yusuke1997/dblp-id-title huggingface dataset:
Neighboring Perturbations of Knowledge Editing on Large Language Models
Local Causal Structure Learning in the Presence of Latent Variables
Rehashing Kernel Evaluation in High Dimensions
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
TarMAC: Targeted Multi-Agent Communication
Inferring Graphs from Cascades: A Sparse Recovery Framework
Kernel Continual Learning
Improving Open-Ended Text Generation via Adaptive Decoding
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation
Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Multicoated Supermasks Enhance Hidden Networks
Online Robust Reinforcement Learning Through Monte-Carlo Planning
Multi-Player Bandits - a Musical Chairs Approach
PIDformer: Transformer Meets Control Theory
Consensus Is All You Get: The Role of Attention in Transformers
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices
Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback
Learning to Scale Logits for Temperature-Conditional GFlowNets
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
Noisin: Unbiased Regularization for Recurrent Neural Networks
Knowledge Propagation in Model-based Reinforcement Learning Tasks
A Distributional Framework For Data Valuation
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
Multiclass Classification with Bandit Feedback using Adaptive Regularization
Learning from Corrupted Binary Labels via Class-Probability Estimation
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
CFPT: Empowering Time Series Forecasting through Cross-Frequency Interaction and Periodic-Aware Timestamp Modeling
Is Space-Time Attention All You Need for Video Understanding?
Non-autoregressive Conditional Diffusion Models for Time Series Prediction
Efficient Mining from Large Databases by Query Learning
Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Modalities Contribute Unequally: Enhancing Medical Multi-modal Learning through Adaptive Modality Token Re-balancing
Nonparametric Return Distribution Approximation for Reinforcement Learning
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
Bacon, Data Analysis and Artificial Intelligence
Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning
Geometric Losses for Distributional Learning
Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective
HaploVL: A Single-Transformer Baseline for Multi-Modal Understanding
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Beyond the One-Step Greedy Approach in Reinforcement Learning
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Provably End-to-end Label-noise Learning without Anchor Points
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Exploring Invariance in Images through One-way Wave Equations
Predictive Sampling with Forecasting Autoregressive Models
A general recurrent state space framework for modeling neural dynamics during decision-making
Motion Question Answering via Modular Motion Programs
Novelty Detection in Reinforcement Learning with World Models
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
Learning Low-Dimensional Temporal Representations
On a theory of learning with similarity functions
Conformal prediction interval for dynamic time-series
Transformers are Meta-Reinforcement Learners
Representation Changes for Efficient Learning in Structural Domains
Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming
Learning Procedural Knowledge in the EBG Context
On Sparse Linear Regression in the Local Differential Privacy Model
Defense against Model Extraction Attack by Bayesian Active Watermarking
A Split-Merge Framework for Comparing Clusterings
Why bigger is not always better: on finite and infinite neural networks
A deep convolutional neural network that is invariant to time rescaling
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time
Private optimization in the interpolation regime: faster rates and hardness results
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Understanding MLP-Mixer as a wide and sparse MLP
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
A Distributed Second-Order Algorithm You Can Trust
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
GraphOpt: Learning Optimization Models of Graph Formation
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Conservative Bandits
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
On the Identifiability of Switching Dynamical Systems
Transfer Q-Learning with Composite MDP Structures
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
CATE: Computation-aware Neural Architecture Encoding with Transformers
LieTransformer: Equivariant Self-Attention for Lie Groups
12-Lead ECG Reconstruction via Koopman Operators
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
DiffAdvMAP: Flexible Diffusion-Based Framework for Generating Natural Unrestricted Adversarial Examples
Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks
Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach
Riemannian Similarity Learning
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
Sparse within Sparse Gaussian Processes using Neighbor Information
Progressive Purification for Instance-Dependent Partial Label Learning
Modeling Dynamic Environments with Scene Graph Memory
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling
Topological Singularity Detection at Multiple Scales
(Near) Dimension Independent Risk Bounds for Differentially Private Learning
CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
Estimating the unseen from multiple populations
Metric embedding for kernel classification rules
Modeling the Acquisition and Improvement of Motor Skkills
Robust Yet Efficient Conformal Prediction Sets
Online Balanced Experimental Design
Evaluating the Variance of Likelihood-Ratio Gradient Estimators
Geometric Multimodal Contrastive Representation Learning
Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Neural Topic Modeling with Continual Lifelong Learning
Subequivariant Graph Reinforcement Learning in 3D Environments
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
A Bayesian Approach to Online Planning
Why Is Public Pretraining Necessary for Private Model Training?
