Most Cited ICML "markerless motion capture" Papers
5,975 papers found • Page 19 of 30
Conference
Provable Interactive Learning with Hindsight Instruction Feedback
Dipendra Misra, Aldo Pacchiano, Robert Schapire
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
Yichuan Mo, Hui Huang, Mingjie Li et al.
Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm
Mimoun Mohamed, Francois Malgouyres, Valentin Emiya et al.
OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification
Shikhar Mohan, Deepak Saini, Anshul Mittal et al.
Language Models with Conformal Factuality Guarantees
Christopher Mohri, Tatsunori Hashimoto
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks
Bjørn Leth Møller, Christian Igel, Kristoffer Wickstrøm et al.
Slot Abstractors: Toward Scalable Abstract Visual Reasoning
Shanka Subhra Mondal, Jonathan Cohen, Taylor Webb
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
Behrad Moniri, Donghwan Lee, Hamed Hassani et al.
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli et al.
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
Hiroshi Morioka, Aapo Hyvarinen
Position: Levels of AGI for Operationalizing Progress on the Path to AGI
Meredith Morris, Jascha Sohl-Dickstein, Noah Fiedel et al.
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam Charles
Truly No-Regret Learning in Constrained MDPs
Adrian Müller, Pragnya Alatur, Volkan Cevher et al.
Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation
Alexander Munteanu, Simon Omlor
Turnstile $\ell_p$ leverage score sampling with applications
Alexander Munteanu, Simon Omlor
BAGEL: Bootstrapping Agents by Guiding Exploration with Language
Shikhar Murty, Christopher Manning, Peter Shaw et al.
Factored-Reward Bandits with Intermediate Observations
Marco Mussi, Simone Drago, Marcello Restelli et al.
Best Arm Identification for Stochastic Rising Bandits
Marco Mussi, Alessandro Montenegro, Francesco Trovò et al.
Test-Time Regret Minimization in Meta Reinforcement Learning
Mirco Mutti, Aviv Tamar
Learning in Deep Factor Graphs with Gaussian Belief Propagation
Seth Nabarro, Mark van der Wilk, Andrew Davison
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh et al.
Density Ratio Estimation with Doubly Strong Robustness
Ryosuke Nagumo, Hironori Fujisawa
Equivariant Deep Weight Space Alignment
Aviv Navon, Aviv Shamsian, Ethan Fetaya et al.
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design
Quan Nguyen, Adji Bousso Dieng
On Least Square Estimation in Softmax Gating Mixture of Experts
Huy Nguyen, Nhat Ho, Alessandro Rinaldo
PIDformer: Transformer Meets Control Theory
Tam Nguyen, Cesar Uribe, Tan Nguyen et al.
Differentially private exact recovery for stochastic block models
Dung Nguyen, Anil Vullikanti
Novel Spectral Algorithms for the Partial Credit Model
Duc Nguyen, Anderson Zhang
Sliced Wasserstein with Random-Path Projecting Directions
Khai Nguyen, Shujian Zhang, Tam Le et al.
Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
Xinyi Ni, Guanlin Liu, Lifeng Lai
How Transformers Learn Causal Structure with Gradient Descent
Eshaan Nichani, Alex Damian, Jason Lee
Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error
Masaaki Nishino, Kengo Nakamura, Norihito Yasuda
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen et al.
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
Xinlei Niu, Christian Walder, Jing Zhang et al.
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching
Divya Nori, Wengong Jin
$f$-Divergence Based Classification: Beyond the Use of Cross-Entropy
Nicola Novello, Andrea Tonello
In value-based deep reinforcement learning, a pruned network is a good network
Johan Obando Ceron, Aaron Courville, Pablo Samuel Castro
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Obando Ceron, Ghada Sokar, Timon Willi et al.
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
Linear Explanations for Individual Neurons
Tuomas Oikarinen, Lily Weng
Adaptive Proximal Gradient Methods Are Universal Without Approximation
Konstantinos Oikonomidis, Emanuel Laude, Puya Latafat et al.
Fair Resource Allocation in Multi-Task Learning
Hao Ban, Kaiyi Ji
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Andreas Opedal, Alessandro Stolfo, Haruki Shirakami et al.
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang et al.
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Differentiable Mapper for Topological Optimization of Data Representation
Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel
Structured Chemistry Reasoning with Large Language Models
Siru Ouyang, Zhuosheng Zhang, Bing Yan et al.
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent
Kaan Ozkara, Can Karakus, Parameswaran Raman et al.
Implicit Representations via Operator Learning
Sourav Pal, Harshavardhan Adepu, Clinton Wang et al.
Bayesian Program Learning by Decompiling Amortized Knowledge
Alessandro Palmarini, Christopher Lucas, Siddharth N
$S^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Zijie Pan, Yushan Jiang, Sahil Garg et al.
Feedback Loops With Language Models Drive In-Context Reward Hacking
Alexander Pan, Erik Jones, Meena Jagadeesan et al.
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
Xiaokang Pan, Xingyu Li, Jin Liu et al.
RMIB: Representation Matching Information Bottleneck for Matching Text Representations
Haihui Pan, zhifang Liao, Wenrui Xie et al.
Auto-Encoding Morph-Tokens for Multimodal LLM
Kaihang Pan, Siliang Tang, Juncheng Li et al.
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar et al.
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Xianghe Pang, shuo tang, Rui Ye et al.
Trainable Transformer in Transformer
Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia et al.
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein et al.
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, woohyeon Byeon, Seongmin Kim et al.
The Linear Representation Hypothesis and the Geometry of Large Language Models
Kiho Park, Yo Joong Choe, Victor Veitch
Mean-field Chaos Diffusion Models
Sungwoo Park, Dongjun Kim, Ahmed Alaa
Foundation Policies with Hilbert Representations
Seohong Park, Tobias Kreiman, Sergey Levine
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
Chanho Park, Namyoon Lee
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Ji Won Park, Natasa Tagasovska, Michael Maser et al.
State-Free Inference of State-Space Models: The *Transfer Function* Approach
Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro et al.
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Yash Patel, Declan McNamara, Jackson Loper et al.
Optimal Ridge Regularization for Out-of-Distribution Prediction
Pratik Patil, Jin-Hong Du, Ryan Tibshirani
LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
Anselm Paulus, Georg Martius, Vit Musil
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Brahma Pavse, Matthew Zurek, Yudong Chen et al.
Graph Automorphism Group Equivariant Neural Networks
Edward Pearce-Crump, William J. Knottenbelt
BetterV: Controlled Verilog Generation with Discriminative Guidance
Zehua Pei, Huiling Zhen, Mingxuan Yuan et al.
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
Amit Peleg, Matthias Hein
Knowledge Distillation with Auxiliary Variable
Bo Peng, zhen fang, Guangquan Zhang et al.
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers
Duo Peng, Qiuhong Ke, Jun Liu
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
Andi Peng, Yuying Sun, Tianmin Shu et al.
UPOCR: Towards Unified Pixel-Level OCR Interface
Dezhi Peng, Zhenhua Yang, Jiaxin Zhang et al.
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Hongyi Peng, Han Yu, Xiaoli Tang et al.
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai et al.
A Subquadratic Time Algorithm for Robust Sparse Mean Estimation
Ankit Pensia
Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach
Johan Peralez, Aurélien Delage, Olivier Buffet et al.
The Relative Value of Prediction in Algorithmic Decision Making
Juan Perdomo
Interpreting and Improving Diffusion Models from an Optimization Perspective
Frank Permenter, Chenyang Yuan
Mechanistic Neural Networks for Scientific Machine Learning
Adeel Pervez, Francesco Locatello, Efstratios Gavves
Bayesian Regret Minimization in Offline Bandits
Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh
Prompting a Pretrained Transformer Can Be a Universal Approximator
Aleksandar Petrov, Phil Torr, Adel Bibi
Transport of Algebraic Structure to Latent Embeddings
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Cross-view Masked Diffusion Transformers for Person Image Synthesis
Trung Pham, Kang Zhang, Chang Yoo
Detecting Influence Structures in Multi-Agent Reinforcement Learning
Fabian Raoul Pieroth, Katherine Fitch, Lenz Belzner
Contrasting Multiple Representations with the Multi-Marginal Matching Gap
Zoe Piran, Michal Klein, James Thornton et al.
Adaptive Conformal Inference by Betting
Aleksandr Podkopaev, Darren Xu, Kuang-chih Lee
Mechanistic Design and Scaling of Hybrid Architectures
Michael Poli, Armin Thomas, Eric Nguyen et al.
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani, Erick Delage, Angelos Georghiou
Learning Multiple Secrets in Mastermind
Milind Prabhu, David Woodruff
The Entropy Enigma: Success and Failure of Entropy Minimization
Ori Press, Ravid Shwartz-Ziv, Yann LeCun et al.
