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ORCIDTransactions on Machine Learning Research, Volume 2022
Volume 2022, 2022
- Yiwei Lu, Gautam Kamath, Yaoliang Yu:
Indiscriminate Data Poisoning Attacks on Neural Networks. - Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet:
COIN++: Neural Compression Across Modalities. - Wei Wei, Hengguan Huang, Xiangming Gu, Hao Wang, Ye Wang:
Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization. - Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola, Jun Pang:
Unsupervised Network Embedding Beyond Homophily. - Thomas George, Guillaume Lajoie, Aristide Baratin:
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty. - Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti:
On Characterizing the Trade-off in Invariant Representation Learning. - Aysegul Bumin, Kejun Huang:
Stochastic Douglas-Rachford Splitting for Regularized Empirical Risk Minimization: Convergence, Mini-batch, and Implementation. - Tong Mu, Stephan Zheng, Alexander R. Trott:
Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning. - Chenjie Cao, Xinlin Ren, Yanwei Fu:
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth. - Zhiri Yuan, Xixu Hu, Qi Wu, Shumin Ma, Cheuk Hang Leung, Xin Shen, Yiyan Huang:
A Unified Domain Adaptation Framework with Distinctive Divergence Analysis. - Mridul Agarwal, Qinbo Bai, Vaneet Aggarwal:
Concave Utility Reinforcement Learning with Zero-Constraint Violations. - Shlok Kumar Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Abhyuday N. Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan:
Object-aware Cropping for Self-Supervised Learning. - Sindy Löwe, Phillip Lippe
, Maja Rudolph, Max Welling:
Complex-Valued Autoencoders for Object Discovery. - Joey Bose, Ricardo Pio Monti, Aditya Grover:
Controllable Generative Modeling via Causal Reasoning. - Lawrence K. Saul:
A geometrical connection between sparse and low-rank matrices and its application to manifold learning. - Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami:
A Stochastic Optimization Framework for Fair Risk Minimization. - Gergely Dániel Németh, Miguel Angel Lozano, Novi Quadrianto, Nuria Oliver Ramirez:
A Snapshot of the Frontiers of Client Selection in Federated Learning. - Adam Fisch, Tommi S. Jaakkola, Regina Barzilay:
Calibrated Selective Classification. - Bryan Eikema, Germán Kruszewski, Christopher R. Dance, Hady Elsahar, Marc Dymetman:
An approximate sampler for energy-based models with divergence diagnostics. - Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Extracting Local Reasoning Chains of Deep Neural Networks. - Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao:
Algorithms and Theory for Supervised Gradual Domain Adaptation. - Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe:
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful. - Lu Han, Han-Jia Ye, De-Chuan Zhan:
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. - Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang:
GIT: A Generative Image-to-text Transformer for Vision and Language. - Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi:
Collaborative Algorithms for Online Personalized Mean Estimation. - Titouan Vayer, Romain Tavenard, Laetitia Chapel, Rémi Flamary, Nicolas Courty, Yann Soullard:
Time Series Alignment with Global Invariances. - Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling:
Optimizing Intermediate Representations of Generative Models for Phase Retrieval. - Jaime Spencer, Chris Russell, Simon Hadfield, Richard Bowden:
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter. - Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet:
An empirical study of implicit regularization in deep offline RL. - Thao Nguyen, Maithra Raghu, Simon Kornblith:
On the Origins of the Block Structure Phenomenon in Neural Network Representations. - Scott E. Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas:
A Generalist Agent. - Ying Nie, Kai Han, Zhenhua Liu, Chuanjian Liu, Yunhe Wang:
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution. - Christopher Gorham Lester:
Using unsupervised learning to detect broken symmetries, with relevance to searches for parity violation in nature. - Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo:
Fourier Sensitivity and Regularization of Computer Vision Models. - Leon Sixt, Tim Landgraf:
A Rigorous Study Of The Deep Taylor Decomposition. - Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh:
On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces. - Jing Wang, Jie Shen, Xiaofei Ma, Andrew O. Arnold:
Uncertainty-Based Active Learning for Reading Comprehension. - Benjamin Rhodes, Michael U. Gutmann:
Enhanced gradient-based MCMC in discrete spaces. - Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, Yonghui Wu:
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation. - Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi:
Diffusion Models for Video Prediction and Infilling. - Jakob J. Hollenstein, Sayantan Auddy, Matteo Saveriano, Erwan Renaudo, Justus H. Piater:
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance. - Philipp Geiger, Christoph-Nikolas Straehle:
Fail-Safe Adversarial Generative Imitation Learning. - Valentin De Bortoli:
Convergence of denoising diffusion models under the manifold hypothesis. - Abram Magner, Carolyn S. Kaminski, Petko Bogdanov:
Fast and Accurate Spreading Process Temporal Scale Estimation. - Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar:
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. - Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. - Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai:
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning. - Alberto Bordino, Stefano Favaro, Sandra Fortini:
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions. - Andreas Kirsch, Yarin Gal:
A Note on "Assessing Generalization of SGD via Disagreement". - Jary Pomponi, Simone Scardapane, Aurelio Uncini:
Centroids Matching: an efficient Continual Learning approach operating in the embedding space. - Carlo Baldassi:
Systematically and efficiently improving $k$-means initialization by pairwise-nearest-neighbor smoothing. - Saptarshi Saha, Utpal Garain:
On Noise Abduction for Answering Counterfactual Queries: A Practical Outlook. - Wenshuo Guo, Serena Lutong Wang, Peng Ding, Yixin Wang, Michael I. Jordan:
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. - Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström:
Incorporating Sum Constraints into Multitask Gaussian Processes. - Andreas Kirsch, Yarin Gal:
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. - Kun Yang, Samory Kpotufe, Nick Feamster:
An Efficient One-Class SVM for Novelty Detection in IoT. - Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross, Kamyar Azizzadenesheli:
Generative Adversarial Neural Operators. - Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge:
If your data distribution shifts, use self-learning. - Nixie S. Lesmana, Huangyuan Su, Chi Seng Pun:
Reinventing Policy Iteration under Time Inconsistency. - Gabriel Ruiz, Oscar Hernan Madrid Padilla, Qing Zhou:
Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores. - Baijiong Lin, Feiyang Ye, Yu Zhang, Ivor W. Tsang:
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning. - Eric Zhan, Jennifer J. Sun, Ann Kennedy, Yisong Yue, Swarat Chaudhuri:
Unsupervised Learning of Neurosymbolic Encoders. - François Charton:
Linear algebra with transformers. - Luca Weihs, Amanda Rose Yuile, Renée Baillargeon, Cynthia Fisher, Gary Marcus, Roozbeh Mottaghi, Aniruddha Kembhavi:
Benchmarking Progress to Infant-Level Physical Reasoning in AI. - Mete Kemertas, Allan Douglas Jepson:
Approximate Policy Iteration with Bisimulation Metrics. - Le Cong Dinh, Stephen Marcus McAleer, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Jun Wang, Haitham Bou-Ammar, Yaodong Yang:
Online Double Oracle. - Alexandre Défossez, Léon Bottou, Francis R. Bach, Nicolas Usunier:
A Simple Convergence Proof of Adam and Adagrad. - Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. - Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li:
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. - Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister:
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling. - Philipp Becker, Gerhard Neumann:
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning. - Clément Bonet, Nicolas Courty, François Septier, Lucas Drumetz:
Efficient Gradient Flows in Sliced-Wasserstein Space. - Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Seungryong Kim, Mathieu Salzmann, Sabine Süsstrunk:
Modeling Object Dissimilarity for Deep Saliency Prediction. - Mingxuan Han, Chenglong Ye, Jeff M. Phillips:
Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression. - Marco Virgolin, Solon P. Pissis:
Symbolic Regression is NP-hard. - Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso, Sameena Shah, Vamsi K. Potluru, Sergey M. Plis:
Explicit Group Sparse Projection with Applications to Deep Learning and NMF. - Ryoya Yamasaki:
Unimodal Likelihood Models for Ordinal Data. - Neha Gupta, Jamie Smith, Ben Adlam, Zelda E. Mariet:
Ensembles of Classifiers: a Bias-Variance Perspective. - Junpei Komiyama, Gustavo Malkomes, Bolong Cheng, Michael McCourt:
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization. - Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar:
Faking Interpolation Until You Make It. - Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski:
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks. - Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. - Yongchan Kwon, Tony Ginart, James Zou:
Competition over data: how does data purchase affect users? - Lang Liu, Mahdi Milani Fard, Sen Zhao:
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks. - Zhunxuan Wang, Linyun He, Chunchuan Lyu, Shay B. Cohen:
Nonparametric Learning of Two-Layer ReLU Residual Units. - Kaichen Zhou, Lanqing Hong, Shoukang Hu, Fengwei Zhou, Binxin Ru, Jiashi Feng, Zhenguo Li:
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture. - Yixi Xu, Sumit Mukherjee, Xiyang Liu, Shruti Tople, Rahul Dodhia, Juan M. Lavista Ferres:
Mace: A flexible framework for membership privacy estimation in generative models. - Klas Leino, Chi Zhang, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina S. Pasareanu:
Degradation Attacks on Certifiably Robust Neural Networks. - Adam Breitholtz, Fredrik D. Johansson:
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks. - Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das:
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. - Haibo Qiu, Baosheng Yu, Dacheng Tao:
GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation. - Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri:
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning. - Alberto Caron, Ioanna Manolopoulou, Gianluca Baio:
Counterfactual Learning with Multioutput Deep Kernels. - Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou:
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges. - Francisco Eiras, Motasem Alfarra, Philip H. S. Torr, M. Pawan Kumar, Puneet K. Dokania, Bernard Ghanem, Adel Bibi:
ANCER: Anisotropic Certification via Sample-wise Volume Maximization. - Manoj Kumar, Neil Houlsby, Nal Kalchbrenner, Ekin Dogus Cubuk:
Do better ImageNet classifiers assess perceptual similarity better? - Jiawei Zhao, Florian Schäfer, Anima Anandkumar:
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones. - Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan:
Nonstationary Reinforcement Learning with Linear Function Approximation. - Nicolas Gast, Bruno Gaujal, Kimang Khun:
Learning Algorithms for Markovian Bandits:\\Is Posterior Sampling more Scalable than Optimism? - Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. - Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, Yonghui Wu:
CoCa: Contrastive Captioners are Image-Text Foundation Models. - Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang:
Can You Win Everything with A Lottery Ticket? - Mohammad Ali Alomrani, Reza Moravej, Elias Boutros Khalil:
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. - Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs:
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. - Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu:
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. - Xiaohui Chen, Xi Chen, Li-Ping Liu:
Interpretable Node Representation with Attribute Decoding. - Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar:
Lookback for Learning to Branch. - Bipasha Sen, Aditya Agarwal, Vinay P. Namboodiri, C. V. Jawahar:
INR-V: A Continuous Representation Space for Video-based Generative Tasks. - Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus:
Emergent Abilities of Large Language Models. - Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi:
Approximating 1-Wasserstein Distance with Trees. - Zhijie Wu, Chunjin Song, Guanxiong Chen, Sheng Guo, Weilin Huang:
Completeness and Coherence Learning for Fast Arbitrary Style Transfer. - Alexandre Défossez, Yossi Adi, Gabriel Synnaeve:
Differentiable Model Compression via Pseudo Quantization Noise. - Philipp Teutsch, Patrick Mäder:
Flipped Classroom: Effective Teaching for Time Series Forecasting. - Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. - James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski, Ben Glocker:
Structured Uncertainty in the Observation Space of Variational Autoencoders. - Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu:
Direct Molecular Conformation Generation. - Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil:
Multitask Online Mirror Descent. - Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown:
Bayesian Methods for Constraint Inference in Reinforcement Learning. - Chengming Xu, Siqian Yang, Yabiao Wang, Zhanxiong Wang, Yanwei Fu, Xiangyang Xue:
Exploring Efficient Few-shot Adaptation for Vision Transformers. - James Morrill, Patrick Kidger, Lingyi Yang, Terry J. Lyons:
On the Choice of Interpolation Scheme for Neural CDEs. - Chandramouli Shama Sastry, Andreas M. Lehrmann, Marcus A. Brubaker, Alexander Radovic:
Efficient CDF Approximations for Normalizing Flows. - Jeremy Vonderfecht, Feng Liu:
Fingerprints of Super Resolution Networks. - Stephanie Lin, Jacob Hilton, Owain Evans:
Teaching Models to Express Their Uncertainty in Words. - Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. - Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker:
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed. - Ilyass Hammouamri, Timothée Masquelier, Dennis George Wilson:
Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation. - Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei:
Identifiable Deep Generative Models via Sparse Decoding.