yusuke1997/dblp-id-title huggingface dataset:
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
Contrastive Predict-and-Search for Mixed Integer Linear Programs
Explaining Reinforcement Learning with Shapley Values
Online Differentially Private Conformal Prediction for Uncertainty Quantification
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Discriminative Feature Selection via Multiclass Variable Memory Markov Model
Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Geometric Clifford Algebra Networks
On the Impact of Algorithmic Recourse on Social Segregation
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
Estimating Maximum Expected Value through Gaussian Approximation
Weight-covariance alignment for adversarially robust neural networks
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
A Neural Network Approach to Constructive Induction
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning
Universal Equivariant Multilayer Perceptrons
On multi-view feature learning
Initial Guessing Bias: How Untrained Networks Favor Some Classes
A Comparison Study to Detect COVID-19 Chest X-Ray Images with SOTA Deep Learning Models
GMAIL: Generative Modality Alignment for generated Image Learning
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
A Clockwork RNN
Making Paper Reviewing Robust to Bid Manipulation Attacks
Primitive Vision: Improving Diagram Understanding in MLLMs
Bayesian Weight Enhancement with Steady-State Adaptation for Test-time Adaptation in Dynamic Environments
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
The Greedy Miser: Learning under Test-time Budgets
System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Mixture of Hidden-Dimensions: Not All Hidden-States' Dimensions are Needed in Transformer
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Solving POMDPs with Levin Search and EIRA
Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Natural Image Bases to Represent Neuroimaging Data
Gromov-Wasserstein Averaging of Kernel and Distance Matrices
Sub-Sequential Physics-Informed Learning with State Space Model
Tree-Sliced Wasserstein Distance: A Geometric Perspective
Tool Unlearning for Tool-Augmented LLMs
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
Provably Correct Optimization and Exploration with Non-linear Policies
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Proto-value functions: developmental reinforcement learning
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
Learning to Continually Learn with the Bayesian Principle
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
Model-based Hierarchical Average-reward Reinforcement Learning
Bayesian Optimization of Function Networks with Partial Evaluations
T-Basis: a Compact Representation for Neural Networks
A Generalization Result for Convergence in Learning-to-Optimize
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Lego Sketch: A Scalable Memory-augmented Neural Network for Sketching Data Streams
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
SAE-V: Interpreting Multimodal Models for Enhanced Alignment
Better depth-width trade-offs for neural networks through the lens of dynamical systems
Centralized Selection with Preferences in the Presence of Biases
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Multi-agent Online Scheduling: MMS Allocations for Indivisible Items
Understanding self-supervised learning dynamics without contrastive pairs
Piecewise Linear Regression via a Difference of Convex Functions
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation
A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC
Neural-Kernel Conditional Mean Embeddings
The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination
No-Regret and Incentive-Compatible Online Learning
Repeat After Me: Transformers are Better than State Space Models at Copying
Coherent Probabilistic Forecasts for Hierarchical Time Series
MyoDex: A Generalizable Prior for Dexterous Manipulation
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms
On the value of pairwise constraints in classification and consistency
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Composing Value Functions in Reinforcement Learning
Differentiable Dynamic Normalization for Learning Deep Representation
Prediction-powered Generalization of Causal Inferences
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Learning Mixtures of Plackett-Luce Models
Efficient and Separate Authentication Image Steganography Network
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
Projected Tensor Power Method for Hypergraph Community Recovery
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Using Linear-threshold Algorithms to Combine Multi-class Sub-experts
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
Accurate max-margin training for structured output spaces
Spherical Structured Feature Maps for Kernel Approximation
Gradient Flow Provably Learns Robust Classifiers for Orthonormal GMMs
Error bounds for correlation clustering
AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses
A theory of representation learning gives a deep generalisation of kernel methods
Large-scale RLSC learning without agony
Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
On hyperparameter tuning in general clustering problemsm
Dynamic Word Embeddings
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries
Rethinking the Temperature for Federated Heterogeneous Distillation
Multiple kernel learning, conic duality, and the SMO algorithm
Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training
Towards Understanding and Reducing Graph Structural Noise for GNNs
KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss
Rethinking Confidence Scores and Thresholds in Pseudolabeling-based SSL
Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
On Recovering from Modeling Errors Using Testing Bayesian Networks
Free-Form Variational Inference for Gaussian Process State-Space Models
AffinityFlow: Guided Flows for Antibody Affinity Maturation
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction
Multi-Agent Best Arm Identification with Private Communications
Locally Linear Support Vector Machines
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
Lossless Compression of Efficient Private Local Randomizers
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation
Online Convex Programming and Generalized Infinitesimal Gradient Ascent
LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data
A Simple yet Universal Strategy for Online Convex Optimization
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
Accessibility by Public Transport Predicts Residential Real Estate Prices: A Case Study in Helsinki Region
Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM
Efficient Mixture Learning in Black-Box Variational Inference
On Local Regret
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach
Inference in a Partially Observed Queuing Model with Applications in Ecology
Adaptive Sparsity in Gaussian Graphical Models
Conceptual Clustering with Systematic Missing Values
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Quantum Algorithms for Finite-horizon Markov Decision Processes
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
The information-theoretic value of unlabeled data in semi-supervised learning
Generalized low rank approximations of matrices
Potemkin Understanding in Large Language Models
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
Learning Search Control Knowledge for Deep Space Network Scheduling
Distributed Gaussian Processes
A Loss Function Analysis for Classification Methods in Text Categorization
tinyBenchmarks: evaluating LLMs with fewer examples
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Particle Transformer for Jet Tagging
Optimal Differential Privacy Composition for Exponential Mechanisms
A ranking approach to global optimization
BounDr.E: Predicting Drug-likeness via Biomedical Knowledge Alignment and EM-like One-Class Boundary Optimization
Achieving Efficient and Cognitively Plausible Learning in Backgammon
Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Self-supervised Models are Good Teaching Assistants for Vision Transformers
On the Size of the Online Kernel Sparsification Dictionary
GNNs Also Deserve Editing, and They Need It More Than Once
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Parameter-Efficient Transfer Learning for NLP
Collaborative Evolutionary Reinforcement Learning
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
A Neural Network Model for Prognostic Prediction
Debiasing a First-order Heuristic for Approximate Bi-level Optimization
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
RoboDreamer: Learning Compositional World Models for Robot Imagination
Provably Invariant Learning without Domain Information
MultiMax: Sparse and Multi-Modal Attention Learning
Exploring Chemical Space with Score-based Out-of-distribution Generation
Persistent Topological Features in Large Language Models
Loss-Proportional Subsampling for Subsequent ERM
Machine Learning and Information Retrieval (Abstract)
Secure Quantized Training for Deep Learning
Behavior Contrastive Learning for Unsupervised Skill Discovery
A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025
Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem
Knowledge Refinement Using a High Level, Non-Technical Vocabulary
A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
Multiclass Boosting with Hinge Loss based on Output Coding
FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting
Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process
Distilling Internet-Scale Vision-Language Models into Embodied Agents
DEALing with Image Reconstruction: Deep Attentive Least Squares
Infinite mixtures of trees
Better Learners Use Analogical Problem Solving Sparingly
Metric-Fair Active Learning
AdaXpert: Adapting Neural Architecture for Growing Data
Reinforced Learning Explicit Circuit Representations for Quantum State Characterization from Local Measurements
PU Learning for Matrix Completion
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Coding for Random Projections
An Integer Linear Programming Framework for Mining Constraints from Data
Feature Selection via Probabilistic Outputs
Circumventing Backdoor Space via Weight Symmetry
Flow-based Recurrent Belief State Learning for POMDPs
Learning Similarity Metrics for Numerical Simulations
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Spectral Learning of Hidden Markov Models from Dynamic and Static Data
Predictive Coding beyond Correlations
Interpretability Illusions in the Generalization of Simplified Models
A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks
No more pesky learning rates
A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions
Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs
Phase Transitions, Distance Functions, and Implicit Neural Representations
The Generalized Skew Spectrum of Graphs
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Magneto: A Foundation Transformer
Rectified Linear Units Improve Restricted Boltzmann Machines
Zero-Shot Offline Imitation Learning via Optimal Transport
Structural Inference of Hierarchies in Networks
Active Covering
Gradient-Free Method for Heavily Constrained Nonconvex Optimization
Weakly-Supervised Contrastive Learning for Imprecise Class Labels
Ideal Theory Refinement under Object Identity
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Making Deep Q-learning methods robust to time discretization
Combining Error-Driven Pruning and Classification for Partial Parsing
Rényi Neural Processes
ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models
Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs
Self Normalizing Flows
Near-linear time Gaussian process optimization with adaptive batching and resparsification
Failures of Gradient-Based Deep Learning
A Recipe for Causal Graph Regression: Confounding Effects Revisited
Discriminative unsupervised learning of structured predictors
Expectation-Complete Graph Representations with Homomorphisms
Conditional Topic Random Fields
Learning to Rehearse in Long Sequence Memorization
Nonparametric Score Estimators
Parsing epileptic events using a Markov switching process model for correlated time series
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Learning Structured Concepts Using Genetic Algorithms
Adversarial Robustness via Runtime Masking and Cleansing
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models
PINNsAgent: Automated PDE Surrogation with Large Language Models
Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective
Cold-start Active Learning with Robust Ordinal Matrix Factorization
A Langevin-like Sampler for Discrete Distributions
An investigation of computational and informational limits in Gaussian mixture clustering
Layered Sampling for Robust Optimization Problems
BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges
Memorization Sinks: Isolating Memorization during LLM Training
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation
How Interpretable Are Interpretable Graph Neural Networks?