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation
Yuanhao Pu, Xiaolong Chen, Xu Huang et al.
Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images
Bin Pu, Xingguo Lv, Jiewen Yang et al.
Learning to Remove Cuts in Integer Linear Programming
Pol Puigdemont, EFSTRATIOS PANTELEIMON SKOULAKIS, Grigorios Chrysos et al.
Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration
Shi-ang Qi, Yakun Yu, Russell Greiner
ByMI: Byzantine Machine Identification with False Discovery Rate Control
Chengde Qian, Mengyuan Wang, Haojie Ren et al.
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation
Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang et al.
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
Dan Qiao, Yu-Xiang Wang
ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision
Congyu Qiao, Ning Xu, Yihao Hu et al.
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Zhen Qin, Daoyuan Chen, Bingchen Qian et al.
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
Haotong Qin, Xudong Ma, Xingyu Zheng et al.
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Zhen Qin, Weigao Sun, Dong Li et al.
Feasible Reachable Policy Iteration
Shentao Qin, Yujie Yang, Yao Mu et al.
Learning High-Order Relationships of Brain Regions
Weikang Qiu, Huangrui Chu, Selena Wang et al.
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
Zi-Hao Qiu, Siqi Guo, Mao Xu et al.
Transferring Knowledge From Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle Robinson et al.
Compute Better Spent: Replacing Dense Layers with Structured Matrices
Shikai Qiu, Andres Potapczynski, Marc Finzi et al.
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Yanru Qu, Keyue Qiu, Yuxuan Song et al.
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
Helen Qu, Sang Michael Xie
Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
Guorui Quan, Zhiqiang Xu, Guiliang Liu
Multiply-Robust Causal Change Attribution
Víctor Quintas-Martínez, Mohammad Bahadori, Eduardo Santiago et al.
Decomposable Submodular Maximization in Federated Setting
Akbar Rafiey
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Ossi Räisä, Joonas Jälkö, Antti Honkela
STEER: Assessing the Economic Rationality of Large Language Models
Narun Raman, Taylor Lundy, Samuel Joseph Amouyal et al.
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona et al.
Position: The Reasonable Person Standard for AI
Sunayana Rane
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi et al.
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
Ishaan Rawal, Alexander Matyasko, Shantanu Jaiswal et al.
Fair Federated Learning via the Proportional Veto Core
Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan et al.
Optimal Batched Linear Bandits
Xuanfei Ren, Tianyuan Jin, Pan Xu
TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules
Weijieying Ren, Xiaoting Li, Huiyuan Chen et al.
Rejuvenating image-GPT as Strong Visual Representation Learners
Sucheng Ren, Zeyu Wang, Hongru Zhu et al.
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model
Milong Ren, Tian Zhu, Haicang Zhang
Plug-and-Play image restoration with Stochastic deNOising REgularization
Marien Renaud, Jean Prost, Arthur Leclaire et al.
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks
Zach Robertson, Sanmi Koyejo
Universal Gradient Methods for Stochastic Convex Optimization
Anton Rodomanov, Ali Kavis, Yongtao Wu et al.
Position: Key Claims in LLM Research Have a Long Tail of Footnotes
Anna Rogers, Sasha Luccioni
Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning
Esther Rolf, Konstantin Klemmer, Caleb Robinson et al.
Invariant Risk Minimization Is A Total Variation Model
Zhao-Rong Lai, Weiwen Wang
Position: Application-Driven Innovation in Machine Learning
David Rolnick, Alan Aspuru-Guzik, Sara Beery et al.
One-Shot Strategic Classification Under Unknown Costs
Elan Rosenfeld, Nir Rosenfeld
Modelling Microbial Communities with Graph Neural Networks
Albane Ruaud, Cansu Sancaktar, Marco Bagatella et al.
Position: Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova et al.
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber et al.
Rolling Diffusion Models
David Ruhe, Jonathan Heek, Tim Salimans et al.
Second-Order Uncertainty Quantification: A Distance-Based Approach
Yusuf Sale, Viktor Bengs, Michele Caprio et al.
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Sudeep Salgia, Sattar Vakili, Qing Zhao
Predictive Coding beyond Correlations
Tommaso Salvatori, Luca Pinchetti, Amine M'Charrak et al.
Proactive Detection of Voice Cloning with Localized Watermarking
Robin San Roman, Pierre Fernandez, Hady Elsahar et al.
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
Sparse and Structured Hopfield Networks
Saúl Santos, Vlad Niculae, Daniel McNamee et al.
A sampling theory perspective on activations for implicit neural representations
Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko et al.
A fast algorithm to simulate nonlinear resistive networks
Benjamin Scellier
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Philip Schär, Michael Habeck, Daniel Rudolf
Incentivized Learning in Principal-Agent Bandit Games
Antoine Scheid, Daniil Tiapkin, Etienne Boursier et al.
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Christian Schlarmann, Naman Singh, Francesco Croce et al.
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
Marvin Schmitt, Desi Ivanova, Daniel Habermann et al.
Online Learning with Bounded Recall
Jon Schneider, Kiran Vodrahalli
Asymptotics of Learning with Deep Structured (Random) Features
Dominik Schröder, Daniil Dmitriev, Hugo Cui et al.
Simultaneous identification of models and parameters of scientific simulators
Cornelius Schröder, Jakob Macke
Bayesian Adaptation of Network Depth and Width for Continual Learning
Jeevan Thapa, Rui Li
Towards Scalable and Versatile Weight Space Learning
Konstantin Schürholt, Michael Mahoney, Damian Borth
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes
Nabeel Seedat, Nicolas Huynh, Boris van Breugel et al.
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi et al.
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar et al.
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
Amrith Setlur, Saurabh Garg, Virginia Smith et al.
A Multimodal Automated Interpretability Agent
Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang et al.
The Balanced-Pairwise-Affinities Feature Transform
Daniel Shalam, Simon Korman
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David Zhang et al.
On Multi-Armed Bandit with Impatient Arms
Yuming Shao, Zhixuan Fang
Language Generation with Strictly Proper Scoring Rules
Chenze Shao, Fandong Meng, Yijin Liu et al.
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Bill Daqian Shao, Ashkan Soleymani, Francesco Quinzan et al.
How Far Can Fairness Constraints Help Recover From Biased Data?
Mohit Sharma, Amit Jayant Deshpande
Reducing sequential change detection to sequential estimation
Shubhanshu Shekhar, Aaditya Ramdas
Exploring the Complexity of Deep Neural Networks through Functional Equivalence
Guohao Shen
Position: Do pretrained Transformers Learn In-Context by Gradient Descent?
Lingfeng Shen, Aayush Mishra, Daniel Khashabi
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Jin Hwa Lee, Stefano Mannelli, Andrew Saxe
ReLUs Are Sufficient for Learning Implicit Neural Representations
Joseph Shenouda, Yamin Zhou, Robert Nowak
Double Momentum Method for Lower-Level Constrained Bilevel Optimization
Wanli Shi, Yi Chang, Bin Gu
LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Rajendrakumar Gare, Jinjin Tian et al.
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
Laixi Shi, Eric Mazumdar, Yuejie Chi et al.
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Dachuan Shi, Chaofan Tao, Anyi Rao et al.
Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu et al.
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou et al.
Statistical Test for Attention Maps in Vision Transformers
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka et al.
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee et al.
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
Jacob Si, Wendy Yusi Cheng, Michael Cooper et al.
Embarrassingly Parallel GFlowNets
Tiago Silva, Luiz Carvalho, Amauri Souza et al.
Deletion-Anticipative Data Selection with a Limited Budget
Rachael Hwee Ling Sim, Jue Fan, Xiao Tian et al.
Latent variable model for high-dimensional point process with structured missingness
Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
Domain Generalisation via Imprecise Learning
Anurag Singh, Siu Lun Chau, Shahine Bouabid et al.
Byzantine Resilient and Fast Federated Few-Shot Learning
Ankit Pratap Singh, Namrata Vaswani
Parallelized Spatiotemporal Slot Binding for Videos
Gautam Singh, Yue Wang, Jiawei Yang et al.
In-Context Reinforcement Learning for Variable Action Spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov et al.
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee et al.
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints
Oscar Smee, Fred Roosta
Probabilistic Modeling of Interpersonal Coordination Processes
Paulo Soares, Adarsh Pyarelal, Meghavarshini Krishnaswamy et al.
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou et al.
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
Ning Liu, Yiming Fan, Xianyi Zeng et al.
Hybrid Reinforcement Learning from Offline Observation Alone
Yuda Song, J. Bagnell, Aarti Singh
SurfPro: Functional Protein Design Based on Continuous Surface
Zhenqiao Song, Tinglin Huang, Lei Li et al.
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
Ziyang Song, Jinxi Li, Bo Yang