yusuke1997/dblp-id-title huggingface dataset:
Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs
A bound on the label complexity of agnostic active learning
Distributed Learning with Sublinear Communication
MC-LSTM: Mass-Conserving LSTM
A General Method for Learning Idiosyncratic Grammars
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Bounds on the Generalization Performance of Kernel Machine Ensembles
Learning Compact Neural Networks with Regularization
Prototype-oriented unsupervised anomaly detection for multivariate time series
Bootstrapping Syntax and Recursion using Alginment-Based Learning
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling
Active Learning of Continuous-time Bayesian Networks through Interventions
Adaptive Learning of Decision-Theoretic Search Control Knowledge
The Hedge Algorithm on a Continuum
Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games
A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations
Manifold alignment using Procrustes analysis
CodeSync: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
Stray Intrusive Outliers-Based Feature Selection on Intra-Class Asymmetric Instance Distribution or Multiple High-Density Clusters
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
RZ-NAS: Enhancing LLM-guided Neural Architecture Search via Reflective Zero-Cost Strategy
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Memory-Based Dual Gaussian Processes for Sequential Learning
An efficient sparse metric learning in high-dimensional space via l 1 -penalized log-determinant regularization
MixMin: Finding Data Mixtures via Convex Minimization
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
A Statistical Perspective on Retrieval-Based Models
Fast Estimation of Partial Dependence Functions using Trees
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States
First-Order Theory Revision
Modeling word burstiness using the Dirichlet distribution
Invited talk: Can learning kernels help performance?
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs
Learning with structured sparsity
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
On Learning Language-Invariant Representations for Universal Machine Translation
Alternating Minimization for Mixed Linear Regression
Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference
Alternative Microfoundations for Strategic Classification
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Empirical Bernstein stopping
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
Statistical Inference and A/B Testing for First-Price Pacing Equilibria
Generalized Disparate Impact for Configurable Fairness Solutions in ML
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions
LASER: Linear Compression in Wireless Distributed Optimization
Reweighted Solutions for Weighted Low Rank Approximation
On Conditional Versus Marginal Bias in Multi-Armed Bandits
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Efficient Training of BERT by Progressively Stacking
Hessian Geometry of Latent Space in Generative Models
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
A General Representation Learning Framework with Generalization Performance Guarantees
Constrained Belief Updates Explain Geometric Structures in Transformer Representations
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
How Does Information Bottleneck Help Deep Learning?
On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
Principled Exploration via Optimistic Bootstrapping and Backward Induction
Conditional random fields for multi-agent reinforcement learning
Policy Gradients with Variance Related Risk Criteria
Variable Elimination in the Fourier Domain
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
LCANets: Lateral Competition Improves Robustness Against Corruption and Attack
ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation
Adaptive Adversarial Multi-task Representation Learning
Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking
Refining Representations to Improve Problem Solving Quality
Global Context-aware Representation Learning for Spatially Resolved Transcriptomics
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Black-Box Methods for Restoring Monotonicity
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios
Position: On the Societal Impact of Open Foundation Models
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
Strategy evaluation in extensive games with importance sampling
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Importance Sampling Tree for Large-scale Empirical Expectation
NetGAN: Generating Graphs via Random Walks
Regression on manifolds using kernel dimension reduction
Learning What to Defer for Maximum Independent Sets
Learning from Sample Stability for Deep Clustering
Unsupervised Part Representation by Flow Capsules
In-context Convergence of Transformers
Univariate Polynomial Inference by Monte Carlo Message Length Approximation
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
Conditional GANs with Auxiliary Discriminative Classifier
The Emergence of Individuality
GBHT: Gradient Boosting Histogram Transform for Density Estimation
Outlier-Robust Optimal Transport
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Towards Better-than-2 Approximation for Constrained Correlation Clustering
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization
Improving the Diffusability of Autoencoders
Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
Hierarchical Equivariant Policy via Frame Transfer
A Mathematical Model for Curriculum Learning for Parities
Adaptive Robust Learning using Latent Bernoulli Variables
Robust partially observable Markov decision process
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Fast Effective Rule Induction
Interaction-Grounded Learning
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
Adaptive Sampling Probabilities for Non-Smooth Optimization
Searching for Structure in Multiple Streams of Data
Characterizing ResNet's Universal Approximation Capability
How to Fake Multiply by a Gaussian Matrix
Stochastic bandits with arm-dependent delays
Bayesian Deep Learning via Subnetwork Inference
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
Anticorrelated Noise Injection for Improved Generalization
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach
Gradient boosting for kernelized output spaces
Beyond Reward: Offline Preference-guided Policy Optimization
Deep Sturm-Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
Error-Correcting Output Coding Corrects Bias and Variance
A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations
Continous Conceptual Set Covering: Learning Robot Operators From Examples
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
UnHiPPO: Uncertainty-aware Initialization for State Space Models
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
Learning Optimal Fair Policies
Multivariate Maximal Correlation Analysis
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
Action Dubber: Timing Audible Actions via Inflectional Flow
Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests
Gated Path Planning Networks
Minimum Regret Search for Single- and Multi-Task Optimization
Differentially Private Set Union
Machine Learning, Proceedings of the Seventh International Conference on Machine Learning, Austin, Texas, USA, June 21-23, 1990
Geodesic Distance Function Learning via Heat Flow on Vector Fields
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
An Identifiable Double VAE For Disentangled Representations
When deep denoising meets iterative phase retrieval
Discriminant kernel and regularization parameter learning via semidefinite programming
Informed Pruning in Constructive Induction
TeDS: Joint Learning of Diachronic and Synchronic Perspectives in Quaternion Space for Temporal Knowledge Graph Completion
Knowledge-aware Reinforced Language Models for Protein Directed Evolution
Guiding Pretraining in Reinforcement Learning with Large Language Models
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
Understanding Dataset Difficulty with V -Usable Information
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Contextual Linear Bandits with Delay as Payoff
A 3 T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
Bottleneck Conditional Density Estimation
RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
TtBA: Two-third Bridge Approach for Decision-Based Adversarial Attack
Robust Euclidean embedding
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
Experimental perspectives on learning from imbalanced data
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
Learning Globally Smooth Functions on Manifolds
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
Black-Box Adversarial Attacks on LLM-Based Code Completion
Do Transformer World Models Give Better Policy Gradients?
Convergence of Stochastic Gradient Descent for PCA
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Near-Optimally Teaching the Crowd to Classify
On The Fairness Impacts of Hardware Selection in Machine Learning
Policy Learning for Balancing Short-Term and Long-Term Rewards
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Thompson Sampling with Diffusion Generative Prior
Compositional Score Modeling for Simulation-Based Inference
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Space-indexed dynamic programming: learning to follow trajectories
Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010
Self-Composing Policies for Scalable Continual Reinforcement Learning
Nonparametric Estimation of Renyi Divergence and Friends
Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis
Enhancing Performance of Explainable AI Models with Constrained Concept Refinement
Faithfulness Measurable Masked Language Models
OpenFE: Automated Feature Generation with Expert-level Performance
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations
Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
How Far Can Fairness Constraints Help Recover From Biased Data?
The Weighted Kendall and High-order Kernels for Permutations
Solving Ridge Regression using Sketched Preconditioned SVRG
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
Image Fusion via Vision-Language Model
Minimal Loss Hashing for Compact Binary Codes
Focused Hierarchical RNNs for Conditional Sequence Processing
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
Learning to Weight Imperfect Demonstrations
Risk-Based Generalizations of f-divergences
Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models
Trajectory prediction: learning to map situations to robot trajectories
ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Generalization Error Bound for Hyperbolic Ordinal Embedding
DeepCoDA: personalized interpretability for compositional health data
Private Model Personalization Revisited
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer
Generalizable Episodic Memory for Deep Reinforcement Learning
A Fast Dual Algorithm for Kernel Logistic Regression
Efficient Pareto Manifold Learning with Low-Rank Structure
Graph Element Networks: adaptive, structured computation and memory
Double Machine Learning for Causal Inference under Shared-State Interference
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data
Building A Learning Bias from Perceived Dependencies
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
When are Non-Parametric Methods Robust?
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
Learning to Route Among Specialized Experts for Zero-Shot Generalization
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning
PolyGen: An Autoregressive Generative Model of 3D Meshes
Improving LLM Video Understanding with 16 Frames Per Second
Multimodal Medical Code Tokenizer
Stagewise Safe Bayesian Optimization with Gaussian Processes
On sampling-based approximate spectral decomposition
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
Graph Random Neural Features for Distance-Preserving Graph Representations
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
Federated Adversarial Learning: A Framework with Convergence Analysis
Evaluating machine learning for information extraction
Drug Discovery with Dynamic Goal-aware Fragments
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
Fast Composite Optimization and Statistical Recovery in Federated Learning
Learning Continuous Semantic Representations of Symbolic Expressions
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
Using query-specific variance estimates to combine Bayesian classifiers
LOGO - Long cOntext aliGnment via efficient preference Optimization
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
A large-scale benchmark for few-shot program induction and synthesis
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
Auto-Linear Phenomenon in Subsurface Imaging
M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution
Combining Trainig Set and Test Set Bounds
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization
Wyckoff Transformer: Generation of Symmetric Crystals
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
How to Synthesize Text Data without Model Collapse?
A choice model with infinitely many latent features
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Online multiclass learning by interclass hypothesis sharing
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Boundaries of Operationality
Scalable Approximation Algorithms for p-Wasserstein Distance and Its Variants
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data
Admixture of Poisson MRFs: A Topic Model with Word Dependencies
Banyan: Improved Representation Learning with Explicit Structure
Environment Inference for Invariant Learning
Privacy Attacks in Decentralized Learning
A Randomized ANOVA Procedure for Comparing Performance Curves
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Latent Semantic Representation Learning for Scene Classification
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
Multi-task reinforcement learning: a hierarchical Bayesian approach
A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity
Permutation Weighting
Event-based Clustering for Reducing Labeling Costs of Incident-Related Microposts
Qualitative reverse engineering
Improving Adversarial Robustness via Mutual Information Estimation
f-Divergence Based Classification: Beyond the Use of Cross-Entropy
Runtime Analysis of Evolutionary NAS for Multiclass Classification
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Learning from Opportunity
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Improved Parallel Algorithms for Density-Based Network Clustering
Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
Exploring interpretable LSTM neural networks over multi-variable data
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
The Big Data Bootstrap
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Sparse Bayesian Learning via Stepwise Regression
Learning Imperfect Information Extensive-form Games with Last-iterate Convergence under Bandit Feedback
Closed-Form Diffeomorphic Transformations for Time Series Alignment
Learning associative Markov networks
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
Meta-learning Parameterized Skills
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
NEAR: Neural Electromagnetic Array Response
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
Approximation to Smooth Functions by Low-Rank Swish Networks
Improving Gradient-Guided Nested Sampling for Posterior Inference
Online Learning with Unknown Constraints
Zero-Shot Text-to-Image Generation
Contextures: Representations from Contexts
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
Inter and Intra Topic Structure Learning with Word Embeddings
Policy Analysis using Synthetic Controls in Continuous-Time
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions
Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models
Learning Probabilistic Models of Relational Structure
Learning Parametric-Output HMMs with Two Aliased States
Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction
Lessons from Theory Revision Applied to Constructive Induction
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians
Learning Optimally Sparse Support Vector Machines
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos
MCU: An Evaluation Framework for Open-Ended Game Agents