yusuke1997/dblp-id-title huggingface dataset:
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation
Adaptive Annealed Importance Sampling with Constant Rate Progress
CommVQ: Commutative Vector Quantization for KV Cache Compression
AGIL: Solving the Exploration Versus Exploration Dilemma in a single Classifier System Applied to Simulated Robotics
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Instance-Optimal Compressed Sensing via Posterior Sampling
Few-shot Language Coordination by Modeling Theory of Mind
Report from Dagstuhl: SocioPaths - Multimodal Door-to-Door Route Planning via Social Paths
When to Forget? Complexity Trade-offs in Machine Unlearning
Topological Mixture Estimation
On the Importance of Embedding Norms in Self-Supervised Learning
Exploiting structure of uncertainty for efficient matroid semi-bandits
Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering
Composite kernel learning
Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
Robust Policy Gradient against Strong Data Corruption
For Every Generalization Action, Is There Really an Equal and Opposite Reaction?
CLIPood: Generalizing CLIP to Out-of-Distributions
Strategic Classification with Unknown User Manipulations
STEER: Assessing the Economic Rationality of Large Language Models
Knowledge Compilation to Speed Up Numerical Optimization
A novel lexicalized HMM-based learning framework for web opinion mining
Tractable Uncertainty for Structure Learning
Regression Learning with Limited Observations of Multivariate Outcomes and Features
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
How Much Can We Forget about Data Contamination?
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Local Fisher discriminant analysis for supervised dimensionality reduction
Transductive Inference for Text Classification using Support Vector Machines
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Declarative Bias for Structural Domains
Theory Refinement of Bayesian Networks with Hidden Variables
Adversarial Regression with Multiple Learners
On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
An Estimation and Analysis Framework for the Rasch Model
On Semi-parametric Inference for BART
Riemannian Convex Potential Maps
A Computational Framework for Solving Wasserstein Lagrangian Flows
Combining TD-learning with Cascade-correlation Networks
CoDy: Counterfactual Explainers for Dynamic Graphs
Instance Pruning Techniques
Multi-Class Classification with Maximum Margin Multiple Kernel
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks
ENSUR: Equitable and Statistically Unbiased Recommendation
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation
Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization
MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation
Federated Optimization with Doubly Regularized Drift Correction
Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond
Reverse Engineering the Neural Tangent Kernel
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
IRNeXt: Rethinking Convolutional Network Design for Image Restoration
Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime
MVA: Linear Attention with High-order Query-Keys Integration and Multi-level Vocabulary Decomposition
LLaGA: Large Language and Graph Assistant
Representational Upper Bounds of Bayesian Networks
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Inductive Learning of Reactive Action Models
Coupling Distributed and Symbolic Execution for Natural Language Queries
Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator
Markov Games as a Framework for Multi-Agent Reinforcement Learning
FiT: Flexible Vision Transformer for Diffusion Model
Learning Multi-Level Features with Matryoshka Sparse Autoencoders
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Structure Adaptive Algorithms for Stochastic Bandits
On Monotonic Linear Interpolation of Neural Network Parameters
Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering
Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures
Dynamic Sensing: Better Classification under Acquisition Constraints
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
Double Momentum Method for Lower-Level Constrained Bilevel Optimization
Deep Bayesian Nonparametric Tracking
Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations
Learning Optimal Auctions with Correlated Valuations from Samples
Multiple indefinite kernel learning with mixed norm regularization
The Price of Differential Privacy for Online Learning
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Auxiliary Modality Learning with Generalized Curriculum Distillation
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model
Learning Robot Skills with Temporal Variational Inference
Feature Engineering and Classifier Selection: A Case Study in Venusian Volcano Detection
Learning Decision Rules by Randomized Iterative Local Search
Robust Inverse Constrained Reinforcement Learning under Model Misspecification
Prediction and Control with Temporal Segment Models
Semi-Implicit Variational Inference
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
Batch and match: black-box variational inference with a score-based divergence
Graph model selection using maximum likelihood
Preferential Temporal Difference Learning
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
A Closer Look at Backdoor Attacks on CLIP
Unveiling Privacy, Memorization, and Input Curvature Links
Discovering Test Set Regularities in Relational Domains
Agent Workflow Memory
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Evasion and Hardening of Tree Ensemble Classifiers
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture
Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization
Parameter-free Locally Accelerated Conditional Gradients
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning
Active Ranking and Matchmaking, with Perfect Matchings
Perception in Reflection
Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
On Efficient Constructions of Checkpoints
Stopping conditions for exact computation of leave-one-out error in support vector machines
Adversarial Distillation of Bayesian Neural Network Posteriors
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Observable Propagation: Uncovering Feature Vectors in Transformers
PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
Learning to Rank Learning Curves
Adaptive Stabilization Based on Machine Learning for Column Generation
Diffusion on Language Model Encodings for Protein Sequence Generation
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks
Probabilistic Constrained Reinforcement Learning with Formal Interpretability
FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining
Compositional noisy-logical learning
Learning and Inference via Maximum Inner Product Search
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Experimental comparison between bagging and Monte Carlo ensemble classification
Discretizing Continuous Attributes While Learning Bayesian Networks
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
Multi-Sender Persuasion: A Computational Perspective
Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Sorting Out Lipschitz Function Approximation
Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models
Data Mining of Subjective Agricultural Data
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks
Deep Hierarchy in Bandits
Learning Approximate MRFs from Large Transactional Data
Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model
Continual Generalized Category Discovery: Learning and Forgetting from a Bayesian Perspective
Classifying matrices with a spectral regularization
Steering Protein Language Models
Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Representational aspects of depth and conditioning in normalizing flows
Expectation maximization algorithms for conditional likelihoods
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations
Learning Latent Space Models with Angular Constraints
Prompt Sketching for Large Language Models
Generic Exploration and K-armed Voting Bandits
Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks
Boosting Neighborhood-Based Classifiers
On the Expressivity of Neural Networks for Deep Reinforcement Learning
Does a Neural Network Really Encode Symbolic Concepts?
DiffDA: a Diffusion model for weather-scale Data Assimilation
Compiling Bayesian Networks into Neural Networks
Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Continual Learning in Linear Classification on Separable Data
Gradient-Free Structured Pruning with Unlabeled Data
FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning
Towards Trustworthy Federated Learning with Untrusted Participants
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees
IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation
Temperature-Annealed Boltzmann Generators
SinDDM: A Single Image Denoising Diffusion Model
Randomized Smoothing of All Shapes and Sizes
Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Causality Inspired Federated Learning for OOD Generalization
Analysis of a Classification-based Policy Iteration Algorithm
Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion
Restoration based Generative Models
MOGIC: Metadata-infused Oracle Guidance for Improved Extreme Classification
A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators
An Improved Clique-Picking Algorithm for Counting Markov Equivalent DAGs via Super Cliques Transfer
High Performance Zero-Memory Overhead Direct Convolutions
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
MoMo: Momentum Models for Adaptive Learning Rates
Relating reinforcement learning performance to classification performance
Stochastic Variance-Reduced Policy Gradient
Convex Adversarial Collective Classification
Dissipativity Theory for Nesterov's Accelerated Method
Generative Decoding of Visual Stimuli
Accurate Inference for Adaptive Linear Models
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
Collage: Light-Weight Low-Precision Strategy for LLM Training
MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution
Scalable Safe Policy Improvement via Monte Carlo Tree Search
Multi-Receiver Online Bayesian Persuasion
Estimating and Penalizing Induced Preference Shifts in Recommender Systems
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Discovering Regularities from Large Knowledge Bases
Learning for control from multiple demonstrations
Generalizable Multi-Camera 3D Object Detection from a Single Source via Fourier Cross-View Learning
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
A Distribution-dependent Analysis of Meta Learning
Best Arm Identification in Multi-Agent Multi-Armed Bandits
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
Relational sequential inference with reliable observations
Reinforcement Learning with Bounded Risk
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
Ranking on graph data
Emergent Agentic Transformer from Chain of Hindsight Experience
Learning large margin classifiers locally and globally
Approximate Dimension Equalization in Vector-based Information Retrieval
A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Feature learning in deep classifiers through Intermediate Neural Collapse
Non-parametric Online Change Point Detection on Riemannian Manifolds
Understanding Self-Training for Gradual Domain Adaptation
Extended and Unscented Kitchen Sinks
Duality and Geometry in SVM Classifiers
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
Identifying Causal Direction via Variational Bayesian Compression
Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization
Learn2Hop: Learned Optimization on Rough Landscapes
Supervised dimensionality reduction using mixture models
ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
Selective sampling algorithms for cost-sensitive multiclass prediction
Comparing Methods for Refining Certainty-Factor Rule-Bases
Boosting Noisy Data
FloE: On-the-Fly MoE Inference on Memory-constrained GPU
Tensor Decomposition Based Memory-Efficient Incremental Learning
Constraints on Predicate Invention
Constrained Exploitability Descent: An Offline Reinforcement Learning Method for Finding Mixed-Strategy Nash Equilibrium
Asymptotically-Optimal Gaussian Bandits with Side Observations
Deep Reference Priors: What is the best way to pretrain a model?
TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state
Connecting Sphere Manifolds Hierarchically for Regularization
Copilot Arena: A Platform for Code LLM Evaluation in the Wild
The Shapley Taylor Interaction Index
EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture
Optimal and Adaptive Algorithms for Online Boosting
Latent Traversals in Generative Models as Potential Flows
Conditionally Strongly Log-Concave Generative Models
Personalized Subgraph Federated Learning
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
Workshop summary: Automated interpretation and modelling of cell images
Whitened CLIP as a Likelihood Surrogate of Images and Captions
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
Black-Box Alpha Divergence Minimization
Interpretable Neural-Symbolic Concept Reasoning
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Learning End-to-end Video Classification with Rank-Pooling
Machine Invention of First Order Predicates by Inverting Resolution
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods
Latent variable model for high-dimensional point process with structured missingness
Scalable Spike-and-Slab
Accelerated Speculative Sampling Based on Tree Monte Carlo
Langevin Policy for Safe Reinforcement Learning
Measuring Sample Quality with Kernels
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
Online Prediction with Privacy
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Convergence of Some Convex Message Passing Algorithms to a Fixed Point
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Revisiting Bellman Errors for Offline Model Selection
Unique Properties of Flat Minima in Deep Networks
Rule Induction for Semantic Query Optimization
Exploring Intrinsic Dimension for Vision-Language Model Pruning
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Extending Test-Time Augmentation with Metamorphic Relations for Combinatorial Problems
Clustering Semi-Random Mixtures of Gaussians
Breaking Locality Accelerates Block Gauss-Seidel
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
A Framework for Improving Efficiency and Accuracy
Volume Optimality in Conformal Prediction with Structured Prediction Sets
Flow Q-Learning
Learning First-Order Acyclic Horn Programs from Entailment
Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
Pluvial Flood Emulation with Hydraulics-informed Message Passing
Adversarial Attacks on Combinatorial Multi-Armed Bandits
Discriminative k -metrics
Neural Pharmacodynamic State Space Modeling
Graph-Based Algorithms for Diverse Similarity Search
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Self-Modulating Nonparametric Event-Tensor Factorization
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits
Provably Improving Generalization of Few-shot models with Synthetic Data
On the Complexity of Bayesian Generalization
Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Variational Inference for Infinitely Deep Neural Networks
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Dirichlet component analysis: feature extraction for compositional data
Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications
CoMemo: LVLMs Need Image Context with Image Memory
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Private Truly-Everlasting Robust-Prediction
Bridging Protein Sequences and Microscopy Images with Unified Diffusion Models
Contextual Conservative Interleaving Bandits
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Diverging Preferences: When do Annotators Disagree and do Models Know?
Triple Changes Estimator for Targeted Policies
Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping
Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation
Preference Modeling with Context-Dependent Salient Features
QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal
Sample Efficient Reinforcement Learning with Gaussian Processes
Coresets for Multiple ℓp Regression
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes
Online Linear Classification with Massart Noise
In-Context Learning Agents Are Asymmetric Belief Updaters
Robust Graph Representation Learning via Neural Sparsification
Conformalization of Sparse Generalized Linear Models
Context-Aware Zero-Shot Learning for Object Recognition
Unsupervised evidence integration
Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals
Flash: Concept Drift Adaptation in Federated Learning
Multirate Training of Neural Networks
Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
Global Selection of Contrastive Batches via Optimization on Sample Permutations
Active Learning based Structural Inference
Expected Variational Inequalities
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
Learning from Nested Data with Ornstein Auto-Encoders
Continuous Semi-Implicit Models
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
Implicit competitive regularization in GANs
Learning Safe Control via On-the-Fly Bandit Exploration
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
Sparse Gaussian graphical models with unknown block structure
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Causal Discovery via Conditional Independence Testing with Proxy Variables
Training OOD Detectors in their Natural Habitats
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Tensor denoising and completion based on ordinal observations