Most Cited ICML "parameterized quasi-physical simulators" Papers
5,975 papers found • Page 19 of 30
Conference
The Importance of Being Lazy: Scaling Limits of Continual Learning
Jacopo Graldi, Alessandro Breccia, Giulia Lanzillotta et al.
How Effective Can Dropout Be in Multiple Instance Learning ?
Wenhui Zhu, Peijie Qiu, Xiwen Chen et al.
Ehrenfeucht-Haussler Rank and Chain of Thought
Pablo Barcelo, Alexander Kozachinskiy, Tomasz Steifer
PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs
Jianqing Zhang, Yang Liu, Jie Fu et al.
Multivariate Conformal Selection
Tian Bai, Yue Zhao, Xiang Yu et al.
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
Ermis Soumalias, Jakob Heiss, Jakob Weissteiner et al.
Compositional Causal Reasoning Evaluation in Language Models
Jacqueline Maasch, Alihan Hüyük, Xinnuo Xu et al.
Sassha: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation
Dahun Shin, Dongyeop Lee, Jinseok Chung et al.
Reweighted Solutions for Weighted Low Rank Approximation
David Woodruff, Taisuke Yasuda
Open-Det: An Efficient Learning Framework for Open-Ended Detection
Guiping Cao, Tao Wang, Wenjian Huang et al.
Adaptive Sample Sharing for Multi Agent Linear Bandits
Hamza Cherkaoui, Merwan Barlier, Igor Colin
Categorical Schrödinger Bridge Matching
Grigoriy Ksenofontov, Aleksandr Korotin
Observable Propagation: Uncovering Feature Vectors in Transformers
Jacob Dunefsky, Arman Cohan
Towards the Causal Complete Cause of Multi-Modal Representation Learning
Jingyao Wang, Siyu Zhao, Wenwen Qiang et al.
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
Yue Meng, Chuchu Fan
On the Consistency of Kernel Methods with Dependent Observations
Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow
The Value of Prediction in Identifying the Worst-Off
Unai Fischer Abaigar, Christoph Kern, Juan Perdomo
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments
Zijian Cheng, 贾 子怡, Zhi Zhou et al.
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
Jing Wang, Yu-Jie Zhang, Peng Zhao et al.
O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck et al.
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
Firas Laakom, Haobo Chen, Jürgen Schmidhuber et al.
Score Matching with Missing Data
Josh Givens, Song Liu, Henry Reeve
Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning
Anna Soligo, Pietro Ferraro, David Boyle
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
Jinmin He, Kai Li, Yifan Zang et al.
Flexibility-conditioned protein structure design with flow matching
Vsevolod Viliuga, Leif Seute, Nicolas Wolf et al.
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
Yang Shen, Xiu-Shen Wei, Yifan Sun et al.
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models
Lingyu Li, Yixu Wang, Haiquan Zhao et al.
Learning with Selectively Labeled Data from Multiple Decision-makers
Jian Chen, Zhehao Li, Xiaojie Mao
Latent Mamba Operator for Partial Differential Equations
Karn Tiwari, Niladri Dutta, N M Anoop Krishnan et al.
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws
Xiyuan Wei, Ming Lin, Fanjiang Ye et al.
Differentiable Quadratic Optimization For the Maximum Independent Set Problem
Ismail Alkhouri, Cedric Le Denmat, Yingjie Li et al.
CogMath: Assessing LLMs' Authentic Mathematical Ability from a Human Cognitive Perspective
Jiayu Liu, Zhenya Huang, Wei Dai et al.
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Hanxiao Wang et al.
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models
Taj Jones-McCormick, Aukosh Jagannath, Subhabrata Sen
Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
Brett Barkley, David Fridovich-Keil
Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
Mikael Møller Høgsgaard, Andrea Paudice
Differentiable Solver Search for Fast Diffusion Sampling
shuai wang, Zexian Li, Qipeng zhang et al.
Permutation Equivariant Neural Networks for Symmetric Tensors
Edward Pearce-Crump
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
Yang Yu, Kai Han, Hang Zhou et al.
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
Yoonsoo Nam, Seok Hyeong Lee, Clémentine Dominé et al.
Self-Bootstrapping for Versatile Test-Time Adaptation
Shuaicheng Niu, Guohao Chen, Peilin Zhao et al.
Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics
Herman Chau, Helen Jenne, Davis Brown et al.
Empirical Privacy Variance
Yuzheng Hu, Fan Wu, Ruicheng Xian et al.
Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements
Arya Mazumdar, Neha Sangwan
Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
Sergei Shumilin, Alexander Ryabov, Nikolay Yavich et al.
On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
Yunyan Bai, Yuxing Liu, Luo Luo
Fast Inference with Kronecker-Sparse Matrices
Antoine Gonon, Léon Zheng, Pascal Carrivain et al.