CN = CPCN
Proto-predictive representation of states with simple recurrent temporal-difference networks
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Learning De-biased Representations with Biased Representations
The Heavy-Tail Phenomenon in SGD
A Rate-Distortion View of Uncertainty Quantification
Improved Generalization of Weight Space Networks via Augmentations
Leveraging Public Data for Practical Private Query Release
Optimal Distributed Online Prediction
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
Understanding and Mitigating the Tradeoff between Robustness and Accuracy
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Evolving Strategies for Focused Web Crawling
Transport of Algebraic Structure to Latent Embeddings
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
Enumerating Distinct Decision Trees
Towards a General Theory of Infinite-Width Limits of Neural Classifiers
Learning Representations by Humans, for Humans
Phase and Amplitude-aware Prompting for Enhancing Adversarial Robustness
Supervised Matrix Factorization: Local Landscape Analysis and Applications
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
NestQuant: nested lattice quantization for matrix products and LLMs
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Adaptive View Validation: A First Step Towards Automatic View Detection
MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
Offline RL Policies Should Be Trained to be Adaptive
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Probabilistic Generating Circuits - Demystified
Learning Symbolic Rules Using Artificial Neural Networks
Capacity Releasing Diffusion for Speed and Locality
Dynamic Tree Block Coordinate Ascent
GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
A Closer Look at Few-shot Classification Again
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression
Mollification Effects of Policy Gradient Methods
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
Functional Space Analysis of Local GAN Convergence
Are Large Language Models Ready for Multi-Turn Tabular Data Analysis?
Nonoverlap-Promoting Variable Selection
Interpreting CLIP with Hierarchical Sparse Autoencoders
Fair Federated Learning via the Proportional Veto Core
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
Efficient optimization of loops and limits with randomized telescoping sums
MS3D: A RG Flow-Based Regularization for GAN Training with Limited Data
Adversarial Mutual Information for Text Generation
A graphical model for protein secondary structure prediction
Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs
Communication-Computation Efficient Gradient Coding
Watermarking Deep Neural Networks with Greedy Residuals
Deep Bayesian Active Learning with Image Data
Scaling Laws in Patchification: An Image Is Worth 50, 176 Tokens And More
A multiplicative up-propagation algorithm
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
GCAL: Adapting Graph Models to Evolving Domain Shifts
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
The Extended Parameter Filter
Geometry and Symmetry in Short-and-Sparse Deconvolution
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Using additive expert ensembles to cope with concept drift
Compression-Based Discretization of Continuous Attributes
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
Decomposable Submodular Maximization in Federated Setting
Codebook Features: Sparse and Discrete Interpretability for Neural Networks
Bayesian Quadrature for Multiple Related Integrals
Multi-Object Representation Learning with Iterative Variational Inference
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Calibrated Physics-Informed Uncertainty Quantification
Representation Surgery in Model Merging with Probabilistic Modeling
Meta-learning with Stochastic Linear Bandits
A Multi-Agent Policy-Gradient Approach to Network Routing
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Partially Supervised Classification of Text Documents
On the Connection Between MPNN and Graph Transformer
Bit Prioritization in Variational Autoencoders via Progressive Coding
An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets
When Maximum Entropy Misleads Policy Optimization
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Latent Variable Modelling with Hyperbolic Normalizing Flows
Learning Reward for Robot Skills Using Large Language Models via Self-Alignment
Meta-Learning for Phonemic Annotation of Corpora
Estimating the Bayes Point Using Linear Knapsack Problems
Partially Linear Additive Gaussian Graphical Models
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
Provably Strict Generalisation Benefit for Equivariant Models
Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture
Hierarchies of Reward Machines
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Imputer: Sequence Modelling via Imputation and Dynamic Programming
Adversarial examples from computational constraints
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Regression with Label Permutation in Generalized Linear Model
Beyond the point cloud: from transductive to semi-supervised learning
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Tensor Decomposition with Smoothness
Beyond Message Passing: Neural Graph Pattern Machine
An efficient method for simplifying support vector machines
CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
Non-separable Non-stationary random fields
Bregman Power k-Means for Clustering Exponential Family Data
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
Direct Parameterization of Lipschitz-Bounded Deep Networks
Learning Modality Knowledge Alignment for Cross-Modality Transfer
Budgeted Heterogeneous Treatment Effect Estimation
Learning to Predict in Uncertain Continuous Tasks
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Position: Foundation Agents as the Paradigm Shift for Decision Making
3D Geometric Shape Assembly via Efficient Point Cloud Matching
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
Learning in POMDPs is Sample-Efficient with Hindsight Observability
On Discrete Prompt Optimization for Diffusion Models
Hierarchical Long-term Video Prediction without Supervision
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
Fundamental Tradeoffs in Learning with Prior Information
BAME: Block-Aware Mask Evolution for Efficient N: M Sparse Training
Learning and Data Selection in Big Datasets
Omnipredictors for Constrained Optimization
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
A Laplacian Framework for Option Discovery in Reinforcement Learning
Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
Self-improvement Based on Reinforcement Learning, Planning and Teaching
Levy Measure Decompositions for the Beta and Gamma Processes
mStruct : a new admixture model for inference of population structure in light of both genetic admixing and allele mutations
Understanding Model Ensemble in Transferable Adversarial Attack
Do Perceptually Aligned Gradients Imply Robustness?
