yusuke1997/dblp-id-title huggingface dataset:
Using Reinforcement Learning to Spider the Web Efficiently
Robust Probabilistic Modeling with Bayesian Data Reweighting
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Discriminative Dimensionality Reduction for Patch-Based Label Fusion
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Distributed Weighted Matching via Randomized Composable Coresets
Sample Complexity of Robust Linear Classification on Separated Data
RLlib: Abstractions for Distributed Reinforcement Learning
Adaptive Overrelaxed Bound Optimization Methods
Gaussian Process Change Point Models
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
Sublinear-Time Clustering Oracle for Signed Graphs
Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
Versatile Verification of Tree Ensembles
Learning low-rank kernel matrices
Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders
Bayesian inference on random simple graphs with power law degree distributions
Learning With Bayesian Networks (Abstract)
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Permutation Search of Tensor Network Structures via Local Sampling
Convexified Convolutional Neural Networks
Online Pre-Training for Offline-to-Online Reinforcement Learning
CUR Algorithm for Partially Observed Matrices
On Implicit Regularization in β-VAEs
Gaussian Process Kernels for Pattern Discovery and Extrapolation
Sparse Parameter Recovery from Aggregated Data
High-Performance Large-Scale Image Recognition Without Normalization
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Faster Adaptive Decentralized Learning Algorithms
Iterative Vectors: In-Context Gradient Steering without Backpropagation
Exogenous Isomorphism for Counterfactual Identifiability
Confidence-Aware Learning for Deep Neural Networks
Low-Cost High-Power Membership Inference Attacks
A Robust Test for the Stationarity Assumption in Sequential Decision Making
Approximate Principal Direction Trees
A unifying framework for vector-valued manifold regularization and multi-view learning
Time-aware Large Kernel Convolutions
Federated Neuro-Symbolic Learning
Probabilistic Matrix Addition
Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion
Communication complexity as a lower bound for learning in games
Feature Noise Induces Loss Discrepancy Across Groups
A model for handling approximate, noisy or incomplete labeling in text classification
Parametric Scaling Law of Tuning Bias in Conformal Prediction
Polyhedral classifier for target detection: a case study: colorectal cancer
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
Softened Approximate Policy Iteration for Markov Games
How to Solve Fair k-Center in Massive Data Models
Principled Gradient-Based MCMC for Conditional Sampling of Text
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
High-dimensional Experimental Design and Kernel Bandits
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results
Towards credible visual model interpretation with path attribution
Forecastable Component Analysis
Policy Evaluation for Variance in Average Reward Reinforcement Learning
MATS: An Audio Language Model under Text-only Supervision
Near-optimal Regret Bounds for Stochastic Shortest Path
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
Communication-Constrained Inference and the Role of Shared Randomness
Using Learning to Recover Side-Effects of Operators in Robotics
Learning with 3D rotations, a hitchhiker's guide to SO(3)
An Online Boosting Algorithm with Theoretical Justifications
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
High Dynamic Range Novel View Synthesis with Single Exposure
Ladder-Residual: Parallelism-Aware Architecture for Accelerating Large Model Inference with Communication Overlapping
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm
Binary action search for learning continuous-action control policies
Bag of Tricks for Training Data Extraction from Language Models
Causal Inference from Competing Treatments
Large-Scale Learning with Less RAM via Randomization
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Hierarchical Neural Coding for Controllable CAD Model Generation
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Language Models as Science Tutors
Classification Using Phi-Machines and Constructive Function Approximation
BayesNAS: A Bayesian Approach for Neural Architecture Search
One-sided Frank-Wolfe algorithms for saddle problems
Training Neural Networks for and by Interpolation
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Generalized Polyak Step Size for First Order Optimization with Momentum
DIVISION: Memory Efficient Training via Dual Activation Precision
Dichotomous Optimistic Search to Quantify Human Perception
Statistical Inference Under Constrained Selection Bias
Black-Box Tuning for Language-Model-as-a-Service
Streaming Algorithms for High-Dimensional Robust Statistics
Boosted Histogram Transform for Regression
Expected Mistake Bound Model for On-Line Reinforcement Learning
LAMDA: Label Matching Deep Domain Adaptation
Mixed Cross Entropy Loss for Neural Machine Translation
Fast Combinatorial Algorithms for Min Max Correlation Clustering
Three Operator Splitting with a Nonconvex Loss Function
Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals
Fast Semidifferential-based Submodular Function Optimization
Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning
Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
Robust Induction of Process Models from Time-Series Data
The Evolution of Gennetic Algorithms: Towards Massive Parallelism
Random Feature Expansions for Deep Gaussian Processes
Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
Hilbert space embeddings of conditional distributions with applications to dynamical systems
SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
A simpler unified analysis of budget perceptrons
DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation
Tighter Variational Representations of f-Divergences via Restriction to Probability Measures
Diffusion Instruction Tuning
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines
Boosting for Control of Dynamical Systems
Patch-level Contrastive Learning via Positional Query for Visual Pre-training
Evaluating Bayesian Models with Posterior Dispersion Indices
Adaptive Elicitation of Latent Information Using Natural Language
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games
Loss Function Search for Face Recognition
Editable Concept Bottleneck Models
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Multi-Modal Classifiers for Open-Vocabulary Object Detection
Disentangling syntax and semantics in the brain with deep networks
K Nearest Neighbor Classification on Feature Projections
Bandits with Delayed, Aggregated Anonymous Feedback
Online AUC Maximization
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
A Reinforcement Learning Method for Maximizing Undiscounted Rewards
FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference
Boosted Density Estimation Remastered
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Posterior Sampling for Deep Reinforcement Learning
Adaptive Accompaniment with ReaLchords
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
The FAST Algorithm for Submodular Maximization
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset
Applying the Multiple Cause Mixture Model to Text Categorization
Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
Smoothness and Structure Learning by Proxy
Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
Stable Fair Graph Representation Learning with Lipschitz Constraint
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
GEFA: A General Feature Attribution Framework Using Proxy Gradient Estimation
Training Deep Surrogate Models with Large Scale Online Learning
LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models
Learning to Filter with Predictive State Inference Machines
Efficient Graph Field Integrators Meet Point Clouds
On Relativistic f-Divergences
Constrained Reinforcement Learning Under Model Mismatch
Unified Analysis of Continuous Weak Features Learning with Applications to Learning from Missing Data
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
Tight Bounds for Approximate Carathéodory and Beyond
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Workshop summary: Sparse methods for music audio
Expand-and-Cluster: Parameter Recovery of Neural Networks
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
Mitigating Label Noise on Graphs via Topological Sample Selection
Meaningfully debugging model mistakes using conceptual counterfactual explanations
Separable value functions across time-scales
Large Scale Max-Margin Multi-Label Classification with Priors
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-Divergence
Streaming Algorithms for Support-Aware Histograms
Sparse Additive Functional and Kernel CCA
Flexibility-conditioned protein structure design with flow matching
Homophily Enhanced Graph Domain Adaptation
MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement
Applying the Waek Learning Framework to Understand and Improve C4.5
Set Valued Predictions For Robust Domain Generalization
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
Rethinking Score Distilling Sampling for 3D Editing and Generation
Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence
On partial optimality in multi-label MRFs
Efficient Quantification of Multimodal Interaction at Sample Level
Minimal Achievable Sufficient Statistic Learning
Anchor Sampling for Federated Learning with Partial Client Participation
Efficient Training of Language Models using Few-Shot Learning
Fine-Grained Captioning of Long Videos through Scene Graph Consolidation
Error Correction in Constructive Induction
Distributional Bellman Operators over Mean Embeddings
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Reinforcement Learning in Factories: The Auton Project (Abstract)
Ehrenfeucht-Haussler Rank and Chain of Thought
Efficient Skill Discovery via Regret-Aware Optimization
Information Geometry and Minimum Description Length Networks
π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation
Learning to impersonate
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Interpreting Generative Adversarial Networks for Interactive Image Generation
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
Second-Order Optimization with Lazy Hessians
Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene Reconstruction
Improved, Deterministic Smoothing for L 1 Certified Robustness
Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes
Recommendations as Treatments: Debiasing Learning and Evaluation
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
Support Matrix Machines
Sequential Nonparametric Regression
Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach
SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
Analogy Access by Mapping Spreading and Abstraction in Large, Multifunctional Knowledge Bases
Linear bandits with Stochastic Delayed Feedback
Predictive Flows for Faster Ford-Fulkerson
Discriminative Gaussian process latent variable model for classification
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series
Fast Co-Training under Weak Dependence via Stream-Based Active Learning
Approximation and non-parametric estimation of ResNet-type convolutional neural networks
Convex Max-Product over Compact Sets for Protein Folding
Reinforcement Learning in POMDP's via Direct Gradient Ascent
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring
Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision
Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Reward-Guided Speculative Decoding for Efficient LLM Reasoning
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Robust Principal Component Analysis with Complex Noise
Learning with Augmented Features for Heterogeneous Domain Adaptation
Functional Transparency for Structured Data: a Game-Theoretic Approach
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Multimodal Deep Learning
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
Focused Concept Formation
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values
Using Inherent Structures to design Lean 2-layer RBMs
Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
Learning Deep Energy Models
An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Anytime Representation Learning
Energy-Based Flow Matching for Generating 3D Molecular Structure
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
Data as Ensembles of Records: Representation and Comparison
Differentiable Structure Learning with Ancestral Constraints
Logistic Regression for Massive Data with Rare Events
Sparse Greedy Matrix Approximation for Machine Learning
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data
DSBRouter: End-to-end Global Routing via Diffusion Schr\"{o}dinger Bridge
LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
Finding Galaxies in the Shadows of Quasars with Gaussian Processes
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines
Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Estimating Information Flow in Deep Neural Networks
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
Toward Controlled Generation of Text
Aligning Spoken Dialogue Models from User Interactions
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
Equivariant Transformer Networks
Fair Generative Modeling via Weak Supervision
Winnowing subspaces
Solving High-Dimensional PDEs with Latent Spectral Models
Fictitious Self-Play in Extensive-Form Games
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
TPC: Transformation-Specific Smoothing for Point Cloud Models
Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
Learning Recursive Relations with Randomly Selected Small Training Sets
CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources
Token Coordinated Prompt Attention is Needed for Visual Prompting
Aligning Transformers with Weisfeiler-Leman
Online Learning with Random Representations
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Plug-in Performative Optimization
Data Poisoning Attacks in Multi-Party Learning
Version Space Algebra and its Application to Programming by Demonstration
Heads up! Large Language Models Can Perform Tasks Without Your Instruction via Selective Attention Head Masking
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Image Hijacks: Adversarial Images can Control Generative Models at Runtime
On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
Apprenticeship Learning About Multiple Intentions
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models
Sparse stochastic inference for latent Dirichlet allocation
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
An Analytical Update Rule for General Policy Optimization
QuanONet: Quantum Neural Operator with Application to Differential Equation
Bidirectional Learning for Offline Model-based Biological Sequence Design
The Role of Deconfounding in Meta-learning
Trompt: Towards a Better Deep Neural Network for Tabular Data
CLLMs: Consistency Large Language Models
HexaGAN: Generative Adversarial Nets for Real World Classification
A Simple and Provable Algorithm for Sparse Diagonal CCA
Simplex Random Features
GATE: How to Keep Out Intrusive Neighbors
Hierarchical Text Generation and Planning for Strategic Dialogue
Co-clustering through Optimal Transport
Model Transferability with Responsive Decision Subjects
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold
Large Scale Text Classification using Semisupervised Multinomial Naive Bayes
On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
Better Training using Weight-Constrained Stochastic Dynamics
AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers
Oneshot Differentially Private Top-k Selection
Revision of Reduced Theories
AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment
Online Learning in Unknown Markov Games
How does Disagreement Help Generalization against Label Corruption?
Turnstile ℓp leverage score sampling with applications
Continuous Time Bayesian Networks with Clocks
Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning
Leveraging Frequency Analysis for Deep Fake Image Recognition
Differentiable Programs with Neural Libraries
Applying Winnow to Context-Sensitive Spelling Correction
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Clustering processes
Post-selection inference with HSIC-Lasso
Online discovery of similarity mappings
A majorization-minimization algorithm for (multiple) hyperparameter learning
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams
Generating Distributional Adversarial Examples to Evade Statistical Detectors
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
Bayesian Max-margin Multi-Task Learning with Data Augmentation
A Smallest Generalization Step Strategy
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
Online kernel PCA with entropic matrix updates
LPGD: A General Framework for Backpropagation through Embedded Optimization Layers
Multidimensional Shape Constraints
Deep Network Approximation in Terms of Intrinsic Parameters
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function
A General Recipe for Likelihood-free Bayesian Optimization
A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
Branching Reinforcement Learning
Adversarial Examples Are a Natural Consequence of Test Error in Noise
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Deep learning via Hessian-free optimization
Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis
Dual Focal Loss for Calibration
BCE vs. CE in Deep Feature Learning
Semi-supervised learning for structured output variables
Optimal approximation for unconstrained non-submodular minimization
Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results
Deletion-Anticipative Data Selection with a Limited Budget
TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm
Eliminating Class Noise in Large Datasets
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
An Analysis of Rule Evaluation Metrics
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
Diffusion Posterior Sampling is Computationally Intractable
Reward-Free Kernel-Based Reinforcement Learning
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Stable and Fair Classification
WAVES: Benchmarking the Robustness of Image Watermarks
Fast Relative Entropy Coding with A* coding