yusuke1997/dblp-id-title huggingface dataset:
TabSDS: a Lightweight, Fully Non-Parametric, and Model Free Approach for Generating Synthetic Tabular Data
Large Language-Geometry Model: When LLM meets Equivariance
The Margin Perceptron with Unlearning
BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization
Local Augmentation for Graph Neural Networks
Online bipartite matching with imperfect advice
The Acquisition of Physical Knowledge in Generative Neural Networks
Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Using Experience-Based Learning in Game Playing
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
Panel Discussion
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Compiling Combinatorial Prediction Games
Exploiting structured data for learning contagious diseases under incomplete testing
Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
Bayesian optimization for automated model selection
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Neuro-Symbolic Hierarchical Rule Induction
SIGNSGD: Compressed Optimisation for Non-Convex Problems
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally
A Theory of Fault-Tolerant Learning
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
Detecting non-causal artifacts in multivariate linear regression models
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
Coarticulation: an approach for generating concurrent plans in Markov decision processes
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Self-supervised Graph-level Representation Learning with Local and Global Structure
Non-Asymptotic Length Generalization
Online Stochastic Linear Optimization under One-bit Feedback
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
Recovering the Pre-Fine-Tuning Weights of Generative Models
Streaming Coresets for Symmetric Tensor Factorization
Convolutional Imputation of Matrix Networks
Evaluating Machine Accuracy on ImageNet
Dataset Distillation with Convexified Implicit Gradients
HyperTuning: Toward Adapting Large Language Models without Back-propagation
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings
AtlasD: Automatic Local Symmetry Discovery
Learning Decision Trees and Forests with Algorithmic Recourse
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Directed Graph Grammars for Sequence-based Learning
K-hyperplane Hinge-Minimax Classifier
EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
Opponent Modeling in Deep Reinforcement Learning
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Information-Theoretic Local Minima Characterization and Regularization
Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models
Unsupervised Learning of Video Representations using LSTMs
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Learning Implicit Generative Models with the Method of Learned Moments
Structured Output Learning with Indirect Supervision
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Local Pan-privacy for Federated Analytics
Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization
Combining Competition and Cooperation in Supervised Inductive Learning
Connectionist Learning of Expert Backgammon Evaluations
The Perceptron Algorithm with Uneven Margins
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
QBMK: Quantum-based Matching Kernels for Un-attributed Graphs
Rethinking Collapsed Variational Bayes Inference for LDA
Structure learning with independent non-identically distributed data
Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Efficient Exploration for LLMs
Multi-group Agnostic PAC Learnability
Variational Sparse Coding with Learned Thresholding
Unveiling the Potential of AI for Nanomaterial Morphology Prediction
Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering
Explaining Groups of Points in Low-Dimensional Representations
Label Ranking through Nonparametric Regression
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks
Learning to sportscast: a test of grounded language acquisition
Training Deep Neural Networks via Direct Loss Minimization
LowFER: Low-rank Bilinear Pooling for Link Prediction
Ordering Effects in Clustering
On Temperature Scaling and Conformal Prediction of Deep Classifiers
Categorical Feature Compression via Submodular Optimization
On Efficient Low Distortion Ultrametric Embedding
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
A Neural PDE Solver with Temporal Stencil Modeling
Learning privately from multiparty data
In defense of dual-encoders for neural ranking
Noisy Natural Gradient as Variational Inference
KDEformer: Accelerating Transformers via Kernel Density Estimation
Prototype vector machine for large scale semi-supervised learning
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
Tensor Balancing on Statistical Manifold
Tractable Bayesian Learning of Tree Augmented Naive Bayes Models
The uniqueness of a good optimum for K-means
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
Learning predictive state representations in dynamical systems without reset
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Test-Time Canonicalization by Foundation Models for Robust Perception
State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks
Learning to learn with the informative vector machine
Optimistic Policy Optimization via Multiple Importance Sampling
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Stronger Neyman Regret Guarantees for Adaptive Experimental Design
A Collective Learning Framework to Boost GNN Expressiveness for Node Classification
CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection
On the Consistency of Kernel Methods with Dependent Observations
Contextual Decision Processes with low Bellman rank are PAC-Learnable
CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
Entropy-Based Concentration Inequalities for Dependent Variables
Star Attention: Efficient LLM Inference over Long Sequences
Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000
Locally Adaptive Label Smoothing Improves Predictive Churn
Risk-Averse No-Regret Learning in Online Convex Games
Two-Stage Metric Learning
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents
Unbiased Multi-Label Learning from Crowdsourced Annotations
TPOT-RL Applied to Network Routing
Zeno++: Robust Fully Asynchronous SGD
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Towards understanding how momentum improves generalization in deep learning
A distributional view on multi-objective policy optimization
On the Robustness of Kernel Density M-Estimators
Incremental Learning in SwiftFile
Asymmetric Transfer Learning with Deep Gaussian Processes
Latent Confusion Analysis by Normalized Gamma Construction
Adversarial Reasoning at Jailbreaking Time
Deep Kernel Processes
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
Testing the significance of attribute interactions
p-Norm Flow Diffusion for Local Graph Clustering
Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation
Defining Neural Network Architecture through Polytope Structures of Datasets
Learning instance specific distances using metric propagation
Adaptive dimension reduction using discriminant analysis and K -means clustering
COALA: A Practical and Vision-Centric Federated Learning Platform
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Infinite attention: NNGP and NTK for deep attention networks
Graphically Structured Diffusion Models
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
A Prototype Based Symbolic Concept Learning System
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Understanding High-Dimensional Bayesian Optimization
Protein Structure Tokenization: Benchmarking and New Recipe
In-context Learning on Function Classes Unveiled for Transformers
Pointwise exact bootstrap distributions of cost curves
Learning Multiscale Transformer Models for Sequence Generation
Scalable Simple Random Sampling and Stratified Sampling
Bayesian Boolean Matrix Factorisation
Consistency of Online Random Forests
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations
Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation
Non-Stationary Delayed Bandits with Intermediate Observations
Signed Laplacians for Constrained Graph Clustering
Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Correlated Variational Auto-Encoders
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Inductive Relation Prediction by Subgraph Reasoning
Learning invariant features by harnessing the aperture problem
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Robustness to Spurious Correlations via Human Annotations
Ranked Entropy Minimization for Continual Test-Time Adaptation
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
Gradient Descent on Neurons and its Link to Approximate Second-order Optimization
Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning
Projections for Approximate Policy Iteration Algorithms
On the Impact of Knowledge Distillation for Model Interpretability
Understanding Finetuning for Factual Knowledge Extraction
OTMatch: Improving Semi-Supervised Learning with Optimal Transport
In-Context Reinforcement Learning for Variable Action Spaces
Training Restricted Boltzmann Machines on Word Observations
Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches
Learning with Feature and Distribution Evolvable Streams
Private frequency estimation via projective geometry
Offline Meta-Reinforcement Learning with Online Self-Supervision
On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data
Discovering Production Rules with Higher Order Neural Networks
Expected Error Analysis for Model Selection
Swept Approximate Message Passing for Sparse Estimation
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Adversarial Dueling Bandits
Near optimal finite time identification of arbitrary linear dynamical systems
Scalable Bayesian Rule Lists
First Order Generative Adversarial Networks
InstructSpeech: Following Speech Editing Instructions via Large Language Models
FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames
Discriminant Trees
Emergence of Separable Manifolds in Deep Language Representations
LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Hybrid Quantum-Classical Multi-Agent Pathfinding
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Characterizing the generalization performance of model selection strategies
Randomization matters How to defend against strong adversarial attacks
Randomized Block Cubic Newton Method
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Support cluster machine
Dynamic Egocentric Models for Citation Networks
Split variational inference
Tractable Transformers for Flexible Conditional Generation
Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
Classification from Pairwise Similarity and Unlabeled Data
Learning to Steer Learners in Games
Expressivity and Generalization: Fragment-Biases for Molecular GNNs
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Attention Networks
Searching for BurgerFormer with Micro-Meso-Macro Space Design
Machine Learning, Proceedings of the Tenth International Conference, University of Massachusetts, Amherst, MA, USA, June 27-29, 1993
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
Equivariant Architectures for Learning in Deep Weight Spaces
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
Combining Experimental and Historical Data for Policy Evaluation
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
Sequential Bayesian Search
Tightening Causal Bounds via Covariate-Aware Optimal Transport
Global Optimization Networks
Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity
Uncertainty-Aware Reward-Free Exploration with General Function Approximation
Deletion Robust Submodular Maximization over Matroids
Loss Functions and Operators Generated by f-Divergences
Optimized cutting plane algorithm for support vector machines
Learning predictive representations from a history
Boosting Graph Contrastive Learning via Graph Contrastive Saliency
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
Convergence of Consistency Model with Multistep Sampling under General Data Assumptions
Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999
A Convergence Rate Analysis for LogitBoost, MART and Their Variant
Benchmarking and Analyzing Point Cloud Classification under Corruptions
Revisiting Character-level Adversarial Attacks for Language Models
Sparse Subspace Clustering with Missing Entries
Heading in the Right Direction
Test-Time Multimodal Backdoor Detection by Contrastive Prompting
Low-Rank Similarity Mining for Multimodal Dataset Distillation
A Quantile-based Approach for Hyperparameter Transfer Learning
Consistent On-Line Off-Policy Evaluation
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
On the Benefits of Active Data Collection in Operator Learning
Multi-Task Learning as a Bargaining Game
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
Unsupervised Speech Decomposition via Triple Information Bottleneck
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Generalized Implicit Follow-The-Regularized-Leader
Improving Sharpness-Aware Minimization by Lookahead
Improved Training of Generative Adversarial Networks using Representative Features
Optimal Bounds between f-Divergences and Integral Probability Metrics
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Abstraction Selection in Model-based Reinforcement Learning
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
Building Sparse Large Margin Classifiers
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
Efficient Message Passing for 0-1 ILPs with Binary Decision Diagrams
End-to-End Learning for Structured Prediction Energy Networks
Physics-Informed Weakly Supervised Learning For Interatomic Potentials
Algorithm Development in Neural Networks: Insights from the Streaming Parity Task
Structure-Guided Large Language Models for Text-to-SQL Generation
Benign Overfitting in Deep Neural Networks under Lazy Training
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Equivariant Diffusion for Molecule Generation in 3D
Recurrent Hierarchical Topic-Guided RNN for Language Generation
Change is Hard: A Closer Look at Subpopulation Shift
Dropout distillation
Tracking Most Significant Shifts in Infinite-Armed Bandits
Random Registers for Cross-Domain Few-Shot Learning
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion
Collaborative Machine Learning with Incentive-Aware Model Rewards
What Do Learning Dynamics Reveal About Generalization in LLM Mathematical Reasoning?
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
ABC Reinforcement Learning
Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
Knowledge-Based Feature Generation
On Symmetric Losses for Learning from Corrupted Labels
Training Diffusion-based Generative Models with Limited Data
A Three-Way Model for Collective Learning on Multi-Relational Data
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI
Proceedings of the 4th Workshop on Machine Learning for Interactive Systems, MLIS 2015, co-located with the 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings
A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources
Learning in Intelligent Information Retrieval
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
WILTing Trees: Interpreting the Distance Between MPNN Embeddings
On the Second-Order Convergence of Biased Policy Gradient Algorithms
Positional Attention: Expressivity and Learnability of Algorithmic Computation
Enhancing the Plausibility of Law Equation Discovery
Evolving Structured Programs with Hierarchical Instructions and Skip Nodes
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
Online Learning in Risk Sensitive constrained MDP
Enabling Uncertainty Estimation in Iterative Neural Networks
Compositional Causal Reasoning Evaluation in Language Models
A Near Linear Query Lower Bound for Submodular Maximization
Learning to Boost Training by Periodic Nowcasting Near Future Weights
A Minimum Risk Metric for Nearest Neighbor Classification
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation
Learning in Deep Factor Graphs with Gaussian Belief Propagation
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
On Implicit Bias in Overparameterized Bilevel Optimization
Efficient Learning with Virtual Threshold Gates
Learning the Reward Function for a Misspecified Model
From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Criterion Collapse and Loss Distribution Control
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis
Modeling Images using Transformed Indian Buffet Processes
Nearly Optimal Catoni's M-estimator for Infinite Variance
Flow Matching for Denoised Social Recommendation
Position: Tensor Networks are a Valuable Asset for Green AI
Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network
Inferring Cause and Effect in the Presence of Heteroscedastic Noise
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Learning from Multiple Outlooks
Generalized Direct Change Estimation in Ising Model Structure
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers
Scale-Free Image Keypoints Using Differentiable Persistent Homology
Actively learning level-sets of composite functions
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
TRAK: Attributing Model Behavior at Scale
Variational Imitation Learning with Diverse-quality Demonstrations