Turnstile $\ell_p$ leverage score sampling with applications
Alexander Munteanu, Simon Omlor
Deep Regression Representation Learning with Topology
Shihao Zhang, Kenji Kawaguchi, Angela Yao
Tilted Sharpness-Aware Minimization
Tian Li, Tianyi Zhou, Jeff Bilmes
Directly Forecasting Belief for Reinforcement Learning with Delays
Qingyuan Wu, Yuhui Wang, Simon Zhan et al.
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
Shuo Han, German Espinosa, Junda Huang et al.
Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources
Renzhe Xu, Kang Wang, Bo Li
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Sidhanth Holalkere, David S Bindel, Silvia Sellán et al.
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar et al.
An Entropy-Based Model for Hierarchical Learning
Amir R. Asadi
Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning
Lianbo Ma, Jianlun Ma, Yuee Zhou et al.
Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments
Runfa Chen, Ling Wang, Yu Du et al.
RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning
Jason Chan, Robert Gaizauskas, Zhixue Zhao
Olica: Efficient Structured Pruning of Large Language Models without Retraining
Jiujun He, Huazhen Lin
Adaptive Elicitation of Latent Information Using Natural Language
Jimmy Wang, Tom Zollo, Richard Zemel et al.
Accelerating Convergence in Bayesian Few-Shot Classification
Tianjun Ke, Haoqun Cao, Feng Zhou
PAC Learning with Improvements
Idan Attias, Avrim Blum, Keziah Naggita et al.
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent et al.
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang et al.
Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning
Zongmeng Zhang, Yufeng Shi, Jinhua Zhu et al.
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu, Idan Attias, Daniel Roy
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability
Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama
FESSNC: Fast Exponentially Stable and Safe Neural Controller
Jingdong Zhang, Luan Yang, Qunxi Zhu et al.
Bridging Layout and RTL: Knowledge Distillation based Timing Prediction
Mingjun Wang, Yihan Wen, Bin Sun et al.
Learning Distribution-wise Control in Representation Space for Language Models
Deng, Ruidi Chang, Hanjie Chen
Mixed-curvature decision trees and random forests
Philippe Chlenski, Quentin Chu, Raiyan Khan et al.
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
Nurbek Tastan, Samuel Horváth, Karthik Nandakumar
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
Can Machines Learn the True Probabilities?
Jinsook Kim
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
Filip Szatkowski, Yaoyue Zheng, Fei Yang et al.
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Shunxing Fan, Mingming Gong, Kun Zhang
Large Continual Instruction Assistant
Jingyang Qiao, zhizhong zhang, Xin Tan et al.
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
Luise Ge, Michael Lanier, Anindya Sarkar et al.
Sample-Optimal Agnostic Boosting with Unlabeled Data
Udaya Ghai, Karan Singh
Bayesian Program Learning by Decompiling Amortized Knowledge
Alessandro Palmarini, Christopher Lucas, Siddharth N
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers et al.
Compact Optimality Verification for Optimization Proxies
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau et al.
Convergence of Consistency Model with Multistep Sampling under General Data Assumptions
Yiding Chen, Yiyi Zhang, Owen Oertell et al.
Bottleneck-Minimal Indexing for Generative Document Retrieval
Xin Du, Lixin Xiu, Kumiko Tanaka-Ishii
Interpreting Equivariant Representations
Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
Demystifying SGD with Doubly Stochastic Gradients
Kyurae Kim, Joohwan Ko, Yian Ma et al.
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
Jaeheun Jung, Jaehyuk Lee, ChangHae Jung et al.
Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
Jaemoo Choi, Jaewoong Choi, Dohyun Kwon
Trustworthy Actionable Perturbations
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon
Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective
Seungwook Han, Jinyeop Song, Jeff Gore et al.
Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals
Vivienne Huiling Wang, Tinghuai Wang, Joni Pajarinen
The Fundamental Limits of Least-Privilege Learning
Theresa Stadler, Bogdan Kulynych, Michael Gastpar et al.
Conformal Predictions under Markovian Data
Frédéric Zheng, Alexandre Proutiere
Test-Time Multimodal Backdoor Detection by Contrastive Prompting
Yuwei Niu, Shuo He, Qi Wei et al.
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Ziyang Yu, Wenbing Huang, Yang Liu
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Olga Ovcharenko, Florian Barkmann, Philip Toma et al.
Simple Path Structural Encoding for Graph Transformers
Louis Airale, Antonio Longa, Mattia Rigon et al.
LieRE: Lie Rotational Positional Encodings
Sophie Ostmeier, Brian Axelrod, Maya Varma et al.
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
Yassir Jedra, William Réveillard, Stefan Stojanovic et al.
Stochastic Online Conformal Prediction with Semi-Bandit Feedback
Haosen Ge, Hamsa Bastani, Osbert Bastani
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan HAO, xingyuan pan, Hanning Zhang et al.
