yusuke1997/dblp-id-title huggingface dataset:
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Combining Empirical and Analytical Learning with Version Spaces
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
LASER: Attention with Exponential Transformation
Integrity Constraints and Interactive Concept-Learning
On the Convergence of Continuous Single-timescale Actor-critic
The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms
Communicating via Markov Decision Processes
Learning Distances from Data with Normalizing Flows and Score Matching
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
On the Role of Attention in Prompt-tuning
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Combining Evidence of Deep and Surface Similarity
From Weight-Based to State-Based Fine-Tuning: Further Memory Reduction on LoRA with Parallel Control
Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Training a Generally Curious Agent
Discriminative parameter learning for Bayesian networks
Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation
An efficient projection for l 1 , infinity regularization
SCoRe: Submodular Combinatorial Representation Learning
MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models
Deep Streaming View Clustering
Hypothesis testing using pairwise distances and associated kernels
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
Faster Maximum Inner Product Search in High Dimensions
ECQ x : Explainability-Driven Quantization for Low-Bit and Sparse DNNs
Copula Mixture Model for Dependency-seeking Clustering
R 1 -PCA: rotational invariant L 1 -norm principal component analysis for robust subspace factorization
Adaptive Consensus ADMM for Distributed Optimization
Rethinking the Bias of Foundation Model under Long-tailed Distribution
Implicit Subgraph Neural Network
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Incremental Basis Construction from Temporal Difference Error
FOCUS: Familiar Objects in Common and Uncommon Settings
Sub-unification: A Tool for Efficient Induction of Recursive Programs
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Adaptive Region-Based Active Learning
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
Learning and Memorization
Multi-Class Optimal Margin Distribution Machine
A Unified Approach to Routing and Cascading for LLMs
Differentially Private Bayesian Inference for Generalized Linear Models
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Sparsity in Partially Controllable Linear Systems
IEMS - The Intelligent Email Sorter
Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection
EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
Understanding the Loss Surface of Neural Networks for Binary Classification
Where is the Truth? The Risk of Getting Confounded in a Continual World
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Distributed Second Order Methods with Fast Rates and Compressed Communication
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation
The Sample Complexity of Online One-Class Collaborative Filtering
Detecting and mitigating issues in image-based COVID-19 diagnosis
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Universal Approximation of Mean-Field Models via Transformers
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Distributed and Provably Good Seedings for k-Means in Constant Rounds
Learning algorithms for online principal-agent problems (and selling goods online)
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Optimal Arms Identification with Knapsacks
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective
Riemannian coordinate descent algorithms on matrix manifolds
Multi-assignment clustering for Boolean data
Visual Generation Without Guidance
Newton Method over Networks is Fast up to the Statistical Precision
Dictionary Learning Based on Sparse Distribution Tomography
Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
Communication-Efficient Adaptive Federated Learning
Learning in Reproducing Kernel Krein Spaces
Representer Point Selection for Explaining Regularized High-dimensional Models
Large-Scale Multi-Agent Deep FBSDEs
High Confidence Policy Improvement
Explaining Graph Neural Networks via Structure-aware Interaction Index
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT
Nearest neighbors in high-dimensional data: the emergence and influence of hubs
Vague Prototype-Oriented Diffusion Model for Multi-Class Anomaly Detection
Parallel Coordinate Descent for L1-Regularized Loss Minimization
Learning structured prediction models: a large margin approach
Almost Tune-Free Variance Reduction
Learning Stochastic Motifs from Genetic Sequences
Quasi-Newton Methods: A New Direction
Generalization Error of Generalized Linear Models in High Dimensions
Learning with Bounded Instance and Label-dependent Label Noise
Liouville Flow Importance Sampler
Flashlight: Enabling Innovation in Tools for Machine Learning
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
NeuralCohort: Cohort-aware Neural Representation Learning for Healthcare Analytics
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising
Competitive Analysis of the Explore/Exploit Tradeoff
Canonical Tensor Decomposition for Knowledge Base Completion
Efficient Learning of Mahalanobis Metrics for Ranking
The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Gradient Temporal-Difference Learning with Regularized Corrections
Momentum Improves Normalized SGD
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Consistency of Multiple Kernel Clustering
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
The General Utility Problem in Machine Learning
Strength from Weakness: Fast Learning Using Weak Supervision
ActiveHedge: Hedge meets Active Learning
Stochastic Local Search in k-Term DNF Learning
Stochastic Iterative Graph Matching
Time-Consistent Self-Supervision for Semi-Supervised Learning
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Mechanisms of Projective Composition of Diffusion Models
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
A Law of Robustness beyond Isoperimetry
Active Nearest Neighbor Regression Through Delaunay Refinement
Accelerated sampling for the Indian Buffet Process
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Quantum Policy Gradient Algorithm with Optimized Action Decoding
Similarity Learning for Provably Accurate Sparse Linear Classification
Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
CogBench: a large language model walks into a psychology lab
MS-TIP: Imputation Aware Pedestrian Trajectory Prediction
Scaling Trends in Language Model Robustness
Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra
Data Scaling Laws in NMT: The Effect of Noise and Architecture
It's My Data Too: Private ML for Datasets with Multi-User Training Examples
Agnostic Learnability of Halfspaces via Logistic Loss
Deterministic Independent Component Analysis
On learning with dissimilarity functions
Reinforcement Learning of Implicit and Explicit Control Flow Instructions
Provable Interactive Learning with Hindsight Instruction Feedback
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Concept-based Explanations for Out-of-Distribution Detectors
One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
Graph Contrastive Backdoor Attacks
Primal-Dual Neural Algorithmic Reasoning
An Embodied Generalist Agent in 3D World
Clustering Properties of Self-Supervised Learning
Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search
video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models
On the Alignment between Fairness and Accuracy: from the Perspective of Adversarial Robustness
Learning Qualitative Models of Dynamic Systems
OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification
Private Streaming SCO in ℓ p geometry with Applications in High Dimensional Online Decision Making
Firing Bandits: Optimizing Crowdfunding
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix
Learning Instance-Specific Augmentations by Capturing Local Invariances
Deep Supervised t-Distributed Embedding
Invariant Rationalization
Plug-and-Play image restoration with Stochastic deNOising REgularization
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
Deep transfer via second-order Markov logic
First-Order Methods for Wasserstein Distributionally Robust MDP
Variational Control for Guidance in Diffusion Models
Modulating Surrogates for Bayesian Optimization
Batch List-Decodable Linear Regression via Higher Moments
Gradually Updated Neural Networks for Large-Scale Image Recognition
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Learning to Explore for Stochastic Gradient MCMC
Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences
Effective Structured Prompting by Meta-Learning and Representative Verbalizer
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
Faster cover trees
Conformal Tail Risk Control for Large Language Model Alignment
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Scaling Reinforcement Learning toward RoboCup Soccer
Non-linear matrix factorization with Gaussian processes
Exploiting Data-Independence for Fast Belief-Propagation
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Parallel Multiscale Autoregressive Density Estimation
Explaining the role of Intrinsic Dimensionality in Adversarial Training
Lazifying Conditional Gradient Algorithms
Understanding Robust Generalization in Learning Regular Languages
Planning by Prioritized Sweeping with Small Backups
STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Evaluating Distance Measures for Trajectories in the Mobile Setting
MAGAN: Aligning Biological Manifolds
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach
LLMs can see and hear without any training
Activized Learning with Uniform Classification Noise
Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search
Contract Design Under Approximate Best Responses
Automatic Parameter Selection by Minimizing Estimated Error
TTFSFormer: A TTFS-based Lossless Conversion of Spiking Transformer
Convergence Analysis of Policy Gradient Methods with Dynamic Stochasticity
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
A Bregman Proximal Viewpoint on Neural Operators
Learning to bid in revenue-maximizing auctions
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
When Bad Data Leads to Good Models
Lost Relatives of the Gumbel Trick
Categorical Schrödinger Bridge Matching
Learning to Score Behaviors for Guided Policy Optimization
Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
Privately detecting changes in unknown distributions
Linear Asymmetric Classifier for cascade detectors
Bridging the Gap Between f-GANs and Wasserstein GANs
Improving Multimodal Learning Balance and Sufficiency through Data Remixing
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Online Learning with Feedback Graphs Without the Graphs
PTTA: Purifying Malicious Samples for Test-Time Model Adaptation
Correlated bandits or: How to minimize mean-squared error online
Sets Clustering
Kernelized Synaptic Weight Matrices
Model Revision: A Theory of Incremental Model Learning
PID: Prompt-Independent Data Protection Against Latent Diffusion Models
The Set Covering Machine with Data-Dependent Half-Spaces
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness
Improving the Gating Mechanism of Recurrent Neural Networks
A Variational Framework for Improving Naturalness in Generative Spoken Language Models
Efficient Sparse Modeling with Automatic Feature Grouping
A Divide and Conquer Framework for Distributed Graph Clustering
Improving Adversarial Energy-Based Model via Diffusion Process
PackIt: A Virtual Environment for Geometric Planning
Coresets for Clustering in Graphs of Bounded Treewidth
Small-GAN: Speeding up GAN Training using Core-Sets
Clustering the Users of Large Web Sites into Communities
Invariant Causal Prediction for Block MDPs
Online Learning in Betting Markets: Profit versus Prediction
MissScore: High-Order Score Estimation in the Presence of Missing Data
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings
Improving Antibody Humanness Prediction using Patent Data
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
Semi-Supervised Learning of Mixture Models
Simulation of Graph Algorithms with Looped Transformers
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability
Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias
Learning to Compile Programs to Neural Networks
Efficient Semi-supervised and Active Learning of Disjunctions
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
On the Duality between Gradient Transformations and Adapters
Piecewise-stationary bandit problems with side observations
D-Flow: Differentiating through Flows for Controlled Generation
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Safe Reinforcement Learning Using Advantage-Based Intervention
Phase transitions for the existence of unregularized M-estimators in single index models
A Dantzig Selector Approach to Temporal Difference Learning
Dimensionality Reduction for General KDE Mode Finding
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models
Truncating Trajectories in Monte Carlo Reinforcement Learning
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements
Disciple-COA: From Agent Programming to Agent Teaching
Differentially Private Fair Learning
Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy
On the Independence Assumption in Neurosymbolic Learning
Learning Causal Relations from Subsampled Time Series with Two Time-Slices
Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency
Non-Linear Decision Trees - NDT
DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Efficient Online Set-valued Classification with Bandit Feedback
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Conservative Objective Models for Effective Offline Model-Based Optimization
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Consistent Adversarially Robust Linear Classification: Non-Parametric Setting
On Becoming Reactive
Safe Exploration for Optimization with Gaussian Processes
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness
RGE: A Repulsive Graph Rectification for Node Classification via Influence
Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion
Residual Components Analysis
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Poisoning Generative Replay in Continual Learning to Promote Forgetting
OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference
MaxMin-RLHF: Alignment with Diverse Human Preferences
Decomposing Temporal High-Order Interactions via Latent ODEs
Exact model averaging with naive Bayesian classifiers
Regret Minimization in Behaviorally-Constrained Zero-Sum Games
Model-Level Dual Learning
Revisiting Differentially Private Algorithms for Decentralized Online Learning
Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis
Large-Scale Meta-Learning with Continual Trajectory Shifting
Uniform Convergence Rates for Kernel Density Estimation
Communication-Constrained Bandits under Additive Gaussian Noise
Faster Greedy MAP Inference for Determinantal Point Processes
Position: What Can Large Language Models Tell Us about Time Series Analysis
Stable Conformal Prediction Sets
Lookahead Feature Construction for Learning Hard Concepts
Optimal Streaming Algorithms for Multi-Armed Bandits
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Linear Programming for Large-Scale Markov Decision Problems
Reinforcement Learning from Passive Data via Latent Intentions
Constructive Induction Framework
Dataflow-Guided Neuro-Symbolic Language Models for Type Inference
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity
Structured Prioritised Sweeping
Filtering with Abstract Particles
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Progressively Label Enhancement for Large Language Model Alignment
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
Interventional Contrastive Learning with Meta Semantic Regularizer
SHINE: Shielding Backdoors in Deep Reinforcement Learning
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes
Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection
Learning Word Association Norms Using Tree Cut Pair Models
Estimation Beyond Data Reweighting: Kernel Method of Moments
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Some Results on the Complexity of Knowledge-Based Refinement
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
Stable Coactive Learning via Perturbation
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
Relative Positional Encoding for Transformers with Linear Complexity
Matrix updates for perceptron training of continuous density hidden Markov models
Augmenting naive Bayes for ranking
How to Leverage Diverse Demonstrations in Offline Imitation Learning
Nonlinear Information-Theoretic Compressive Measurement Design
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
A stochastic memoizer for sequence data
Set Functions for Time Series
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance
MODA: MOdular Duplex Attention for Multimodal Perception, Cognition, and Emotion Understanding
Supervised versus multiple instance learning: an empirical comparison
Hierarchical Compound Poisson Factorization
DRAW: A Recurrent Neural Network For Image Generation
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory
Robust Perception through Equivariance
Off-Belief Learning
Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet
Controlled Generation with Equivariant Variational Flow Matching
Robust Asymmetric Learning in POMDPs
Multiple-source cross-validation
Encoding Musical Style with Transformer Autoencoders
Distribution kernels based on moments of counts
Constrained K-means Clustering with Background Knowledge
Wasserstein Policy Optimization
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
Bipartite Edge Prediction via Transductive Learning over Product Graphs
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
Learning from Biased Data: A Semi-Parametric Approach
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
Learning Parametric Distributions from Samples and Preferences
Discriminative learning for differing training and test distributions
Learning Useful Representations of Recurrent Neural Network Weight Matrices
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
A semiparametric statistical approach to model-free policy evaluation
Online Control with Adversarial Disturbances
Towards characterizing the value of edge embeddings in Graph Neural Networks
Confidence-Budget Matching for Sequential Budgeted Learning
Device Placement Optimization with Reinforcement Learning
\(\ell_{1, p}\)-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods
On the chance accuracies of large collections of classifiers
Black-box Adversarial Attacks with Limited Queries and Information
Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
Diffusion Counterfactual Generation with Semantic Abduction
Active feature acquisition via explainability-driven ranking
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning