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ORCIDTransactions on Machine Learning Research, Volume 2024
Volume 2024, 2024
- Melissa Hall, Candace Ross, Adina Williams, Nicolas Carion, Michal Drozdzal, Adriana Romero-Soriano:
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity. - Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham:
Pathologies of Predictive Diversity in Deep Ensembles. - Shehzaad Zuzar Dhuliawala, Mrinmaya Sachan, Carl Allen:
Variational Classification: A Probabilistic Generalization of the Softmax Classifier. - Shihao Liang, Runchu Tian, Kunlun Zhu, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, Maosong Sun:
Exploring Format Consistency for Instruction Tuning. - Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell:
Neural Implicit Manifold Learning for Topology-Aware Density Estimation. - Kazuma Suetake, Takuya Ushimaru, Ryuji Saiin, Yoshihide Sawada:
Synaptic Interaction Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks. - Alexis Teter, Iman Nodozi, Abhishek Halder:
Proximal Mean Field Learning in Shallow Neural Networks. - Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, Yongbin Li:
A Survey on Out-of-Distribution Detection in NLP. - Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates:
DyG2Vec: Efficient Representation Learning for Dynamic Graphs. - Vincent Abbott:
Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures. - Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. - Lorenzo Luzi, Paul M. Mayer, Josue Casco-Rodriguez, Ali Siahkoohi, Richard G. Baraniuk:
Boomerang: Local sampling on image manifolds using diffusion models. - Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski:
DINOv2: Learning Robust Visual Features without Supervision. - Nariman Niknejad, Farnaz Adib Yaghmaie, Hamidreza Modares:
Online Reference Tracking For Linear Systems with Unknown Dynamics and Unknown Disturbances. - Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel:
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions. - Kefan Su, Zongqing Lu:
A Fully Decentralized Surrogate for Multi-Agent Policy Optimization. - Yi Shen, Pan Xu, Michael M. Zavlanos:
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits. - Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. - Saurav Prakash, Jin Sima, Chao Pan, Eli Chien, Olgica Milenkovic:
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls. - Victor-Alexandru Padurean, Georgios Tzannetos, Adish Singla:
Neural Task Synthesis for Visual Programming. - Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht:
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning. - Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio:
AmbientFlow: Invertible generative models from incomplete, noisy measurements. - Shaoyuan Xie, Zichao Li, Zeyu Wang, Cihang Xie:
On the Adversarial Robustness of Camera-based 3D Object Detection. - Jiayu Zhao, Renyu Yang, Shenghao Qiu, Zheng Wang:
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization. - Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer:
Separability Analysis for Causal Discovery in Mixture of DAGs. - Julien Demange-Chryst, François Bachoc, Jérôme Morio, Timothé Krauth:
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling. - David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. - Shayan Mohajer Hamidi, En-Hui Yang:
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction. - Jitao Lu, Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li:
Hyperspherical Prototype Node Clustering. - Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei:
CAREER: A Foundation Model for Labor Sequence Data. - Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar:
Prismer: A Vision-Language Model with Multi-Task Experts. - Steffen Herbold:
Semantic similarity prediction is better than other semantic similarity measures. - Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau:
On the Choice of Learning Rate for Local SGD. - Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar:
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. - Tamara T. Mueller, Sophie Starck, Alina Dima, Stephan Wunderlich, Kyriaki-Margarita Bintsi, Kamilia Zaripova, Rickmer Braren, Daniel Rueckert, Anees Kazi, Georgios Kaissis:
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations. - Meng Liu, Haiyang Yu, Shuiwang Ji:
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm. - Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
Towards fully covariant machine learning. - Guanbo Wang, Mireille Schnitzer, Tom Chen, Rui Wang, Robert W. Platt:
A general framework for formulating structured variable selection. - Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang:
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy. - Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. - Piyushi Manupriya, Saketha Nath Jagarlapudi, Pratik Jawanpuria:
MMD-Regularized Unbalanced Optimal Transport. - Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel:
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. - Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin:
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling. - Hongyang Yu, Hongjiang C. Yu:
TensorVAE: a simple and efficient generative model for conditional molecular conformation generation. - Lukang Sun, Adil Salim, Peter Richtárik:
Federated Sampling with Langevin Algorithm under Isoperimetry. - Philipp Schiele, Eric Luxenberg, Stephen P. Boyd:
Disciplined Saddle Programming. - Tim Chard, Mark Dras, Paul F. Sowman, Steve Cassidy, Jia Wu:
Temporally Rich Deep Learning Models for Magnetoencephalography. - Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun, Stéphane Deny:
Blockwise Self-Supervised Learning at Scale. - Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick:
Are you using test log-likelihood correctly? - Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zhuoxinran Li, Bolei Zhou, Jian Tang:
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled. - Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari, Judy Hoffman, Prithvijit Chattopadhyay, Viraj Prabhu:
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline. - Nathan H. Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho:
Blind Biological Sequence Denoising with Self-Supervised Set Learning. - William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. - Anirbit Mukherjee, Amartya Roy:
Size Lowerbounds for Deep Operator Networks. - Noriyuki Kojima, Hadar Averbuch-Elor, Yoav Artzi:
A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models. - Young-Kyung Kim, J. Matías Di Martino, Guillermo Sapiro:
Generalizing Neural Additive Models via Statistical Multimodal Analysis. - Jun Yu, Zhaoming Kong, Kun Chen, Xin Zhang, Yong Chen, Lifang He:
A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis. - Zhou Fan, Xinran Han, Zi Wang:
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces. - Spyros Gidaris, Andrei Bursuc, Oriane Siméoni, Antonín Vobecký, Nikos Komodakis, Matthieu Cord, Patrick Pérez:
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments. - Angelica Chen, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho:
