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ORCIDTransactions on Machine Learning Research, Volume 2025
Volume 2025, 2025
- Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry:
Ask Your Distribution Shift if Pre-Training is Right for You. - Shubhankar Gupta, Saksham Sharma, Suresh Sundaram:
Reward-based Autonomous Online Learning Framework for Resilient Cooperative Target Monitoring using a Swarm of Robots. - Wenhao Lu, Xufeng Zhao, Josua Spisak, Jae Hee Lee, Stefan Wermter:
Mental Modelling of Reinforcement Learning Agents by Language Models. - Debarshi Brahma, Anuska Roy, Soma Biswas:
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts. - Myeongho Jeon, Suhwan Choi, Hyoje Lee, Teresa Yeo:
An Analysis of Model Robustness across Concurrent Distribution Shifts. - Madison Cooley, Varun Shankar, Mike Kirby, Shandian Zhe:
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases. - Weijian Luo:
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences. - David Chiang:
Transformers in Uniform TC0. - Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu:
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding. - Yuan Zang, Tian Yun, Hao Tan, Trung Bui, Chen Sun:
Pre-trained Vision-Language Models Learn Discoverable Visual Concepts. - Peihong Yu, Manav Mishra, Alec Koppel, Carl E. Busart, Priya Narayan, Dinesh Manocha, Amrit Singh Bedi, Pratap Tokekar:
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning. - Tim Z. Xiao, Johannes Zenn, Robert Bamler:
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? - Dominik Fay, Sebastian Mair, Jens Sjölund:
Personalized Privacy Amplification via Importance Sampling. - Alexander Larionov, Niall M. Adams, Kevin N. Webster:
Investigating the impact of missing value handling on Boosted trees and Deep learning for Tabular data: A Claim Reserving case study. - Franka Bause, Fabian Jogl, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen:
Maximally Expressive GNNs for Outerplanar Graphs. - Shruti P. Maralappanavar, Prashant Khanduri, B. N. Bharath:
Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime. - Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael Ng, Zhenguo Li, Zhaoqiang Liu:
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures. - Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun:
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models. - Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai:
Improving GFlowNets for Text-to-Image Diffusion Alignment. - Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P. Dickerson, Pin-Yu Chen, Jeff Bilmes:
Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective. - Manu Gaur, Darshan Singh S, Makarand Tapaswi:
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning. - Miles Everett, Mingjun Zhong, Georgios Leontidis:
Masked Capsule Autoencoders. - Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra, Biswajit Basu:
Sparse Neural Architectures via Deterministic Ramanujan Graphs. - Chloe Loughridge, Qinyi Sun, Seth Ahrenbach, Federico Cassano, Chuyue Sun, Ying Sheng, Anish Mudide, Md Rakib Hossain Misu, Nada Amin, Max Tegmark:
DafnyBench: A Benchmark for Formal Software Verification. - Clément Bonet, Kimia Nadjahi, Thibault Séjourné, Kilian Fatras, Nicolas Courty:
Slicing Unbalanced Optimal Transport. - Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy:
On Inherent Adversarial Robustness of Active Vision Systems. - Marco Paul E. Apolinario, Kaushik Roy:
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks. - Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci:
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers. - Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao:
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. - Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya, Debdeep Pati:
Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on [0, 1]d under Wasserstein Distance. - Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar:
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models. - Bingxin Zhou, Outongyi Lv, Jing Wang, Xiang Xiao, Weishu Zhao:
ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs. - Seth Neel:
PRIMO: Private Regression in Multiple Outcomes. - Tobias Fuchs, Florian Kalinke, Klemens Böhm:
Partial-Label Learning with a Reject Option. - Stefano Peluchetti:
BM2: Coupled Schrödinger Bridge Matching. - Vidhi Lalchand, Anna-Christina Eilers:
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra. - Pedro Cisneros-Velarde, Zhijie Chen, Sanmi Koyejo, Arindam Banerjee:
Optimization and Generalization Guarantees for Weight Normalization. - Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac:
Optimal Transport for Domain Adaptation through Gaussian Mixture Models. - Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht, Javen Qinfeng Shi:
Highway Graph to Accelerate Reinforcement Learning. - Saeideh Ghanbari Azar, Lorenzo Tronchin, Attila Simkó, Tufve Nyholm, Tommy Löfstedt:
From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. - Arman Rahbar, Niklas Åkerblom, Morteza Haghir Chehreghani:
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach. - Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, Yuriy Nevmyvaka:
Reweighting Improves Conditional Risk Bounds. - Lei Zhao, Lin Cai, Wu-Sheng Lu:
Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction. - Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön:
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations. - Marc T. Law, Karsten Kreis, Haggai Maron:
Directed Graph Generation with Heat Kernels. - Shuai Zhao, Meihuizi Jia, Zhongliang Guo, Leilei Gan, Xiaoyu Xu, Xiaobao Wu, Jie Fu, Yichao Feng, Fengjun Pan, Anh Tuan Luu:
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models. - Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot:
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes. - Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park:
Decoupled Sequence and Structure Generation for Realistic Antibody Design. - Nicolas Boizard, Kevin El Haddad, Céline Hudelot, Pierre Colombo:
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs. - Adarsh Kappiyath, Anmol Garg, Ramya Hebbalaguppe, Prathosh AP:
Lifelong Learning in StyleGAN through Latent Subspaces. - Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari:
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval. - Alejandro Guerra-Manzanares, Farah Shamout:
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks. - Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu:
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. - Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart:
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. - Subba Reddy Oota, Zijiao Chen, Manish Gupta, Bapi Raju Surampudi, Gaël Jobard, Frédéric Alexandre, Xavier Hinaut:
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey). - Eugene A. Golikov:
A Generalization Bound for Nearly-Linear Networks. - Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda, Narges Razavian:
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling. - Hiroyuki Sakai, Hideaki Iiduka:
A general framework of Riemannian adaptive optimization methods with a convergence analysis. - Tal Reiss, Yedid Hoshen:
An Attribute-based Method for Video Anomaly Detection. - Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, Xiaoxiao Li:
Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation. - Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth:
Optimization Dynamics of Equivariant and Augmented Neural Networks. - Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe:
Event-Triggered Time-Varying Bayesian Optimization. - Thomas Pethick, Parameswaran Raman, Lenon Minorics, Mingyi Hong, Shoham Sabach, Volkan Cevher:
νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints. - Netta Ollikka, Amro Abbas, Andrea Perin, Markku Kilpeläinen, Stéphane Deny:
A comparison between humans and AI at recognizing objects in unusual poses. - David Mueller, Mark Dredze, Nicholas Andrews:
Can Optimization Trajectories Explain Multi-Task Transfer? - Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Global Safe Sequential Learning via Efficient Knowledge Transfer. - Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire:
An analysis of the noise schedule for score-based generative models. - Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
On the Properties and Estimation of Pointwise Mutual Information Profiles. - Luciana Ferrer, Daniel Ramos:
Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration. - Koki Okajima, Tomoyuki Obuchi:
Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis. - Ruisu Zhang, Yicong Chen, Kangwook Lee:
Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning. - Chuanhui Liu, Xiao Wang:
Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach. - Luca Simi:
A Scalable Approach for Mapper via Efficient Spatial Search. - Alexey Kravets, Vinay P. Namboodiri:
Zero-shot CLIP Class Forgetting via Text-image Space Adaptation. - Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim:
Fairness Through Matching. - Sai Saketh Rambhatla, Ishan Misra:
SelfEval: Leveraging discriminative nature of generative models for evaluation. - Zhiyu Guo, Hidetaka Kamigaito, Taro Watanabe:
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models. - Prithviraj Tarale, Edward A. Rietman, Hava T. Siegelmann:
Distributed Multi-Agent Lifelong Learning. - Yu Wang, Chi Han, Tongtong Wu, Xiaoxin He, Wangchunshu Zhou, Nafis Sadeq, Xiusi Chen, Zexue He, Wei Wang, Gholamreza Haffari, Heng Ji, Julian J. McAuley:
Towards LifeSpan Cognitive Systems. - Zhuoran Yu, Chenchen Zhu, Sean Culatana, Raghuraman Krishnamoorthi, Fanyi Xiao, Yong Jae Lee:
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images. - Tanguy Bosser, Souhaib Ben Taieb:
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes. - Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE. - Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. - Dun Zeng, Zenglin Xu, Yu Pan, Xu Luo, Qifan Wang, Xiaoying Tang:
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance. - Riccardo Majellaro, Jonathan Collu, Aske Plaat, Thomas M. Moerland:
Explicitly Disentangled Representations in Object-Centric Learning. - Nicholas Krämer:
Numerically Robust Fixed-Point Smoothing Without State Augmentation. - Joseph Paul Cohen, Louis Blankemeier, Akshay S. Chaudhari:
Identifying Spurious Correlations using Counterfactual Alignment. - Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian:
Semantic Alignment for Prompt-Tuning in Vision Language Models. - Georgios Vlassis, David Belius, Volodymyr Fomichov:
A thorough reproduction and evaluation of µP. - Zhuo Zhi, Yuxuan Sun, Qiangqiang Wu, Ziquan Liu, Miguel R. D. Rodrigues:
Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust. - Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen:
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance. - Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi:
Can AI-Generated Text be Reliably Detected? Stress Testing AI Text Detectors Under Various Attacks. - Nauman Ahad, Mark A. Davenport, Eva L. Dyer:
Time Series Domain Adaptation via Channel-Selective Representation Alignment. - Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla:
ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection. - Meher Chaitanya, Kshitijaa Jaglan, Ulrik Brandes:
Adjacency Search Embeddings. - Michal Derezinski:
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches. - Bolian Li, Ruqi Zhang:
Making Reliable and Flexible Decisions in Long-tailed Classification. - Georgios Sidiropoulos, Samarth Bhargav, Panagiotis Eustratiadis, Evangelos Kanoulas:
Multivariate Dense Retrieval: A Reproducibility Study under a Memory-limited Setup. - Shayan Mohajer Hamidi, Linfeng Ye:
Distributed Quasi-Newton Method for Fair and Fast Federated Learning. - Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, Xavier Boix:
In-distribution adversarial attacks on object recognition models using gradient-free search. - Hongyi Ling, Zhimeng Jiang, Na Zou, Shuiwang Ji:
Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability. - Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya, Marc T. Law:
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhari, Huzefa Rangwala, George Karypis, Rasool Fakoor:
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens. - Jieru Mei, Liang-Chieh Chen, Alan L. Yuille, Cihang Xie:
SPFormer: Enhancing Vision Transformer with Superpixel Representation. - Yuzhu Mao, Zihao Zhao, Siqi Ping, Yang Liu, Wenbo Ding:
Enhancing Parameter Efficiency and Generalization in Large Models: A Regularized and Masked Low-Rank Adaptation Approach. - Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, Steffen Staab:
Explanation Shift: How Did the Distribution Shift Impact the Model? - Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer G. Dy:
ADAPT to Robustify Prompt Tuning Vision Transformers. - Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal:
Private Fine-tuning of Large Language Models with Zeroth-order Optimization. - Chao Zhou, Huishuai Zhang, Jiang Bian, Weiming Zhang, Nenghai Yu:
\copyright Plug-in Authorization for Human Copyright Protection in Text-to-Image Model. - Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik:
Interactive Task Planning with Language Models. - Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas Girdzijauskas:
On the effects of similarity metrics in decentralized deep learning under distribution shift. - Gabriel Dubé, Mario Marchand:
Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions. - Florian Kalinke, Marco Heyden, Georg Gntuni, Edouard Fouché, Klemens Böhm:
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection. - Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà:
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions. - Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek:
Cooperative Minibatching in Graph Neural Networks. - Nicholas Bai, Rahul A. Iyer, Tuomas P. Oikarinen, Akshay R. Kulkarni, Tsui-Wei Weng:
Interpreting Neurons in Deep Vision Networks with Language Models. - Noureddine Henka, Mohamad Assaad, Sami Tazi:
Mixture Degree-Corrected Stochastic Block Model for Multi-Group Community Detection in Multiplex Graphs. - Sebastian Wankerl, Jan Pfister, Andrzej Dulny, Gerhard Götz, Andreas Hotho:
Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks. - Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth:
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi:
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model. - Michele Caprio, David Stutz, Shuo Li, Arnaud Doucet:
Conformalized Credal Regions for Classification with Ambiguous Ground Truth. - Geri Skenderi, Hang Li, Jiliang Tang, Marco Cristani:
Graph-level Representation Learning with Joint-Embedding Predictive Architectures. - Zidan Wang, Rui Shen, Bradly C. Stadie:
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs. - Bas van der Heijden, Jens Kober, Robert Babuska, Laura Ferranti:
REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation. - Travis E. Gibson, Sawal Acharya, Anjali Parashar, Joseph E. Gaudio, Anuradha Annaswamy:
On the stability of gradient descent with second order dynamics for time-varying cost functions. - Motasem Alfarra, Alvaro H. C. Correia, Bernard Ghanem, Christos Louizos:
Test-Time Adaptation with Source Based Auxiliary Tasks. - Saptarshi Chakraborty:
Minimax Lower Bounds for Estimating Distributions on Low-dimensional Spaces. - Saleh Gholam Zadeh, Vaisakh Shaj, Patrick Jahnke, Gerhard Neumann, Tim Breitenbach:
Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification. - Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo:
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning. - Chandramouli Shama Sastry, Mahdi Gilany, Kry Yik-Chau Lui, Martin Magill, Alexander Pashevich:
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis. - Jackson Petty, Sjoerd van Steenkiste, Tal Linzen:
How Does Code Pretraining Affect Language Model Task Performance? - Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot:
Selective Prediction via Training Dynamics. - Arash Behboodi, Gabriele Cesa:
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing. - Minguk Jang, Hye Won Chung:
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation. - Hikari Otsuka, Daiki Chijiwa, Ángel López García-Arias, Yasuyuki Okoshi, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Susumu Takeuchi, Masato Motomura:
Partially Frozen Random Networks Contain Compact Strong Lottery Tickets. - Jiazheng Li, Jundong Li, Chuxu Zhang:
Instance-Aware Graph Prompt Learning. - Savvas Melidonis, Yiming Xi, Konstantinos C. Zygalakis, Yoann Altmann, Marcelo Pereyra:
Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems. - Haonan Wang, Qian Liu, Chao Du, Tongyao Zhu, Cunxiao Du, Kenji Kawaguchi, Tianyu Pang:
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training. - Pengyun Wang, Yadi Cao, Chris Russell, Yanxin Shen, Junyu Luo, Ming Zhang, Siyu Heng, Xiao Luo:
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation. - Ibrahim Serouis, Florence Sèdes:
Towards context and domain-aware algorithms for scene analysis. - Luca Butera, Giovanni de Felice, Andrea Cini, Cesare Alippi:
On the Regularization of Learnable Embeddings for Time Series Forecasting. - Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li:
LLaVA-OneVision: Easy Visual Task Transfer. - Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann:
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration. - Liran Nochumsohn, Omri Azencot:
Data Augmentation Policy Search for Long-Term Forecasting. - Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang:
Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks. - Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag:
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Anna Hedström, Philine Lou Bommer, Thomas F. Burns, Sebastian Lapuschkin, Wojciech Samek, Marina M.-C. Höhne:
Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. - Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi:
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach. - Neil Ashtekar, Jingxi Zhu, Vasant G. Honavar:
Class Incremental Learning from First Principles: A Review. - Aymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Adrian Iacovelli, David Naccache:
CNN Interpretability with Multivector Tucker Saliency Maps for Self-Supervised Models. - Thibault de Surrel, Sylvain Chevallier, Fabien Lotte, Florian Yger:
Geometry-Aware visualization of high dimensional Symmetric Positive Definite matrices. - Sharmita Dey, Benjamin Paassen, Sarath Ravindran Nair, Sabri Boughorbel, Arndt F. Schilling:
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling. - Ali Shirali, Moritz Hardt:
What Makes ImageNet Look Unlike LAION. - Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal:
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors. - Antonios Valkanas, Yuening Wang, Yingxue Zhang, Mark Coates:
Personalized Negative Reservoir for Incremental Learning in Recommender Systems. - Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari:
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? - Cullen Anderson, Jeff M. Phillips:
Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study. - Viraj Shah, Svetlana Lazebnik, Julien Philip:
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition. - Hikaru Umeda, Hideaki Iiduka:
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. - Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernárdez, Olga Zaghen, Simone Scardapane, Pietro Lio:
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design. - Yancheng Wang, Changyu Liu, Yingzhen Yang:
Diffusion on Graph: Augmentation of Graph Structure for Node Classification. - Haoyun Yin, Yixuan Qiu, Xiao Wang:
Wasserstein Coreset via Sinkhorn Loss. - Mahrokh Ghoddousi Boroujeni, Andreas Krause, Giancarlo Ferrari-Trecate:
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach. - Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Irina Rish, Pierre-Luc Bacon, Razvan Pascanu, Aristide Baratin:
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. - Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Necessary and Sufficient Watermark for Large Language Models. - Sara Venturini, Marianna De Santis, Jordan Patracone, Martin Schmidt, Francesco Rinaldi, Saverio Salzo:
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization. - Stefano Bruno, Ying Zhang, Dongyoung Lim, Ömer Deniz Akyildiz, Sotirios Sabanis:
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates. - Joel Jonsson, Bevan Leslie Cheeseman, Ivo F. Sbalzarini:
APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images. - Eric Tang, Bangding Yang, Xingyou Song:
Understanding LLM Embeddings for Regression. - Rundong Luo, Hong-Xing Yu, Jiajun Wu:
Unsupervised Discovery of Object-Centric Neural Fields. - Yuki Ichihara, Yuu Jinnai, Tetsuro Morimura, Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Eiji Uchibe:
Evaluation of Best-of-N Sampling Strategies for Language Model Alignment. - Harsh Raj, Vipul Gupta, Domenic Rosati, Subhabrata Majumdar:
Improving Consistency in Large Language Models through Chain of Guidance. - Michal Lewandowski, Hamid Eghbalzadeh, Bernhard Heinzl, Raphael Pisoni, Bernhard Alois Moser:
On Space Folds of ReLU Neural Networks. - Astrit Tola, Jack Myrick, Baris Coskunuzer:
PROXI: Challenging the GNNs for Link Prediction. - Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed:
A Strong Baseline for Molecular Few-Shot Learning. - Xiangru Jian, Xinjian Zhao, Wei Pang, Chaolong Ying, Yimu Wang, Yaoyao Xu, Tianshu Yu:
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning. - Margherita Mele, Roberto Menichetti, Alessandro Ingrosso, Raffaello Potestio:
Density of states in neural networks: an in-depth exploration of learning in parameter space. - Peter Shaw, James Cohan, Jacob Eisenstein, Kenton Lee, Jonathan Berant, Kristina Toutanova:
ALTA: Compiler-Based Analysis of Transformers. - David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach, Sham M. Kakade:
Loss-to-Loss Prediction: Scaling Laws for All Datasets. - Fang Wu, Stan Z. Li:
Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling. - Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang:
The 2023 Foundation Model Transparency Index. - Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan L. Yuille, Cihang Xie:
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning. - Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke:
Using representation balancing to learn conditional-average dose responses from clustered data. - Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu, Yezhou Yang:
Latent Space Energy-based Neural ODEs. - Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability. - Francois Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang:
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning. - Weizhi Lu, Zhongzheng Li, Mingrui Chen, Weiyu Li:
The Sparse Matrix-Based Random Projection: A Study of Binary and Ternary Quantization. - Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang:
On Memorization in Diffusion Models. - Piyush Tiwary, Atri Guha, Subhodip Panda, Prathosh AP:
Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks. - Kieran A. Murphy, Sam Dillavou, Danielle S. Bassett:
Comparing the information content of probabilistic representation spaces. - Martha Lewis, Melanie Mitchell:
Evaluating the Robustness of Analogical Reasoning in Large Language Models. - Eliav Mor, Yair Carmon:
An Analytical Model for Overparameterized Learning Under Class Imbalance. - Song Wang, Zhen Tan, Yaochen Zhu, Chuxu Zhang, Jundong Li:
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs. - Rudi Coppola, Manuel Mazo Espinosa:
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals. - Leyla Naz Candogan, Yongtao Wu, Elías Abad-Rocamora, Grigorios Chrysos, Volkan Cevher:
Single-pass Detection of Jailbreaking Input in Large Language Models. - Amer Essakine, Yanqi Cheng, Chun-Wun Cheng, Lipei Zhang, Zhongying Deng, Lei Zhu, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey. - Haoyu Wang, Guozheng Ma, Cong Yu, Ning Gui, Linrui Zhang, Zhiqi Huang, Suwei Ma, Yongzhe Chang, Sen Zhang, Li Shen, Xueqian Wang, Peilin Zhao, Dacheng Tao:
Are Large Language Models Really Robust to Word-Level Perturbations? - Julius Ott, Huawei Sun, Enrico Rinaldi, Gianfranco Mauro, Lorenzo Servadei, Robert Wille:
Exploiting Benford's Law for Weight Regularization of Deep Neural Networks. - Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro:
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging. - Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek:
SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation. - Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal, Ahmad Beirami, Chirag Nagpal, Peter Shaw, Jonathan Berant:
Robust Preference Optimization through Reward Model Distillation. - Adrian Remonda, Cole Corbitt Terrell, Eduardo E. Veas, Marc Masana:
Uncertainty-Based Experience Replay for Task-Agnostic Continual Reinforcement Learning. - Shachar Schnapp, Sivan Sabato:
Differentially Private Source-Target Clustering. - Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä:
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature. - Cristina Garbacea, Qiaozhu Mei:
Why is constrained neural language generation particularly challenging? - Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal:
Counterfactual Learning of Stochastic Policies with Continuous Actions. - Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu:
Verbalized Machine Learning: Revisiting Machine Learning with Language Models. - Olivier Teytaud, Mariia Zameshina, Tom Sander, Pierre Fernandez, Furong Ye, Laurent Najman, Thomas Bäck, Ismail Labiad:
Lognormal Mutations and their Use in Detecting Surreptitious Fake Images. - Omer Rochman Sharabi, Sacha Lewin, Gilles Louppe:
A Neural Material Point Method for Particle-based Emulation. - Zeyu Yang, Han Yu, Peikun Guo, Khadija Zanna, Xiaoxue Yang, Akane Sano:
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models. - Krishna Acharya, Juba Ziani, Jingyan Wang, Varun Vangala:
Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems. - Prabhu Babu, Petre Stoica, Astha Saini:
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA. - Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan:
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. - Zach Nussbaum, John Xavier Morris, Andriy Mulyar, Brandon Duderstadt:
Nomic Embed: Training a Reproducible Long Context Text Embedder. - Lan V. Truong:
Global Convergence Rate of Deep Equilibrium Models with General Activations. - Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh:
TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. - Haozhe Liu, Wentian Zhang, Jinheng Xie, Francesco Faccio, Mengmeng Xu, Tao Xiang, Mike Zheng Shou, Juan-Manuel Pérez-Rúa, Jürgen Schmidhuber:
Faster Diffusion Through Temporal Attention Decomposition. - Nikita Malik, Konda Reddy Mopuri:
FaAlGrad: Fairness through Alignment of Gradients across Different Subpopulations. - Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng:
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization. - Minttu Alakuijala, Reginald McLean, Isaac Woungang, Nariman Farsad, Samuel Kaski, Pekka Marttinen, Kai Yuan:
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. - Zhong Chuang, Yusuke Tanaka, Tomoharu Iwata:
Meta-Learning for Graphs with Heterogeneous Node Attribute Spaces for Few-Shot Edge Predictions. - Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Metalearning Continual Learning Algorithms. - Zijun Wang, Haoqin Tu, Jieru Mei, Bingchen Zhao, Yisen Wang, Cihang Xie:
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation. - Seyed Moslem Shokrolahi, Il-Min Kim:
Combating Inter-Task Confusion and Catastrophic Forgetting by Metric Learning and Re-Using a Past Trained Model. - Yilun Kong, Hangyu Mao, Qi Zhao, Bin Zhang, Jingqing Ruan, Li Shen, Yongzhe Chang, Xueqian Wang, Rui Zhao, Dacheng Tao:
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning. - Amadou S. Sangare, Nicolas Dunou, Jhony H. Giraldo, Fragkiskos D. Malliaros:
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning. - Tian Xie, Jifan Zhang, Haoyue Bai, Robert D. Nowak:
Deep Active Learning in the Open World. - Roozbeh Yousefzadeh, Xuenan Cao:
A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems. - Mohsen Tabejamaat, Farzaneh Etminani, Mattias Ohlsson:
Cycle Conditioning for Robust Representation Learning from Categorical Data. - Isay Katsman, Anna Gilbert:
Shedding Light on Problems with Hyperbolic Graph Learning. - Weiguo Gao, Ming Li:
Evolution of Discriminator and Generator Gradients in GAN Training: From Fitting to Collapse. - Jiacheng You, Xinyang Chen, Yu Sun, Weili Guan, Liqiang Nie:
Long Short-Term Imputer: Handling Consecutive Missing Values in Time Series. - Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
A Self-Explainable Heterogeneous GNN for Relational Deep Learning. - Hejia Geng, Peng Li:
HoSNNs: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds. - Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. - Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat, Edward S. Hu:
Reset-free Reinforcement Learning with World Models. - Sebastian Gregor Gruber, Francis R. Bach:
Optimizing Estimators of Squared Calibration Errors in Classification. - Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche, Mario Fritz:
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators. - Pavel Rumiantsev, Mark Coates:
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation. - Xingmei Lou, Yu Hu, Xiaodong Li:
Learning Linear Polytree Structural Equation Model. - Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine Knauer-Arloth, Annalisa Marsico:
Random Walk Diffusion for Efficient Large-Scale Graph Generation. - Kelly Ramsay, Aukosh Jagannath, Shoja'eddin Chenouri:
An elementary concentration bound for Gibbs measures arising in statistical learning theory. - Giuseppe Serra, Ben Werner, Florian Buettner:
How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning. - Rishi Bommasani, Kevin Klyman, Sayash Kapoor, Shayne Longpre, Betty Xiong, Nestor Maslej, Percy Liang:
The 2024 Foundation Model Transparency Index. - Shenghong Dai, Jy-yong Sohn, Yicong Chen, S. M. Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, Kangwook Lee:
Buffer-based Gradient Projection for Continual Federated Learning. - Roman Bresson, Giannis Nikolentzos, George Panagopoulos, Michail Chatzianastasis, Jun Pang, Michalis Vazirgiannis:
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning. - Daisuke Hatano, Satoshi Hara, Hiromi Arai:
Path-Specific Counterfactual Fairness via Dividend Correction. - Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong, Zhihan Liu, Ziyi Yang, Shuohang Wang, Hany Hassan Awadalla, Zhaoran Wang:
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment. - Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Milad Cheraghalikhani, Gustavo Adolfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers:
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D. - Qinxun Bai, Steven Rosenberg, Wei Xu:
Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient. - Luciana Ferrer:
No Need for Ad-hoc Substitutes: The Expected Cost is a Principled All-purpose Classification Metric. - Phuong Quynh Le, Jörg Schlötterer, Christin Seifert:
Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations. - Vincent Abbott, Gioele Zardini:
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness. - Thomas De Min, Massimiliano Mancini, Stéphane Lathuilière, Subhankar Roy, Elisa Ricci:
Unlearning Personal Data from a Single Image. - Billy Joe Franks, Moshe Eliasof, Semih Cantürk, Guy Wolf, Carola-Bibiane Schönlieb, Sophie Fellenz, Marius Kloft:
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings. - Vinoth Nandakumar, Qiang Qu, Peng Mi, Tongliang Liu:
State space models can express n-gram languages. - Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. - Thibault Le Sellier de Chezelles, Maxime Gasse, Alexandre Lacoste, Massimo Caccia, Alexandre Drouin, Léo Boisvert, Megh Thakkar, Tom Marty, Rim Assouel, Sahar Omidi Shayegan, Lawrence Keunho Jang, Xing Han Lù, Ori Yoran, Dehan Kong, Frank F. Xu, Siva Reddy, Graham Neubig, Quentin Cappart, Russ Salakhutdinov, Nicolas Chapados:
The BrowserGym Ecosystem for Web Agent Research. - Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch, Daniel Rueckert, Alexander Ziller:
Visual Privacy Auditing with Diffusion Models. - Zihao Liang, Tianyu Zhou, Zehui Lu, Shaoshuai Mou:
Online Control-Informed Learning. - Pihe Hu, Shaolong Li, Xun Wang, Longbo Huang:
Mixed Sparsity Training: Achieving 4× FLOP Reduction for Transformer Pretraining. - Oisín Nolan, Tristan S. W. Stevens, Wessel L. van Nierop, Ruud van Sloun:
Active Diffusion Subsampling. - Martin Bichler, Davide Legacci, Panayotis Mertikopoulos, Matthias Oberlechner, Bary S. R. Pradelski:
Characterizing the Convergence of Game Dynamics via Potentialness. - Jonas Brusokas, Seshu Tirupathi, Dalin Zhang, Torben Bach Pedersen:
The Time-Energy Model: Selective Time-Series Forecasting Using Energy-Based Models. - Arash Mari Oriyad, Mohammadali Banayeeanzade, Reza Abbasi, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah:
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models! - Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu:
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement. - Muheng Li, Ruqi Zhang:
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization. - Manuel Faysse, Patrick Fernandes, Nuno Miguel Guerreiro, António Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, João Alves, Ricardo Rei, Pedro Henrique Martins, Antoni Bigata Casademunt, François Yvon, André F. T. Martins, Gautier Viaud, Céline Hudelot, Pierre Colombo:
CroissantLLM: A Truly Bilingual French-English Language Model. - Tao Daniel Alter, Raz Lapid, Moshe Sipper:
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective. - Ashka Shah, Adela Frances DePavia, Nathaniel C. Hudson, Ian T. Foster, Rick Stevens:
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning. - Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Clemens Kaptein, Mykola Pechenizkiy:
Rethinking Knowledge Transfer in Learning Using Privileged Information. - Hanyang Wang, Juergen Branke, Matthias Poloczek:
Respecting the limit: Bayesian optimization with a bound on the optimal value. - Yousef El-Laham, Zhongchang Sun, Haibei Zhu, Tucker Balch, Svitlana Vyetrenko:
Variational Neural Stochastic Differential Equations with Change Points. - Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff, Kevin Patrick Murphy:
A unifying framework for generalised Bayesian online learning in non-stationary environments. - Garweet Sresth, Satish Mulleti, Ajit Rajwade:
Unlabelled Compressive Sensing under Sparse Permutation and Prior Information. - Akshay Kumar, Jarvis D. Haupt:
Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations. - Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, Grant Van Horn:
Align and Distill: Unifying and Improving Domain Adaptive Object Detection. - Hana Yahia, Bruno Figliuzzi, Florent Di Meglio, Laurent Gerbaud, Stephane Menand, Mohamed Mahjoub:
Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction. - Sai Srinivas Kancheti, Rahul Vigneswaran, Bamdev Mishra, Vineeth N. Balasubramanian:
HARE: Human-in-the-Loop Algorithmic Recourse. - Ramansh Sharma, Varun Shankar:
Ensemble and Mixture-of-Experts DeepONets For Operator Learning. - Shwai He, Daize Dong, Liang Ding, Ang Li:
Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques. - Yaochen Hu, Mai Zeng, Ge Zhang, Pavel Rumiantsev, Liheng Ma, Yingxue Zhang, Mark Coates:
Sparse Decomposition of Graph Neural Networks. - Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky:
Meta-learning Optimizers for Communication-Efficient Learning. - Yun Jin Park, Didong Li:
Lower Ricci Curvature for Efficient Community Detection. - Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner:
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM). - Pranav Jeevan, Amit Sethi:
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision. - Mikko A. Heikkilä:
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps. - Asaf Shul, Eliahu Horwitz, Yedid Hoshen:
Distilling Datasets Into Less Than One Image. - José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu:
A Unified View of Double-Weighting for Marginal Distribution Shift. - Brian Matejek, Ashish Gehani, Nathaniel D. Bastian, Daniel J. Clouse, Bradford J. Kline, Susmit Jha:
SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection. - Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson:
Understanding and Robustifying Sub-domain Alignment for Domain Adaptation. - Michael Hagmann, Michael Staniek, Stefan Riezler:
Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation. - Moussa Kassem Sbeyti, Nadja Klein, Azarm Nowzad, Fikret Sivrikaya, Sahin Albayrak:
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection. - Nolan Simran Dey, J. Eric Taylor, Alexander Wong, Bryan P. Tripp, Graham W. Taylor:
Neuron-based explanations of neural networks sacrifice completeness and interpretability. - Dan Kushnir, Sandeep Silwal:
Cluster Tree for Nearest Neighbor Search. - Christian Dietrich Weilbach, Frank Wood:
Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks. - Yuki Tsukada, Hideaki Iiduka:
Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate. - William Chang, Yuanhao Lu:
Multiplayer Information Asymmetric Contextual Bandits. - Weixin Liang, Lili Yu, Liang Luo, Srini Iyer, Ning Dong, Chunting Zhou, Gargi Ghosh, Mike Lewis, Wen-tau Yih, Luke Zettlemoyer, Xi Victoria Lin:
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models. - Alessandro De Palma, Serge Durand, Zakaria Chihani, François Terrier, Caterina Urban:
On Using Certified Training towards Empirical Robustness. - Tristan S. W. Stevens, Hans Van Gorp, Faik C. Meral, Jun Seob Shin, Jason Yu, Jean-Luc Robert, Ruud van Sloun:
Removing Structured Noise using Diffusion Models. - Woomin Song, Jihoon Tack, Sangwoo Mo, Seunghyuk Oh, Jinwoo Shin:
Sparsified State-Space Models are Efficient Highway Networks.
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