Most Cited ICML "decision boundary mapping" Papers
5,975 papers found • Page 22 of 30
Conference
Sign Rank Limitations for Inner Product Graph Decoders
Su Hyeong Lee, QINGQI ZHANG, Risi Kondor
GenCO: Generating Diverse Designs with Combinatorial Constraints
Aaron Ferber, Arman Zharmagambetov, Taoan Huang et al.
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen et al.
ReLU Network with Width $d+\mathcal{O}(1)$ Can Achieve Optimal Approximation Rate
Chenghao Liu, Minghua Chen
Characterizing ResNet's Universal Approximation Capability
Chenghao Liu, Enming Liang, Minghua Chen
Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
Gil Kur, Pedro Abdalla, Pierre Bizeul et al.
Individual Fairness in Graph Decomposition
Kamesh Munagala, Govind S. Sankar
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Xiaoxuan Wang, ziniu hu, Pan Lu et al.
Bivariate Causal Discovery using Bayesian Model Selection
Anish Dhir, Samuel Power, Mark van der Wilk
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Gaurav Pandey, Yatin Nandwani, Tahira Naseem et al.
A Bayesian Approach to Online Planning
Nir Greshler, David Ben Eli, Carmel Rabinovitz et al.
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
Yuchen Wu, Minshuo Chen, Zihao Li et al.
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao et al.
Position: Video as the New Language for Real-World Decision Making
Sherry Yang, Jacob C Walker, Jack Parker-Holder et al.
Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
Position: Will we run out of data? Limits of LLM scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla et al.
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints
Akhil Agnihotri, Rahul Jain, Haipeng Luo
Local vs. Global Interpretability: A Computational Complexity Perspective
Shahaf Bassan, Guy Amir, Guy Katz
A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle
Nadav Hallak, Kfir Levy
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training
Tehila Dahan, Kfir Levy
Efficient Value Iteration for s-rectangular Robust Markov Decision Processes
Navdeep Kumar, Kaixin Wang, Kfir Levy et al.
Retrieval-Augmented Score Distillation for Text-to-3D Generation
Junyoung Seo, Susung Hong, Wooseok Jang et al.
Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation
Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu
Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems
David T. Hoffmann, Simon Schrodi, Jelena Bratulić et al.
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik et al.
Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Jiayue Wan, Raul Astudillo et al.
Prospective Side Information for Latent MDPs
Jeongyeol Kwon, Yonathan Efroni, Shie Mannor et al.
Neural NeRF Compression
Tuan Pham, Stephan Mandt
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
Chaoqun Du, Yizeng Han, Gao Huang
Graph Neural Network Explanations are Fragile
Jiate Li, Meng Pang, Yun Dong et al.
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
Generalization Error of Graph Neural Networks in the Mean-field Regime
Gholamali Aminian, Yixuan He, Gesine Reinert et al.
ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
Rohan Wadhawan, Hritik Bansal, Kai-Wei Chang et al.
Explorations of Self-Repair in Language Models
Cody Rushing, Neel Nanda
Effective Federated Graph Matching
Yang Zhou, Zijie Zhang, Zeru Zhang et al.
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Cunxiao Du, Jing Jiang, Xu Yuanchen et al.
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Jaehyeong Jo, Sung Ju Hwang
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
Xutong Liu, Siwei Wang, Jinhang Zuo et al.
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
Feihu Huang
Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
Jiewei Zhang, Song Guo, Peiran Dong et al.
BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
Shikai Fang, Qingsong Wen, Yingtao Luo et al.
When Will Gradient Regularization Be Harmful?
Yang Zhao, Hao Zhang, Xiuyuan Hu
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose et al.
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Wei Chen, Zhen Huang, Liang Xie et al.
Differentiable Model Scaling using Differentiable Topk
Kai Liu, Ruohui Wang, Jianfei Gao et al.
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
TaeHo Yoon, Jaeyeon (Jay) Kim, Jaewook Suh et al.
Differentially Private Decentralized Learning with Random Walks
Edwige Cyffers, Aurélien Bellet, Jalaj Upadhyay
Adaptive Robust Learning using Latent Bernoulli Variables
Aleksandr Karakulev, Dave Zachariah, Prashant Singh
Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation
Weixuan Liang, En Zhu, Shengju Yu et al.
Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information
Xinhang Wan, Jiyuan Liu, Xinwang Liu et al.
Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization
Qiang Fu, Ashia Wilson
DoRA: Weight-Decomposed Low-Rank Adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin et al.
Bayesian Exploration Networks
Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt et al.
Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
Brian Chen, Tianyang Hu, Hui Jin et al.
SAPG: Split and Aggregate Policy Gradients
Jayesh Singla, Ananye Agarwal, Deepak Pathak
Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data
Yujun Zhou, Yufei Han, Haomin Zhuang et al.
Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention
Aaron Havens, Alexandre Araujo, Huan Zhang et al.
Operator SVD with Neural Networks via Nested Low-Rank Approximation
Jongha (Jon) Ryu, Xiangxiang Xu, Hasan Sabri Melihcan Erol et al.
Offline Actor-Critic Reinforcement Learning Scales to Large Models
Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang et al.
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
Jonas Schweisthal, Dennis Frauen, M van der Schaar et al.
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi et al.
PGODE: Towards High-quality System Dynamics Modeling
Xiao Luo, Yiyang Gu, Huiyu Jiang et al.
Decomposing and Editing Predictions by Modeling Model Computation
Harshay Shah, Andrew Ilyas, Aleksander Madry
Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs
Luca Arnaboldi, Yatin Dandi, FLORENT KRZAKALA et al.
Asymptotics of feature learning in two-layer networks after one gradient-step
Hugo Cui, Luca Pesce, Yatin Dandi et al.
Initial Guessing Bias: How Untrained Networks Favor Some Classes
Emanuele Francazi, Aurelien Lucchi, Marco Baity-Jesi
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Dongyoung Lim, Sotirios Sabanis
Taylor Videos for Action Recognition
Lei Wang, Xiuyuan Yuan, Tom Gedeon et al.
On Statistical Learning Theory for Distributional Inputs
Christian Fiedler, Pierre-François Massiani, Friedrich Solowjow et al.
Structure-based drug design by denoising voxel grids
Pedro O. Pinheiro, Arian Jamasb, Omar Mahmood et al.
On the Second-Order Convergence of Biased Policy Gradient Algorithms
Siqiao Mu, Diego Klabjan
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs et al.
A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation
Yosra MARNISSI, Maxime Leiber
Non-parametric Online Change Point Detection on Riemannian Manifolds
Xiuheng Wang, Ricardo Borsoi, Cédric Richard
DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
Chenfeng Miao, Qingying Zhu, Chen Minchuan et al.
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization
Yang Jin, Zhicheng Sun, Kun Xu et al.
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
Tianshu Chu, Dachuan Xu, Wei Yao et al.
Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation
Kui Zhang, Hang Zhou, Jie Zhang et al.
Emergent Equivariance in Deep Ensembles
Jan Gerken, Pan Kessel
Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sebastian Kairat, Sander Bohte et al.
GFlowNet Training by Policy Gradients
Puhua Niu, Shili Wu, Mingzhou Fan et al.
Logistic Variational Bayes Revisited
Michael Komodromos, Marina Evangelou, Sarah Filippi
Cross-domain Open-world Discovery
Shuo Wen, Maria Brbic
Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
Yuxing Liu, Lesi Chen, Luo Luo
Fewer Truncations Improve Language Modeling
Hantian Ding, Zijian Wang, Giovanni Paolini et al.
Nesting Particle Filters for Experimental Design in Dynamical Systems
Sahel Iqbal, Adrien Corenflos, Simo Särkkä et al.
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
Tenglong Liu, Yang Li, Yixing Lan et al.
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
Luca Masserano, Alexander Shen, Michele Doro et al.
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
Taehong Moon, Moonseok Choi, EungGu Yun et al.
Beyond the Norms: Detecting Prediction Errors in Regression Models
Andres Altieri, Marco Romanelli, Georg Pichler et al.
Improving Open-Ended Text Generation via Adaptive Decoding
Wenhong Zhu, Hongkun Hao, Zhiwei He et al.
Overcoming the Optimizer's Curse: Obtaining Realistic Prescriptions from Neural Networks
Asterios Tsiourvas, Georgia Perakis
Causal Inference out of Control: Estimating Performativity without Treatment Randomization
Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
Differentiability and Optimization of Multiparameter Persistent Homology
Luis Scoccola, Siddharth Setlur, David Loiseaux et al.
Hybrid Inverse Reinforcement Learning
Juntao Ren, Gokul Swamy, Steven Wu et al.
Cooperative Graph Neural Networks
Ben Finkelshtein, Xingyue Huang, Michael Bronstein et al.
Generalization to New Sequential Decision Making Tasks with In-Context Learning
Sharath Chandra Raparthy, Eric Hambro, Robert Kirk et al.
Latent Space Symmetry Discovery
Jianke Yang, Nima Dehmamy, Robin Walters et al.
Leverage Class-Specific Accuracy to Guide Data Generation for Improving Image Classification
Jay Gala, Pengtao Xie
Data-free Neural Representation Compression with Riemannian Neural Dynamics
Zhengqi Pei, Anran Zhang, Shuhui Wang et al.
FlowMM: Generating Materials with Riemannian Flow Matching
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram et al.
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar Shalom, Beatrice Bevilacqua, Haggai Maron
Efficient Exploration for LLMs
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao et al.
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.
Amortizing Pragmatic Program Synthesis with Rankings
Yewen Pu, Saujas Vaduguru, Priyan Vaithilingam et al.
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi et al.
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
Siddartha Devic, Aleksandra Korolova, David Kempe et al.
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Jiaqi Zhai, Yunxing Liao, Xing Liu et al.
Transitional Uncertainty with Layered Intermediate Predictions
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Hengyi Wang, Shiwei Tan, Hao Wang
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Zhenlong Liu, Lei Feng, HUIPING ZHUANG et al.
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
Jiarong Pan, Stefan Falkner, Felix Berkenkamp et al.
A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights
Davide Legacci, Panayotis Mertikopoulos, Bary Pradelski
Learning and Forgetting Unsafe Examples in Large Language Models
Jiachen Zhao, Zhun Deng, David Madras et al.
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
Federico Bianchi, Patrick John Chia, Mert Yuksekgonul et al.
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Haowei Lin, Baizhou Huang, Haotian Ye et al.
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels
Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation
Lujie Yang, Hongkai Dai, Zhouxing Shi et al.
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
Zhening Li, Gabriel Poesia, Armando Solar-Lezama
Assessing Large Language Models on Climate Information
Jannis Bulian, Mike Schäfer, Afra Amini et al.
The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal et al.
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
Reweighted Solutions for Weighted Low Rank Approximation
David Woodruff, Taisuke Yasuda
Coresets for Multiple $\ell_p$ Regression
David Woodruff, Taisuke Yasuda
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
Rico Angell, Andrew McCallum
Model-Free Robust $\phi$-Divergence Reinforcement Learning Using Both Offline and Online Data
Kishan Panaganti, Adam Wierman, Eric Mazumdar
WAVES: Benchmarking the Robustness of Image Watermarks
Bang An, Mucong Ding, Tahseen Rabbani et al.
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
Tianci Liu, Haoyu Wang, Shiyang Wang et al.
Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller et al.
Editing Partially Observable Networks via Graph Diffusion Models
Puja Trivedi, Ryan A Rossi, David Arbour et al.
Meta Evidential Transformer for Few-Shot Open-Set Recognition
Hitesh Sapkota, Krishna Neupane, Qi Yu
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim et al.
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
Mo Yu, Qiujing Wang, Shunchi Zhang et al.
Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
Edwin Zhang, Sadie Zhao, Tonghan Wang et al.
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Sikha Pentyala, Mayana Pereira, Martine De Cock
Grokking Group Multiplication with Cosets
Dashiell Stander, Qinan Yu, Honglu Fan et al.
Augmenting Decision with Hypothesis in Reinforcement Learning
Nguyen Minh Quang, Hady Lauw
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Haozheng Luo et al.
High-Dimensional Geometric Streaming for Nearly Low Rank Data
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni et al.
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger et al.
Contrastive Predict-and-Search for Mixed Integer Linear Programs
Taoan Huang, Aaron Ferber, Arman Zharmagambetov et al.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng et al.
AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training
Li Ding, Wen Fei, Yuyang Huang et al.
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
Prajwal K R, Bowen Shi, Matthew Le et al.
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
Hao Hu, yiqin yang, Jianing Ye et al.
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni et al.
Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
Andries Smit, Nathan Grinsztajn, Paul Duckworth et al.
Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen et al.
Listening to the noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles Margossian, Ruben Ohana et al.
Larimar: Large Language Models with Episodic Memory Control
Payel Das, Subhajit Chaudhury, Elliot Nelson et al.
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
Ching-Yun (Irene) Ko, Pin-Yu Chen, Payel Das et al.
Matrix Information Theory for Self-Supervised Learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang et al.
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein et al.
Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski et al.
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al.
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Andrey Bryutkin, Jiahao Huang, Zhongying Deng et al.
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
Evaluation of Trajectory Distribution Predictions with Energy Score
Novin Shahroudi, Mihkel Lepson, Meelis Kull
Stochastic positional embeddings improve masked image modeling
Amir Bar, Florian Bordes, Assaf Shocher et al.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang et al.
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre et al.
Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images
Jun-Peng Jiang, Han-Jia Ye, Leye Wang et al.
Dynamic Facility Location in High Dimensional Euclidean Spaces
Sayan Bhattacharya, Gramoz Goranci, Shaofeng Jiang et al.
Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization
Xingyi Zhao, Depeng Xu, Shuhan Yuan
Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
Shenzhi Yang, Bin Liang, An Liu et al.
Deconstructing the Goldilocks Zone of Neural Network Initialization
Artem Vysogorets, Anna Dawid, Julia Kempe
Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping
Ben Lonnqvist, Zhengqing Wu, Michael Herzog
Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
Yajie Bao, Michael Crawshaw, Mingrui Liu
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
Thomas Pouplin, Alan Jeffares, Nabeel Seedat et al.
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil et al.
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil et al.
Conditional Language Learning with Context
Xiao Zhang, Miao Li, Ji Wu
Getting the most out of your tokenizer for pre-training and domain adaptation
Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere
Optimization without Retraction on the Random Generalized Stiefel Manifold
Simon Vary, Pierre Ablin, Bin Gao et al.
Representing Molecules as Random Walks Over Interpretable Grammars
Michael Sun, Minghao Guo, Weize Yuan et al.
Towards a Better Theoretical Understanding of Independent Subnetwork Training
Egor Shulgin, Peter Richtarik
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Yahong Yang, Juncai He
Towards a Self-contained Data-driven Global Weather Forecasting Framework
Yi Xiao, LEI BAI, Wei Xue et al.
Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
Haitong LIU, Qiang Li, Hoi To Wai
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim et al.
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu et al.
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang et al.
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Utsav Singh, Wesley A. Suttle, Brian Sadler et al.
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel et al.
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
Andrew C. Cullen, Shijie Liu, Paul Montague et al.
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
Amine Ouasfi, Adnane Boukhayma
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
Sudarshan Babu, Richard Liu, Zi Yu Zhou et al.
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
Active Ranking and Matchmaking, with Perfect Matchings
Hafedh El Ferchichi, Matthieu LERASLE, Vianney Perchet
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu, Idan Attias, Daniel Roy
Towards Neural Architecture Search through Hierarchical Generative Modeling
Lichuan Xiang, Łukasz Dudziak, Mohamed Abdelfattah et al.
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Jordan Dotzel, Yuzong Chen, Bahaa Kotb et al.
Amortized Variational Deep Kernel Learning
Alan Matias, César Lincoln Mattos, Joao Paulo Gomes et al.
SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP
Subhojyoti Mukherjee, Josiah Hanna, Robert Nowak
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Alex Tamkin, Mohammad Taufeeque, Noah Goodman
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
Graph Out-of-Distribution Detection Goes Neighborhood Shaping
Tianyi Bao, Qitian Wu, Zetian Jiang et al.