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs. - Harsh Pandey, Amitabha Bagchi, Srikanta J. Bedathur, Arindam Bhattacharya:
Data-Dependent Generalization Bounds for Neural Networks with ReLU. - Brendon Boldt, David R. Mortensen:
A Review of the Applications of Deep Learning-Based Emergent Communication. - Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould:
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder. - Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias Boutros Khalil:
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations. - Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen, Mathieu Salzmann, Pascal Fua:
Using Motion Cues to Supervise Single-frame Body Pose & Shape Estimation in Low Data Regimes. - Tianlin Liu, Jose Antonio Lara Benitez, Florian Faucher, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic:
WaveBench: Benchmarking Data-driven Solvers for Linear Wave Propagation PDEs. - Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen:
Learning to Abstain From Uninformative Data. - Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen:
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning. - Recep Can Yavas, Vincent Y. F. Tan:
Fixed-Budget Best-Arm Identification in Sparse Linear Bandits. - Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. - Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum:
Domain-Generalizable Multiple-Domain Clustering. - Ramnath Kumar, Dheeraj Mysore Nagaraj:
Introspective Experience Replay: Look Back When Surprised. - Ran Wei, Nathan Lambert, Anthony D. McDonald, Alfredo García, Roberto Calandra:
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning. - Tal Daniel, Aviv Tamar:
DDLP: Unsupervised Object-centric Video Prediction with Deep Dynamic Latent Particles. - Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa:
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis. - Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. - Justin Singh Kang, Ramtin Pedarsani, Kannan Ramchandran:
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. - Adit Jain, Vikram Krishnamurthy:
Controlling Federated Learning for Covertness. - Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi, Nicolas G. Hadjiconstantinou:
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation. - Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao:
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? - Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer:
Hierarchical Neural Simulation-Based Inference Over Event Ensembles. - Albert S. Berahas, Lindon Roberts, Fred Roosta:
Non-Uniform Smoothness for Gradient Descent. - Joe Benton, George Deligiannidis, Arnaud Doucet:
Error Bounds for Flow Matching Methods. - Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite:
Leveraging Function Space Aggregation for Federated Learning at Scale. - Ziyu Jiang, Guoqing Zheng, Yu Cheng, Ahmed Hassan Awadallah, Zhangyang Wang:
CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning. - Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, Dacheng Tao:
Visual Prompt Based Personalized Federated Learning. - Chuyang Ke, Jean Honorio:
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns. - Baturay Saglam, Dogan Can Çiçek, Furkan Burak Mutlu, Suleyman S. Kozat:
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach. - Zhongying Deng, Rihuan Ke, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning. - W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Gabriele Allievi:
Models of human preference for learning reward functions. - Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. - Adarsh Barik, Jean Honorio:
Recovering Exact Support in Federated lasso without Optimization. - Sheng Qiao, Yong He, Wenxin Zhou:
Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee. - Matteo Sordello, Niccolò Dalmasso, Hangfeng He, Weijie J. Su:
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic. - Yutaro Yamada, Yihan Bao, Andrew Kyle Lampinen, Jungo Kasai, Ilker Yildirim:
Evaluating Spatial Understanding of Large Language Models. - Zhiwei Zhang, Yuliang Liu:
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation. - Site Bai, Chuyang Ke, Jean Honorio:
On the Dual Problem of Convexified Convolutional Neural Networks. - Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta:
Image Reconstruction via Deep Image Prior Subspaces. - Thomas Markovich:
QDC: Quantum Diffusion Convolution Kernels on Graphs. - Siqi Liu, Andreas Lehrmann:
DynaConF: Dynamic Forecasting of Non-Stationary Time Series. - Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Paul Scemama, Ariel Kapusta:
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning. - Jarrod Haas, William Yolland, Bernhard Rabus:
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization. - Qi Zhao, Qiqi Duan, Bai Yan, Shi Cheng, Yuhui Shi:
Automated Design of Metaheuristic Algorithms: A Survey. - Sonam Gupta, Snehal Singh Tomar, Grigorios Chrysos, Sukhendu Das, Ambasamudram Narayanan Rajagopalan:
PNeRV: A Polynomial Neural Representation for Videos. - Satoshi Hayakawa, Tetsuro Morimura:
Policy Gradient with Kernel Quadrature. - Wanyun Xie, Thomas Pethick, Ali Ramezani-Kebrya, Volkan Cevher:
Mixed Nash for Robust Federated Learning. - Vimal Thilak, Etai Littwin, Shuangfei Zhai, Omid Saremi, Roni Paiss, Joshua M. Susskind:
The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods. - Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya R. Gupta:
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. - Andi Han, Dai Shi, Lequan Lin, Junbin Gao:
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. - Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. - Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, Eric Nyberg:
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers. - Keliang Li, Hong Chang, Shiguang Shan, Xilin Chen:
Enhancing Robustness to Class-Conditional Distribution Shift in Long-Tailed Recognition. - Matthew D. Kvalheim, Eduardo D. Sontag:
Why should autoencoders work? - Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee:
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets. - Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo A. Cecchi, Guillaume Dumas:
Effective Latent Differential Equation Models via Attention and Multiple Shooting. - Linus Aronsson, Morteza Haghir Chehreghani:
Correlation Clustering with Active Learning of Pairwise Similarities. - Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen:
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation. - Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. - Pouya M. Ghari, Yanning Shen:
Budgeted Online Model Selection and Fine-Tuning via Federated Learning. - Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. - Bruno Régaldo-Saint Blancard, Michael Eickenberg:
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures. - Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig:
Multitask Learning Can Improve Worst-Group Outcomes. - Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert:
Are Population Graphs Really as Powerful as Believed?