Geometric Algebra Planes: Convex Implicit Neural Volumes
Irmak Sivgin, Sara Fridovich-Keil, Gordon Wetzstein et al.
SPHINX: Structural Prediction using Hypergraph Inference Network
Iulia Duta, Pietro Lió
Concept-Centric Token Interpretation for Vector-Quantized Generative Models
Tianze Yang, Yucheng Shi, Mengnan Du et al.
Polynomial Time Learning Augmented Algorithms for NP-hard Permutation Problems
Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis et al.
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback
Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar et al.
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
Xiang Zhang, Jiaqi Wei, Zijie Qiu et al.
FedECADO: A Dynamical System Model of Federated Learning
Aayushya Agarwal, Gauri Joshi, Lawrence Pileggi
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut et al.
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng, Jiashuo Liu, Jiaxing Li et al.
Switched Flow Matching: Eliminating Singularities via Switching ODEs
Qunxi Zhu, Wei Lin
Reflective Policy Optimization
Yaozhong Gan, yan renye, zhe wu et al.
POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval
Yaoyang Liu, Junlin Li, Yinjun Wu et al.
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach
Qian Peng, Yajie Bao, Haojie Ren et al.
Measuring In-Context Computation Complexity via Hidden State Prediction
Vincent Herrmann, Róbert Csordás, Jürgen Schmidhuber
When to retrain a machine learning model
Florence Regol, Leo Schwinn, Kyle Sprague et al.
Pareto-frontier Entropy Search with Variational Lower Bound Maximization
Masanori Ishikura, Masayuki Karasuyama
Finite-Time Analysis of Discrete-Time Stochastic Interpolants
Yuhao Liu, Yu Chen, Rui Hu et al.
Stealthy Imitation: Reward-guided Environment-free Policy Stealing
Zhixiong Zhuang, Irina Nicolae, Mario Fritz
Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective
Yunzhen Yao, Lie He, Michael Gastpar
An in depth look at the Procrustes-Wasserstein distance: properties and barycenters
Davide Adamo, Marco Corneli, Manon Vuillien et al.
ConText: Driving In-context Learning for Text Removal and Segmentation
Fei Zhang, Pei Zhang, Baosong Yang et al.
Neutral residues: revisiting adapters for model extension
Franck TALLA, Edouard Grave, Herve Jegou
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani, Erick Delage, Angelos Georghiou
Scalable Sobolev IPM for Probability Measures on a Graph
Tam Le, Truyen Nguyen, Hideitsu Hino et al.
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui et al.
From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set
Mara Finkelstein, Daniel Deutsch, Parker Riley et al.
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
Kaizhao Liu, Jose Blanchet, Lexing Ying et al.
Function-to-Style Guidance of LLMs for Code Translation
Longhui Zhang, Bin Wang, Jiahao Wang et al.
Super Deep Contrastive Information Bottleneck for Multi-modal Clustering
Zhengzheng Lou, Ke Zhang, Yucong Wu et al.
On The Statistical Complexity of Offline Decision-Making
Thanh Nguyen-Tang, Raman Arora
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
Bokun Wang, Tianbao Yang
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar et al.
Are Large Language Models Ready for Multi-Turn Tabular Data Analysis?
Jinyang Li, Nan Huo, Yan Gao et al.
Low-Dimension-to-High-Dimension Generalization and Its Implications for Length Generalization
Yang Chen, Long Yang, Yitao Liang et al.
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Zhao Yang, jiwei zhu, Bing Su
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
Sungnyun Kim, Kangwook Jang, Sangmin Bae et al.
Beyond One-Hot Labels: Semantic Mixing for Model Calibration
Haoyang Luo, Linwei Tao, Minjing Dong et al.
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen et al.
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Lorenzo Magnino, Yuchen Zhu, Mathieu Lauriere
Representation Surgery in Model Merging with Probabilistic Modeling
Qi Wei, Shuo He, Enneng Yang et al.
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Ian Gemp, Andreas Haupt, Luke Marris et al.
On the Impact of Performative Risk Minimization for Binary Random Variables
Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi et al.
Learning Latent Graph Structures and their Uncertainty
Alessandro Manenti, Daniele Zambon, Cesare Alippi
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun et al.
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
Xiang Fang, Arvind Easwaran, Blaise Genest
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
Yididiya Nadew, Xuhui Fan, Christopher J Quinn
Pausing Policy Learning in Non-stationary Reinforcement Learning
Hyunin Lee, Ming Jin, Javad Lavaei et al.
A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations
Antoine Gonon, Nicolas Brisebarre, Elisa Riccietti et al.
Efficient Quantification of Multimodal Interaction at Sample Level
Zequn Yang, Hongfa Wang, Di Hu
State-Constrained Zero-Sum Differential Games with One-Sided Information
Mukesh Ghimire, Lei Zhang, Zhe Xu et al.
Online Learning with Unknown Constraints
Karthik Sridharan, Seung Won Wilson Yoo
Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation
Yiming Yang, Xiaoyuan Cheng, Daniel Giles et al.
Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Chen Wei, Chi Zhang, Jiachen Zou et al.
Generalizing Orthogonalization for Models with Non-Linearities
David Rügamer, Chris Kolb, Tobias Weber et al.
GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models
Pengxiang Zhao, Xiaoming Yuan
Position: We Need Responsible, Application-Driven (RAD) AI Research
Sarah Hartman, Cheng Soon Ong, Julia Powles et al.
Relational Conformal Prediction for Correlated Time Series
Andrea Cini, Alexander Jenkins, Danilo Mandic et al.
Nonparametric Teaching for Graph Property Learners
Chen Zhang, Weixin Bu, Zeyi Ren et al.
On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning
Naram Mhaisen, George Iosifidis
Sample-specific Noise Injection for Diffusion-based Adversarial Purification
Yuhao Sun, Jiacheng Zhang, Zesheng Ye et al.
In-Context Reinforcement Learning From Suboptimal Historical Data
Juncheng Dong, Moyang Guo, Ethan Fang et al.
Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
Ross Clarke, Jose Miguel Hernandez-Lobato
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Seungho Baek, Taegeon Park, Jongchan Park et al.
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
Chi Zhang, Ziying Jia, George Atia et al.
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Tianjie Ju, Yi Hua, Hao Fei et al.
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
Tao Tang, Lijun Zhou, Pengkun Hao et al.
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Philip Schär, Michael Habeck, Daniel Rudolf
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks
Anas Jnini, Lorenzo Breschi, Flavio Vella
The Limits of Tractable Marginalization
Oliver Broadrick, Sanyam Agarwal, Guy Van den Broeck et al.
On the Duality between Gradient Transformations and Adapters
Lucas Torroba Hennigen, Hunter Lang, Han Guo et al.
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
Mao-Lin Luo, Zi-Hao Zhou, Tong Wei et al.
Evolving Subnetwork Training for Large Language Models
hanqi li, Lu Chen, Da Ma et al.
Unsupervised Learning for Class Distribution Mismatch
Pan Du, Zhao, Xinai Lu et al.
EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
Chung-Yiu Yau, Hoi To Wai, Parameswaran Raman et al.
Long-Short Alignment for Effective Long-Context Modeling in LLMs
Tianqi Du, Haotian Huang, Yifei Wang et al.
Sanity Checking Causal Representation Learning on a Simple Real-World System
Juan L. Gamella, Simon Bing, Jakob Runge
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
Youwei Shu, Xi Xiao, Derui Wang et al.
Demonstration Selection for In-Context Learning via Reinforcement Learning
Xubin Wang, Jianfei Wu, Yuan Yichen et al.
Progressively Label Enhancement for Large Language Model Alignment
Biao Liu, Ning Xu, Xin Geng
All-atom inverse protein folding through discrete flow matching
Kai Yi, Kiarash Jamali, Sjors Scheres
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai, Nika Haghtalab, Eric Zhao
FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
Zhen Sun, Lei Tan, Yunhang Shen et al.
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Bill Daqian Shao, Ashkan Soleymani, Francesco Quinzan et al.
Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
Ojash Neopane, Aaditya Ramdas, Aarti Singh
Improving Memory Efficiency for Training KANs via Meta Learning
Zhangchi Zhao, Jun Shu, Deyu Meng et al.
Compositional Generalization via Forced Rendering of Disentangled Latents
Qiyao Liang, Daoyuan Qian, Liu Ziyin et al.
Testing Conditional Mean Independence Using Generative Neural Networks
Yi Zhang, Linjun Huang, Yun Yang et al.
Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment
Yu Zhu, Chunfeng Song, Wanli Ouyang et al.
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
Shengbin Ye, Meng Li
Combinatorial Reinforcement Learning with Preference Feedback
Joongkyu Lee, Min-hwan Oh
Wait-Less Offline Tuning and Re-solving for Online Decision Making
Jingruo Sun, Wenzhi Gao, Ellen Vitercik et al.
Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
Maximilian Graf, Victor Thuot, Nicolas Verzelen
Submodular framework for structured-sparse optimal transport
Piyushi Manupriya, Pratik Kumar Jawanpuria, Karthik Gurumoorthy et al.
Reinforcement Learning with Random Time Horizons
Enric Borrell, Lorenz Richter, Christof Schuette
Stable Offline Value Function Learning with Bisimulation-based Representations
Brahma Pavse, Yudong Chen, Qiaomin Xie et al.
Generative Data Mining with Longtail-Guided Diffusion
David Hayden, Mao Ye, Timur Garipov et al.