Poster: Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning »
  Yu Yao · Tongliang Liu · Bo Han · Mingming Gong · Jiankang Deng · Gang Niu · Masashi Sugiyama
• 2020 Poster: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
  Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama
• 2020 Spotlight: Part-dependent Label Noise: Towards Instance-dependent Label Noise »
  Xiaobo Xia · Tongliang Liu · Bo Han · Nannan Wang · Mingming Gong · Haifeng Liu · Gang Niu · Dacheng Tao · Masashi Sugiyama
• 2020 Poster: Self-Supervised Relational Reasoning for Representation Learning »
  Massimiliano Patacchiola · Amos Storkey
• 2020 Spotlight: Self-Supervised Relational Reasoning for Representation Learning »
  Massimiliano Patacchiola · Amos Storkey
• 2020 Poster: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
  Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama
• 2020 Poster: Learning from Aggregate Observations »
  Yivan Zhang · Nontawat Charoenphakdee · Zhenguo Wu · Masashi Sugiyama
• 2020 Poster: Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring »
  Taira Tsuchiya · Junya Honda · Masashi Sugiyama
• 2020 Spotlight: Rethinking Importance Weighting for Deep Learning under Distribution Shift »
  Tongtong Fang · Nan Lu · Gang Niu · Masashi Sugiyama
• 2020 Poster: Provably Consistent Partial-Label Learning »
  Lei Feng · Jiaqi Lv · Bo Han · Miao Xu · Gang Niu · Xin Geng · Bo An · Masashi Sugiyama
• 2020 Poster: Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels »
  Massimiliano Patacchiola · Jack Turner · Elliot Crowley · Michael O'Boyle · Amos Storkey
• 2020 Spotlight: Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels »
  Massimiliano Patacchiola · Jack Turner · Elliot Crowley · Michael O'Boyle · Amos Storkey
• 2020 Poster: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
  Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama
• 2020 Oral: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators »
  Takeshi Teshima · Isao Ishikawa · Koichi Tojo · Kenta Oono · Masahiro Ikeda · Masashi Sugiyama
• 2019 : Poster Presentations »
  Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange
• 2019 Poster: Zero-shot Knowledge Transfer via Adversarial Belief Matching »
  Paul Micaelli · Amos Storkey
• 2019 Spotlight: Zero-shot Knowledge Transfer via Adversarial Belief Matching »
  Paul Micaelli · Amos Storkey
• 2019 Poster: Learning to Learn By Self-Critique »
  Antreas Antoniou · Amos Storkey
• 2019 Poster: Uncoupled Regression from Pairwise Comparison Data »
  Ritsugen Jo · Junya Honda · Gang Niu · Masashi Sugiyama
• 2019 Poster: Are Anchor Points Really Indispensable in Label-Noise Learning? »
  Xiaobo Xia · Tongliang Liu · Nannan Wang · Bo Han · Chen Gong · Gang Niu · Masashi Sugiyama
• 2019 Poster: On the Calibration of Multiclass Classification with Rejection »
  Chenri Ni · Nontawat Charoenphakdee · Junya Honda · Masashi Sugiyama
• 2018 Poster: Binary Classification from Positive-Confidence Data »
  Takashi Ishida · Gang Niu · Masashi Sugiyama
• 2018 Spotlight: Binary Classification from Positive-Confidence Data »
  Takashi Ishida · Gang Niu · Masashi Sugiyama
• 2018 Poster: Uplift Modeling from Separate Labels »
  Ikko Yamane · Florian Yger · Jamal Atif · Masashi Sugiyama
• 2018 Poster: Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces »
  Motoya Ohnishi · Masahiro Yukawa · Mikael Johansson · Masashi Sugiyama
• 2018 Poster: Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks »
  Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama
• 2018 Poster: Masking: A New Perspective of Noisy Supervision »
  Bo Han · Jiangchao Yao · Gang Niu · Mingyuan Zhou · Ivor Tsang · Ya Zhang · Masashi Sugiyama
• 2018 Poster: Moonshine: Distilling with Cheap Convolutions »
  Elliot Crowley · Gavia Gray · Amos Storkey
• 2018 Poster: Co-teaching: Robust training of deep neural networks with extremely noisy labels »
  Bo Han · Quanming Yao · Xingrui Yu · Gang Niu · Miao Xu · Weihua Hu · Ivor Tsang · Masashi Sugiyama
• 2017 : Poster Session (encompasses coffee break) »
  Beidi Chen · Borja Balle · Daniel Lee · iuri frosio · Jitendra Malik · Jan Kautz · Ke Li · Masashi Sugiyama · Miguel A. Carreira-Perpinan · Ramin Raziperchikolaei · Theja Tulabandhula · Yung-Kyun Noh · Adams Wei Yu
• 2017 Poster: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
  Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama
• 2017 Poster: Learning from Complementary Labels »
  Takashi Ishida · Gang Niu · Weihua Hu · Masashi Sugiyama
• 2017 Oral: Positive-Unlabeled Learning with Non-Negative Risk Estimator »
  Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama
• 2017 Poster: Expectation Propagation for t-Exponential Family Using q-Algebra »
  Futoshi Futami · Issei Sato · Masashi Sugiyama
• 2017 Poster: Generative Local Metric Learning for Kernel Regression »
  Yung-Kyun Noh · Masashi Sugiyama · Kee-Eung Kim · Frank Park · Daniel Lee
• 2016 Poster: Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning »
  Gang Niu · Marthinus Christoffel du Plessis · Tomoya Sakai · Yao Ma · Masashi Sugiyama
• 2015 Poster: Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling »
  Xiaocheng Shang · Zhanxing Zhu · Benedict Leimkuhler · Amos Storkey
• 2014 Workshop: NIPS Workshop on Transactional Machine Learning and E-Commerce »
  David Parkes · David H Wolpert · Jennifer Wortman Vaughan · Jacob D Abernethy · Amos Storkey · Mark Reid · Ping Jin · Nihar Bhadresh Shah · Mehryar Mohri · Luis E Ortiz · Robin Hanson · Aaron Roth · Satyen Kale · Sebastien Lahaie
• 2014 Poster: Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP »
  Shinichi Nakajima · Issei Sato · Masashi Sugiyama · Kazuho Watanabe · Hiroko Kobayashi
• 2014 Poster: Multitask learning meets tensor factorization: task imputation via convex optimization »
  Kishan Wimalawarne · Masashi Sugiyama · Ryota Tomioka
• 2014 Poster: Analysis of Learning from Positive and Unlabeled Data »
  Marthinus C du Plessis · Gang Niu · Masashi Sugiyama
• 2013 Poster: Parametric Task Learning »
  Ichiro Takeuchi · Tatsuya Hongo · Masashi Sugiyama · Shinichi Nakajima
• 2013 Poster: Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering »
  Shinichi Nakajima · Akiko Takeda · S. Derin Babacan · Masashi Sugiyama · Ichiro Takeuchi
• 2012 Poster: Perfect Dimensionality Recovery by Variational Bayesian PCA »
  Shinichi Nakajima · Ryota Tomioka · Masashi Sugiyama · S. Derin Babacan
• 2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
  Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey
• 2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
  Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey
• 2012 Poster: Density-Difference Estimation »
  Masashi Sugiyama · Takafumi Kanamori · Taiji Suzuki · Marthinus C du Plessis · Song Liu · Ichiro Takeuchi
• 2012 Poster: The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes »
  Simon Lyons · Amos Storkey · Simo Sarkka
• 2011 Poster: Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability »
  David Reichert · Peggy Series · Amos Storkey
• 2011 Poster: Relative Density-Ratio Estimation for Robust Distribution Comparison »
  Makoto Yamada · Taiji Suzuki · Takafumi Kanamori · Hirotaka Hachiya · Masashi Sugiyama
• 2011 Poster: Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification »
  Ichiro Takeuchi · Masashi Sugiyama
• 2011 Spotlight: Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability »
  David Reichert · Peggy Series · Amos Storkey
• 2011 Poster: Analysis and Improvement of Policy Gradient Estimation »
  Tingting Zhao · Hirotaka Hachiya · Gang Niu · Masashi Sugiyama
• 2011 Poster: Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent »
  Shinichi Nakajima · Masashi Sugiyama · S. Derin Babacan
• 2010 Spotlight: Global Analytic Solution for Variational Bayesian Matrix Factorization »
  Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka
• 2010 Poster: Global Analytic Solution for Variational Bayesian Matrix Factorization »
  Shinichi Nakajima · Masashi Sugiyama · Ryota Tomioka
• 2010 Poster: Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model »
  David Reichert · Peggy Series · Amos Storkey
• 2010 Poster: Sparse Instrumental Variables (SPIV) for Genome-Wide Studies »
  Felix V Agakov · Paul McKeigue · Jon Krohn · Amos Storkey
• 2008 Poster: Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection »
  Takafumi Kanamori · Shohei Hido · Masashi Sugiyama
• 2007 Poster: Continuous Time Particle Filtering for fMRI »
  Lawrence Murray · Amos Storkey
• 2007 Poster: Modelling motion primitives and their timing in biologically executed movements »
  Ben H Williams · Marc Toussaint · Amos Storkey
• 2007 Poster: Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation »
  Masashi Sugiyama · Shinichi Nakajima · Hisashi Kashima · Paul von Buenau · Motoaki Kawanabe
• 2007 Poster: Multi-Task Learning via Conic Programming »
  Tsuyoshi Kato · Hisashi Kashima · Masashi Sugiyama · Kiyoshi Asai
• 2006 Workshop: Learning when test and training inputs have different distributions »
  Joaquin Quiñonero-Candela · Masashi Sugiyama · Anton Schwaighofer · Neil D Lawrence
• 2006 Poster: Learning Structural Equation Models for fMRI »
  Amos Storkey · Enrico Simonotto · Heather Whalley · Stephen Lawrie · Lawrence Murray · David McGonigle