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
COSDA: Counterfactual-based Susceptibility Risk Framework for Open-Set Domain Adaptation
On the Optimality of Misspecified Kernel Ridge Regression
Safe and Robust Subgame Exploitation in Imperfect Information Games
Active Detection via Adaptive Submodularity
Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain
Model-agnostic Measure of Generalization Difficulty
Adaptively Learning to Select-Rank in Online Platforms
Interpretable Deep Clustering for Tabular Data
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
Convex Calibrated Surrogates for the Multi-Label F-Measure
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data
Dynamic Balancing for Model Selection in Bandits and RL
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications
Efficient RL via Disentangled Environment and Agent Representations
Preference learning with Gaussian processes
BDC-CLIP: Brownian Distance Covariance for Adapting CLIP to Action Recognition
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation
Dynamic Similarity Graph Construction with Kernel Density Estimation
Temporal Poisson Square Root Graphical Models
DCBM: Data-Efficient Visual Concept Bottleneck Models
InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
Generalization Error of Graph Neural Networks in the Mean-field Regime
MODeL: Memory Optimizations for Deep Learning
Controllable Data Generation with Hierarchical Neural Representations
Graph Neural Networks Inspired by Classical Iterative Algorithms
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
On Numerical Integration in Neural Ordinary Differential Equations
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
Learning Markov logic network structure via hypergraph lifting
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Hindering Adversarial Attacks with Implicit Neural Representations
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
Enhancing Implicit Shape Generators Using Topological Regularizations
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Efficient inference on sequence segmentation models
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Full Bayesian network classifiers
Accelerating PDE Data Generation via Differential Operator Action in Solution Space
Pausing Policy Learning in Non-stationary Reinforcement Learning
WL meet VC
Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Discovering Functional Communities in Dynamical Networks
L-Diffusion: Laplace Diffusion for Efficient Pathology Image Segmentation
Making Convolutional Networks Shift-Invariant Again
Using Transitional Proximity for Faster Reinforcement Learning
Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources
Minimax Pareto Fairness: A Multi Objective Perspective
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Roping in Uncertainty: Robustness and Regularization in Markov Games
Fast classification using sparse decision DAGs
A Rate-Distortion Framework for Explaining Black-Box Model Decisions
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Low-Dimension-to-High-Dimension Generalization and Its Implications for Length Generalization
Sketching Algorithms and Lower Bounds for Ridge Regression
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
Using Pre-Training Can Improve Model Robustness and Uncertainty
A Closer Look at the Limitations of Instruction Tuning
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
Constrained Efficient Global Optimization of Expensive Black-box Functions
Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling
Refining Domain Theories Expressed as Finite-State Automata
Model Assessment and Selection under Temporal Distribution Shift
Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant
A Birth-Death Process for Feature Allocation
Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration
Scalable Private Partition Selection via Adaptive Weighting
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Bifurcate then Alienate: Incomplete Multi-view Clustering via Coupled Distribution Learning with Linear Overhead
Gibbs Sampling of Continuous Potentials on a Quantum Computer
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Estimating the Generalization Performance of an SVM Efficiently
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Revisiting the Predictability of Performative, Social Events
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction
Provable Reset-free Reinforcement Learning by No-Regret Reduction
Provable Self-Play Algorithms for Competitive Reinforcement Learning
GFlowNet-EM for Learning Compositional Latent Variable Models
Operator SVD with Neural Networks via Nested Low-Rank Approximation
PAC-Learning for Strategic Classification
Learning a kernel matrix for nonlinear dimensionality reduction
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples