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ORCIDTransactions on Machine Learning Research, Volume 2023
Volume 2023, 2023
- Robert Schmier, Ullrich Köthe, Christoph-Nikolas Straehle:
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data. - Newton Mwai Kinyanjui, Emil Carlsson, Fredrik D. Johansson:
Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration. - Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alex Dimakis, Peyman Milanfar:
Soft Diffusion: Score Matching with General Corruptions. - Maxime Haddouche, Benjamin Guedj:
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales. - Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood:
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. - Manoj Kumar, Mostafa Dehghani, Neil Houlsby:
Dual PatchNorm. - Tian Yun, Usha Bhalla, Ellie Pavlick, Chen Sun:
Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? - Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar:
A Revenue Function for Comparison-Based Hierarchical Clustering. - Remo Sasso, Matthia Sabatelli, Marco A. Wiering:
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. - Diego A. Velázquez, Pau Rodríguez, Alexandre Lacoste, Issam H. Laradji, F. Xavier Roca, Jordi Gonzàlez:
Explaining Visual Counterfactual Explainers. - Guillaume Morel, Lucas Drumetz, Simon Benaïchouche, Nicolas Courty, François Rousseau:
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows. - Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. - Fahad Sarfraz, Elahe Arani, Bahram Zonooz:
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning. - Barna Pásztor, Andreas Krause, Ilija Bogunovic:
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. - Afonso Eduardo, Michael U. Gutmann:
Bayesian Optimization with Informative Covariance. - Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park:
Pre-trained Perceptual Features Improve Differentially Private Image Generation. - Fabian Schaipp, Robert M. Gower, Michael Ulbrich:
A Stochastic Proximal Polyak Step Size. - Xinran Zhu, Leo Huang, Eric Hans Lee, Cameron Alexander Ibrahim, David Bindel:
Bayesian Transformed Gaussian Processes. - Jacobie Mouton, Rodney Stephen Kroon:
Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders. - Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jérôme Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti:
Data Models for Dataset Drift Controls in Machine Learning With Optical Images. - Rishi Sonthalia, Raj Rao Nadakuditi:
Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role of Training Noise. - Ambar Pal, Jeremias Sulam:
Understanding Noise-Augmented Training for Randomized Smoothing. - Yuhang Li, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda:
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient. - Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke:
A Simulation Environment and Reinforcement Learning Method for Waste Reduction. - Vignesh Kothapalli:
Neural Collapse: A Review on Modelling Principles and Generalization. - Clément Lalanne, Aurélien Garivier, Rémi Gribonval:
On the Statistical Complexity of Estimation and Testing under Privacy Constraints. - Amrit Nagarajan, Anand Raghunathan:
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks. - Zhili Feng, Ezra Winston, J. Zico Kolter:
Monotone deep Boltzmann machines. - Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley A. Fritz, Daniel Felsky, Michael Avidan, Yi-Xin Chen, Christopher Ryan King:
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues. - Asher Trockman, J. Zico Kolter:
Patches Are All You Need? - Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei:
Partition-Based Active Learning for Graph Neural Networks. - Dongyue Li, Huy L. Nguyen, Hongyang Ryan Zhang:
Identification of Negative Transfers in Multitask Learning Using Surrogate Models. - Dennis Wagner, Tobias Michels, Florian C. F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft:
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. - Tiange Luo, Honglak Lee, Justin Johnson:
Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program. - Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
:
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. - Maurits J. R. Bleeker, Andrew Yates, Maarten de Rijke:
Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval. - Zijie Li, Kazem Meidani, Amir Barati Farimani:
Transformer for Partial Differential Equations' Operator Learning. - Dennis Ulmer, Christian Hardmeier, Jes Frellsen:
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation. - Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. - David Kuric, Herke van Hoof:
Reusable Options through Gradient-based Meta Learning. - Oyku Deniz Kose, Yanning Shen:
Fast&Fair: Training Acceleration and Bias Mitigation for GNNs. - Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang:
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. - Pascal Kilian, Sangbeak Ye, Augustin Kelava:
Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression. - Harsh Satija, Alessandro Lazaric, Matteo Pirotta, Joelle Pineau:
Group Fairness in Reinforcement Learning. - Josephine Maria Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara Holzhüter:
Graph Neural Networks Designed for Different Graph Types: A Survey. - Alina Selega, Kieran R. Campbell:
Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows. - Patrick M. Soga, David Chiang:
Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata. - Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. - Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, Hervé Jégou:
Image Compression with Product Quantized Masked Image Modeling. - Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban, Adish Singla:
Proximal Curriculum for Reinforcement Learning Agents. - Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. - Matteo Gamba, Erik Englesson, Mårten Björkman, Hossein Azizpour:
Deep Double Descent via Smooth Interpolation. - Alan Q. Wang, Mert R. Sabuncu:
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification. - Nathan J. Wispinski, Andrew Butcher, Kory Wallace Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski:
Adaptive patch foraging in deep reinforcement learning agents. - Firat Ozdemir, Berkan Lafci, Xosé-Luís Dean-Ben, Daniel Razansky, Fernando Pérez-Cruz:
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing. - Magnus Ross, Markus Heinonen:
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes. - Donlapark Ponnoprat:
Dirichlet Mechanism for Differentially Private KL Divergence Minimization. - Shengyuan Hu, Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. - Yiling Xie, Yiling Luo, Xiaoming Huo:
Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm. - Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. - Matthew Koichi Grimes, Joseph Modayil, Piotr W. Mirowski, Dushyant Rao, Raia Hadsell:
Learning to Look by Self-Prediction. - Yi Heng Lim, Muhammad Firmansyah Kasim:
Unifying physical systems' inductive biases in neural ODE using dynamics constraints. - Damjan Kalajdzievski, Ximeng Mao, Pascal Fortier-Poisson, Guillaume Lajoie, Blake Aaron Richards:
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer. - Axel Böhm:
Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions. - Bahjat Kawar, Roy Ganz, Michael Elad:
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. - Farnaz Adib Yaghmaie, Hamidreza Modares:
Online Optimal Tracking of Linear Systems with Adversarial Disturbances. - Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. - Juan Miguel Lopez Alcaraz, Nils Strodthoff:
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models. - Lucas Prieto, Jeroen den Boef, Paul Groth, Joran Cornelisse:
Parameter Efficient Node Classification on Homophilic Graphs. - David A. Klindt:
Controlling Neural Network Smoothness for Neural Algorithmic Reasoning. - Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer:
MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. - Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky:
Differentially Private Image Classification from Features. - Nasir Ahmad, Ellen Schrader, Marcel van Gerven:
Constrained Parameter Inference as a Principle for Learning. - Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, Bolei Zhou:
ChemSpacE: Interpretable and Interactive Chemical Space Exploration. - Anuj Singh, Hadi Jamali Rad:
Transductive Decoupled Variational Inference for Few-Shot Classification. - Roy Ganz, Michael Elad:
BIGRoC: Boosting Image Generation via a Robust Classifier. - Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin:
Extreme Masking for Learning Instance and Distributed Visual Representations. - Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou:
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization. - Guanlin Liu, Lifeng Lai:
Action Poisoning Attacks on Linear Contextual Bandits. - Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. - Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth:
A Variational Perspective on Generative Flow Networks. - Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. - Zhiliang Peng, Li Dong, Hangbo Bao, Furu Wei, Qixiang Ye:
A Unified View of Masked Image Modeling. - Zhangheng Li, Tianlong Chen, Linyi Li, Bo Li, Zhangyang Wang:
Can Pruning Improve Certified Robustness of Neural Networks? - Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi:
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. - Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. - Xingran Chen, Hesam Nikpey, Jungyeol Kim, Saswati Sarkar, Shirin Saeedi Bidokhti:
Containing a spread through sequential learning: to exploit or to explore? - Ryoya Yamasaki:
Optimal Threshold Labeling for Ordinal Regression Methods. - Shuo Sun, Molei Qin, Xinrun Wang, Bo An:
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets. - Boxin Zhao, Boxiang Lyu, Mladen Kolar:
L-SVRG and L-Katyusha with Adaptive Sampling. - Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. - Simon Wiedemann, Daniel Hein, Steffen Udluft, Christian B. Mendl:
Quantum Policy Iteration via Amplitude Estimation and Grover Search - Towards Quantum Advantage for Reinforcement Learning. - Zibo Liu, Parshin Shojaee, Chandan K. Reddy:
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting. - Matthew James Vowels, Sina Akbari, Necati Cihan Camgöz, Richard Bowden:
A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation. - Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. - Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, Chaim Baskin:
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings. - Yuntao Du, Juan Jiang, Hongtao Luo, Haiyang Yang, Mingcai Chen, Chongjun Wang:
Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation. - Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang:
Black-Box Prompt Learning for Pre-trained Language Models. - Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky:
Towards Large Scale Transfer Learning for Differentially Private Image Classification. - Joy Hsu, Jiayuan Mao, Jiajun Wu:
DisCo: Improving Compositional Generalization in Visual Reasoning through Distribution Coverage. - Bartlomiej Polaczyk, Jacek Cyranka:
Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks. - Yao-Yuan Yang, Cyrus Rashtchian, Ruslan Salakhutdinov, Kamalika Chaudhuri:
Probing Predictions on OOD Images via Nearest Categories. - Marissa Catherine Connor, Kion Fallah, Christopher John Rozell:
Learning Identity-Preserving Transformations on Data Manifolds. - Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi:
FLUID: A Unified Evaluation Framework for Flexible Sequential Data. - Leo Kozachkov, Patrick M. Wensing, Jean-Jacques E. Slotine:
Generalization as Dynamical Robustness-The Role of Riemannian Contraction in Supervised Learning. - Zhen Xu, Quanming Yao, Yong Li, Qiang Yang:
Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks. - Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman:
Clustering using Approximate Nearest Neighbour Oracles. - Enayat Ullah, Raman Arora:
Generalization bounds for Kernel Canonical Correlation Analysis. - Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. - Yixuan Su, Nigel Collier:
Contrastive Search Is What You Need For Neural Text Generation. - Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor:
Learning Representations for Pixel-based Control: What Matters and Why? - Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. - Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. - Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu:
Proportional Fairness in Federated Learning. - Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu:
Scalable Deep Compressive Sensing. - Tony Tohme, Dehong Liu, Kamal Youcef-Toumi:
GSR: A Generalized Symbolic Regression Approach. - Andrea Schioppa, Nal Kalchbrenner:
Stacking Diverse Architectures to Improve Machine Translation. - Zhiying Fang, Guang Cheng:
Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. - Amine El Hattami, Issam H. Laradji, Stefania Raimondo, David Vázquez, Pau Rodríguez, Christopher Pal:
Workflow Discovery from Dialogues in the Low Data Regime. - Sean Gunn, Jorio Cocola, Paul Hand:
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems. - Qijun Luo, Xiao Li:
Finite-Time Analysis of Decentralized Single-Timescale Actor-Critic. - Jonas Gehring, Deepak Gopinath, Jungdam Won, Andreas Krause, Gabriel Synnaeve, Nicolas Usunier:
Leveraging Demonstrations with Latent Space Priors. - Jiamin Chen, Xuhong Li, Lei Yu, Dejing Dou, Haoyi Xiong:
Beyond Intuition: Rethinking Token Attributions inside Transformers. - Vijaya Raghavan T. Ramkumar, Elahe Arani, Bahram Zonooz:
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks. - Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. - Saiteja Utpala, Praneeth Vepakomma, Nina Miolane:
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. - Jing Wu, David Pichler, Daniel Marley, Naira Hovakimyan, David Wilson, Jennifer A. Hobbs:
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis. - Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio:
DEUP: Direct Epistemic Uncertainty Prediction. - Serge Assaad, Carlton Downey, Rami Al-Rfou', Nigamaa Nayakanti, Benjamin Sapp:
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons. - Pradeep Kumar Jayaraman, Joseph George Lambourne, Nishkrit Desai, Karl D. D. Willis, Aditya Sanghi, Nigel J. W. Morris:
SolidGen: An Autoregressive Model for Direct B-rep Synthesis. - Jireh Huang, Qing Zhou:
Bayesian Causal Bandits with Backdoor Adjustment Prior. - Mona Buisson-Fenet, Valéry Morgenthaler, Sebastian Trimpe, Florent Di Meglio:
Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs. - Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider:
Intrinsic Dimension for Large-Scale Geometric Learning.