Stochastic Q-learning for Large Discrete Action Spaces
Uncertainty Modeling in Generative Compressed Sensing
Efficiently predicting high resolution mass spectra with graph neural networks
Learning in Nonzero-Sum Stochastic Games with Potentials
Minimum Cost Intervention Design for Causal Effect Identification
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-text Decoding
Improving Gibbs Sampler Scan Quality with DoGS
MRA-based Statistical Learning from Incomplete Rankings
Online Multi-Task Learning for Policy Gradient Methods
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Diffusion Rejection Sampling
On Investigating the Conservative Property of Score-Based Generative Models
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
The Primacy Bias in Deep Reinforcement Learning
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Learning to fly by combining reinforcement learning with behavioural cloning
DriftSurf: Stable-State / Reactive-State Learning under Concept Drift
Entropic Gromov-Wasserstein between Gaussian Distributions
Towards Explaining Distribution Shifts
Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
Multistrategy Learning for Information Extraction
LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Learning to Learn without Gradient Descent by Gradient Descent
Robust Models Are More Interpretable Because Attributions Look Normal
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
A Probabilistic Approach to Feature Selection - A Filter Solution
PODS: Policy Optimization via Differentiable Simulation
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Hashing for Distributed Data
Reachability-Aware Laplacian Representation in Reinforcement Learning
Learning to Identify Regular Expressions that Describe Email Campaigns
Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series
Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference
RNNRepair: Automatic RNN Repair via Model-based Analysis
AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem
Efficient Diffusion Models for Symmetric Manifolds
Principled Methods for Advising Reinforcement Learning Agents
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Noisy Activation Functions
Prediction-Powered E-Values
Analogical Inference for Multi-relational Embeddings
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
Attribute Efficient Linear Regression with Distribution-Dependent Sampling
Continual Vision-Language Representation Learning with Off-Diagonal Information
Hierarchically Structured Meta-learning
Statistical debugging: simultaneous identification of multiple bugs
Contrastive Learning Meets Homophily: Two Birds with One Stone
Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation
M+: Extending MemoryLLM with Scalable Long-Term Memory
When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
Leveraging Language to Learn Program Abstractions and Search Heuristics
Learning to Fly: An Application of Hierarchical Reinforcement Learning
Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
Moccasin: Efficient Tensor Rematerialization for Neural Networks
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Extra-gradient with player sampling for faster convergence in n-player games
Multi-class Graph Clustering via Approximated Effective p-Resistance
On the Robustness of CountSketch to Adaptive Inputs
Emergent Response Planning in LLMs
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures
Online Feature Selection for Model-based Reinforcement Learning
ReconBoost: Boosting Can Achieve Modality Reconcilement
Group Fisher Pruning for Practical Network Compression
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
Hierarchical Decompositional Mixtures of Variational Autoencoders
SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
Topic modeling: beyond bag-of-words
Deep Laplacian-based Options for Temporally-Extended Exploration
Principled Acceleration of Iterative Numerical Methods Using Machine Learning
ICA and ISA using Schweizer-Wolff measure of dependence
A Variational Approximation for Topic Modeling of Hierarchical Corpora
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Learning to track 3D human motion from silhouettes
SPHINX: Structural Prediction using Hypergraph Inference Network
From Neurons to Neutrons: A Case Study in Interpretability
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
Learning distance function by coding similarity
Learning in Non-stationary Conditions: A Control Theoretic Approach
Architecture-Agnostic Masked Image Modeling - From ViT back to CNN
Learning deep kernels for exponential family densities
Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
TD(lambda) networks: temporal-difference networks with eligibility traces
Online and Consistent Correlation Clustering
Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation