yusuke1997/dblp-id-title huggingface dataset:
LookupFFN: Making Transformers Compute-lite for CPU inference
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
Hessian-Free High-Resolution Nesterov Acceleration For Sampling
Efficient nonparametric statistical inference on population feature importance using Shapley values
Improving Rationality in the Reasoning Process of Language Models through Self-playing Game
Deep Metric Learning by Exploring Confusing Triplet Embeddings for COVID-19 Medical Images Diagnosis
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
Dynamic abstraction in reinforcement learning via clustering
Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
A Sparsity Principle for Partially Observable Causal Representation Learning
When Explanations Lie: Why Many Modified BP Attributions Fail
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements
Leveraging Proxy of Training Data for Test-Time Adaptation
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
Emergent Social Learning via Multi-agent Reinforcement Learning
Towards Memorization Estimation: Fast, Formal and Free
Randomized Schur Complement Views for Graph Contrastive Learning
Incremental Concept Formation with Composite Objects
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Learning Population-Level Diffusions with Generative RNNs
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
Feedback-Based Tree Search for Reinforcement Learning
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information
Learning from Noisy Side Information by Generalized Maximum Entropy Model
Feedforward Few-shot Species Range Estimation
A Rational Analysis of Categorization
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
Discretization Drift in Two-Player Games
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification
Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets
Gradient Boosting Reinforcement Learning
Online Mechanism Design for Information Acquisition
Differentially Private Domain Adaptation with Theoretical Guarantees
A Temporal-Difference Approach to Policy Gradient Estimation
Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning
Vertical Federated Graph Neural Network for Recommender System
Automatic Shortcut Removal for Self-Supervised Representation Learning
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
PoisonBench: Assessing Language Model Vulnerability to Poisoned Preference Data
Adaptive Sensor Placement for Continuous Spaces
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing
On Scalable and Efficient Computation of Large Scale Optimal Transport
Scalable Generative Models for Multi-label Learning with Missing Labels
Information-Directed Pessimism for Offline Reinforcement Learning
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Expectation-maximization for sparse and non-negative PCA
Large Language Models are Demonstration Pre-Selectors for Themselves
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks
Classification of Text Documents Based on Minimum System Entropy
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search
Multimodal Neural Language Models
Fairness in Matching under Uncertainty
CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
PILAF: Optimal Human Preference Sampling for Reward Modeling
Training Data Subset Selection for Regression with Controlled Generalization Error
Discovering Latent Causal Graphs from Spatiotemporal Data
Learning Relational Concepts with Decision Trees
The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations
Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
Feature Construction with Version Spaces for Biochemical Applications
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
Robust Satisficing MDPs
Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Curse of High Dimensionality Issue in Transformer for Long Context Modeling
A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping
Sparse Dimensionality Reduction Revisited
Using Multidimensional Projection to Find Relations
Perfect Alignment May be Poisonous to Graph Contrastive Learning
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Using Left and Right Brains Together: Towards Vision and Language Planning
Sequential information bottleneck for finite data
Near-optimal rate of consistency for linear models with missing values
Sequential Transfer in Reinforcement Learning with a Generative Model
QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms
Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series
Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling
Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
Probabilistic Imputation for Time-series Classification with Missing Data
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions
Policy Optimization for CMDPs with Bandit Feedback: Learning Stochastic and Adversarial Constraints
Message passing with l1 penalized KL minimization
A query-optimal algorithm for finding counterfactuals
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
Asymmetry in Low-Rank Adapters of Foundation Models
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Diverse ensembles for active learning
Mechanistic Design and Scaling of Hybrid Architectures
An empirical evaluation of deep architectures on problems with many factors of variation
Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds
Stochastic PCA with 𝓁 2 and 𝓁 1 Regularization
Equivariant Polynomials for Graph Neural Networks
Approximate Policy Iteration Schemes: A Comparison
Non-convex Stochastic Composite Optimization with Polyak Momentum
A Game Theoretic Framework for Model Based Reinforcement Learning
Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Group Equivariant Fourier Neural Operators for Partial Differential Equations
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
Latent Goal Analysis for Dimension Reduction in Reinforcement Learning
Differentially Private Learning of Geometric Concepts
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
Fast Excess Risk Rates via Offset Rademacher Complexity
Generative Active Learning for Long-tailed Instance Segmentation
Improving Molecular Design by Stochastic Iterative Target Augmentation
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Group Sparse Additive Models
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Towards Understanding and Improving GFlowNet Training
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
New kernels for protein structural motif discovery and function classification
A Picture of the Space of Typical Learnable Tasks
Learning Near Optimal Policies with Low Inherent Bellman Error
Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning
Federated Learning with Positive and Unlabeled Data
Tight and Fast Bounds for Multi-Label Learning
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Collective Certified Robustness against Graph Injection Attacks
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
The Alternating Decision Tree Learning Algorithm
Clustering using Max-norm Constrained Optimization
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking
Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Feature and Parameter Selection in Stochastic Linear Bandits
Multi-Modal Object Re-identification via Sparse Mixture-of-Experts
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks
Online Curvature-Aware Replay: Leveraging 2nd Order Information for Online Continual Learning
GPTSwarm: Language Agents as Optimizable Graphs
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Deep Anomaly Detection under Labeling Budget Constraints
Hierarchical VAEs Know What They Don't Know
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Revisiting Cooperative Off-Policy Multi-Agent Reinforcement Learning
On the Calibration of Human Pose Estimation
InfoOT: Information Maximizing Optimal Transport
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Screening Hypotheses with Explicit Bias
A least squares formulation for a class of generalized eigenvalue problems in machine learning
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
Incremental Multi-Step Q-Learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Submodular Observation Selection and Information Gathering for Quadratic Models
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Learning Novel Policies For Tasks
Model-based Reinforcement Learning for Parameterized Action Spaces
Interpolation between Residual and Non-Residual Networks
Measuring the Impact of Programming Language Distribution
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Hypertext Categorization using Hyperlink Patterns and Meta Data
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Feature-map-level Online Adversarial Knowledge Distillation
Global optimality for Euclidean CCCP under Riemannian convexity
Deterministic Sparse Fourier Transform for Continuous Signals with Frequency Gap
Relational clustering by symmetric convex coding
Collaborative Channel Pruning for Deep Networks
Doubly Robust Fusion of Many Treatments for Policy Learning
Trained Random Forests Completely Reveal your Dataset
Provable Dynamic Fusion for Low-Quality Multimodal Data
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Stochastic Deep Networks
FIC-TSC: Learning Time Series Classification with Fisher Information Constraint
Discriminative Latent Variable Models for Object Detection
A Conservation Law for Generalization Performance
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Universal Value Function Approximators
General-purpose, long-context autoregressive modeling with Perceiver AR
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
Fundamental Benefit of Alternating Updates in Minimax Optimization
Cover learning for large-scale topology representation
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Selective Prompt Anchoring for Code Generation
Incremental Batch Learning
Compression, Significance, and Accuracy
Continuous Visual Autoregressive Generation via Score Maximization
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Learnability and Algorithm for Continual Learning
Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy
A Resilient Distributed Boosting Algorithm
LSB: Local Self-Balancing MCMC in Discrete Spaces
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Conditional gradient methods for stochastically constrained convex minimization
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
An analysis of graph cut size for transductive learning
Rates of Convergence for Sparse Variational Gaussian Process Regression
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal
On the Embedding Collapse when Scaling up Recommendation Models
A Divide-and-Conquer Solver for Kernel Support Vector Machines
Effectively Using Public Data in Privacy Preserving Machine Learning
Functional Regularized Least Squares Classication with Operator-valued Kernels
Neural networks trained with SGD learn distributions of increasing complexity
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time
Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality
Utilizing Experience for Improving the Tactical Manager
Regularized Langevin Dynamics for Combinatorial Optimization
Channel Importance Matters in Few-Shot Image Classification
Meta-Learning with Memory-Augmented Neural Networks
On-the-fly Rectification for Robust Large-Vocabulary Topic Inference
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
Discovering Symbolic Cognitive Models from Human and Animal Behavior
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
An Aligned Subtree Kernel for Weighted Graphs
Predictive search distributions
Learning and discovery of predictive state representations in dynamical systems with reset
H-Consistency Bounds for Pairwise Misranking Loss Surrogates
A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights
Nonparametric Identification of Latent Concepts
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation
Stratified Sampling Meets Machine Learning
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Visualizing User Model in Exploratory Search Tasks
Customizing ML Predictions for Online Algorithms
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
Why do Variational Autoencoders Really Promote Disentanglement?
An Alternate Objective Function for Markovian Fields
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization
Greedy Algorithms for Sparse Reinforcement Learning
Generalized LARS as an effective feature selection tool for text classification with SVMs
Statistical predicate invention
TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation
On Limited-Memory Subsampling Strategies for Bandits
Sample Complexity of Branch-length Estimation by Maximum Likelihood
Fairness of Exposure in Stochastic Bandits
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data
Online Bayesian Passive-Aggressive Learning
Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective
Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Variational Policy for Guiding Point Processes
Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation
Differentiable Dynamic Programming for Structured Prediction and Attention
Neural Genetic Search in Discrete Spaces
Learning to Play Atari in a World of Tokens
ReLUs Are Sufficient for Learning Implicit Neural Representations
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale
Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
Masked Generative Nested Transformers with Decode Time Scaling
Factored conditional restricted Boltzmann Machines for modeling motion style
Discovering Homogeneous Regions in Spatial Data through Competition
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
Boosting the margin: A new explanation for the effectiveness of voting methods
Differentially Private Stochastic Convex Optimization under a Quantile Loss Function
Collaborative hyperparameter tuning
The Landmark Selection Method for Multiple Output Prediction
DUET: 2D Structured and Approximately Equivariant Representations
COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
Laplacian Regularized Few-Shot Learning
Can Transformers Learn Full Bayesian Inference in Context?
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
Stochastic Optimization for Regularized Wasserstein Estimators
Scaling Test-Time Compute Without Verification or RL is Suboptimal
Why does Throwing Away Data Improve Worst-Group Error?
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Anytime-Constrained Equilibria in Polynomial Time
Fast Prediction of New Feature Utility
Variational Laplace Autoencoders
An Instrumental Value for Data Production and its Application to Data Pricing
PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction
Structural Machine Learning with Galois Lattice and Graphs
KNAS: Green Neural Architecture Search
Foundation Policies with Hilbert Representations
Boosting Offline Reinforcement Learning with Action Preference Query
Batch Reinforcement Learning with Hyperparameter Gradients
Unknown Attribute Values in Induction
Reverse-engineering deep ReLU networks
Modeling interleaved hidden processes
Sample-specific Noise Injection for Diffusion-based Adversarial Purification
Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
A theory of continuous generative flow networks
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
Spectral Regularization for Max-Margin Sequence Tagging
Variational Autoencoding Neural Operators
Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
Estimating individual treatment effect: generalization bounds and algorithms
A New PAC-Bayesian Perspective on Domain Adaptation
On Data Manifolds Entailed by Structural Causal Models
Confounder-Free Continual Learning via Recursive Feature Normalization
Robust Structural Metric Learning
Fast Multi-stage Submodular Maximization
On Convergence of Incremental Gradient for Non-convex Smooth Functions
Simplified Support Vector Decision Rules
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Top-Down Induction of Clustering Trees
Gaussian Process-Based Real-Time Learning for Safety Critical Applications
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Groupwise Constrained Reconstruction for Subspace Clustering
On the Spectrum of Random Features Maps of High Dimensional Data
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks
LETS Forecast: Learning Embedology for Time Series Forecasting
Distinguishing rule and exemplar-based generalization in learning systems
Train and Test Tightness of LP Relaxations in Structured Prediction
An analytic solution to discrete Bayesian reinforcement learning
State Relevance for Off-Policy Evaluation
Loss Landscapes of Regularized Linear Autoencoders
Fast Decision Boundary based Out-of-Distribution Detector
Correcting and Extending Domain Knowledge using Outside Guidance
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
Pruning for GNNs: Lower Complexity with Comparable Expressiveness
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Automated Synthetic-to-Real Generalization
Optimal Sets and Solution Paths of ReLU Networks
CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration
Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
Stochastic Gradient Hamiltonian Monte Carlo
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
Reducing Dueling Bandits to Cardinal Bandits
K -means clustering via principal component analysis
Constrained Interacting Submodular Groupings
MAML and ANIL Provably Learn Representations
ADIOS: Architectures Deep In Output Space
Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization
Heider vs Simmel: Emergent Features in Dynamic Structures
Agnostic Bayesian Learning of Ensembles