yusuke1997/dblp-id-title huggingface dataset:
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
The Complexity of k-Means Clustering when Little is Known
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Committee-Based Sampling For Training Probabilistic Classifiers
Entropic Affinities: Properties and Efficient Numerical Computation
Deep Latent State Space Models for Time-Series Generation
UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model
An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation
GFlowNet Training by Policy Gradients
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Uncertainty Estimation for Molecules: Desiderata and Methods
Static Automatic Batching In TensorFlow
Causal Abstraction Inference under Lossy Representations
Hyperplane Clustering via Dual Principal Component Pursuit
Almost Optimal Exploration in Multi-Armed Bandits
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks
QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning
Learning Program Embeddings to Propagate Feedback on Student Code
Model-based reinforcement learning with nearly tight exploration complexity bounds
Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Just Train Twice: Improving Group Robustness without Training Group Information
Learning Priorities From Noisy Examples
Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies
Return Capping: Sample Efficient CVaR Policy Gradient Optimisation
Sampling in Unit Time with Kernel Fisher-Rao Flow
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Point-Level Topological Representation Learning on Point Clouds
Overcoming Catastrophic Forgetting with Hard Attention to the Task
TACTiS: Transformer-Attentional Copulas for Time Series
Constructive Induction on Symbolic Features
PoKED: A Semi-Supervised System for Word Sense Disambiguation
Local similarity discriminant analysis
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Neural Algorithmic Reasoning with Causal Regularisation
McGan: Mean and Covariance Feature Matching GAN
Using Abductive Recovery of Failed Proofs for Problem Solving by Analogy
Label propagation through linear neighborhoods
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
Differentially Private Federated k-Means Clustering with Server-Side Data
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
AlphaNet: Improved Training of Supernets with Alpha-Divergence
Learning Initial Basis Selection for Linear Programming via Duality-Inspired Tripartite Graph Representation and Comprehensive Supervision
Boosting with Confidence Information
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Role-based Multiplex Network Embedding
Black-Box Variational Inference for Stochastic Differential Equations
Batched Dueling Bandits
Nested Subspace Arrangement for Representation of Relational Data
Metropolis-Hastings Generative Adversarial Networks
EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search
ILLUME: Rationalizing Vision-Language Models through Human Interactions
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Amortized Monte Carlo Integration
On the Convergence of Boosting Procedures
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
IMEXnet A Forward Stable Deep Neural Network
Adversarial Parameter Attack on Deep Neural Networks
Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
TGRL: An Algorithm for Teacher Guided Reinforcement Learning
Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
Reward Shaping for Reinforcement Learning with An Assistant Reward Agent
Tighter Variational Bounds are Not Necessarily Better
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation
On Measuring Long-Range Interactions in Graph Neural Networks
Are Generative Classifiers More Robust to Adversarial Attacks?
OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition
The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
Differential Privacy has Bounded Impact on Fairness in Classification
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Trustworthy Machine Learning through Data-Specific Indistinguishability
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation
Minerva: A Programmable Memory Test Benchmark for Language Models
Coin Flipping Neural Networks
Naive imputation implicitly regularizes high-dimensional linear models
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
Rademacher Observations, Private Data, and Boosting
Uncovering Adversarial Risks of Test-Time Adaptation
HarmonyDream: Task Harmonization Inside World Models
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
Clustering through ranking on manifolds
Additive Causal Bandits with Unknown Graph
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
On the Local Complexity of Linear Regions in Deep ReLU Networks
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Understanding and Simplifying One-Shot Architecture Search
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM
On the Learnability of the Uncomputable
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Understanding and Improving Length Generalization in Recurrent Models
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Sharper Bounds for ℓ p Sensitivity Sampling
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Partial Linear Trees
Inductive Biases and Variable Creation in Self-Attention Mechanisms
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
Limited Preference Aided Imitation Learning from Imperfect Demonstrations
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Simple and Fast Group Robustness by Automatic Feature Reweighting
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Feel-Good Thompson Sampling for Contextual Dueling Bandits
Trainable Transformer in Transformer
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Relational DNN Verification With Cross Executional Bound Refinement
A Kernelized Stein Discrepancy for Biological Sequences
Fast Sketching of Polynomial Kernels of Polynomial Degree
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Mutual Information Neural Estimation
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages
Reinforcement Learning with Segment Feedback
MGit: A Model Versioning and Management System
Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting
Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
Boosting with Online Binary Learners for the Multiclass Bandit Problem
Position: Do pretrained Transformers Learn In-Context by Gradient Descent?
Distance dependent Chinese restaurant processes
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
Learning by Playing Solving Sparse Reward Tasks from Scratch
Image Transformer
TabDDPM: Modelling Tabular Data with Diffusion Models
Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
Hierarchical Diffusion for Offline Decision Making
Rule learning with monotonicity constraints
Adaptive Sampling for Estimating Probability Distributions
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Imprecise Concept Learning within a Growing Language
Auditing Prompt Caching in Language Model APIs
Scale Invariant Conditional Dependence Measures
Random classification noise defeats all convex potential boosters
Towards Robust Graph Incremental Learning on Evolving Graphs
Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible
Differentially Private Query Release Through Adaptive Projection
Prompt-based Depth Pruning of Large Language Models
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation
Online Variational Sequential Monte Carlo
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
A Unified Framework for Structured Low-rank Matrix Learning
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Thompson Sampling for Combinatorial Semi-Bandits
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Wasserstein Generative Adversarial Networks
Predicting Actions from Induction on Past Performance
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
Differentially Private Approximate Quantiles
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
On Compact Codes for Spatially Pooled Features
Domain Agnostic Learning with Disentangled Representations
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Learning to Initiate and Reason in Event-Driven Cascading Processes
Top-k Selection based on Adaptive Sampling of Noisy Preferences
Aligning Multimodal Representations through an Information Bottleneck
VectorMapNet: End-to-end Vectorized HD Map Learning
Max-Mahalanobis Linear Discriminant Analysis Networks
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
Learning Divergence Fields for Shift-Robust Graph Representations
All in a Row: Compressed Convolution Networks for Graphs
Why Regularized Auto-Encoders learn Sparse Representation?
Latent Diffusion Planning for Imitation Learning
Run-Time Task Composition with Safety Semantics
Adversarially Learned Representations for Information Obfuscation and Inference
Cross-domain Open-world Discovery
A generalization of Haussler's convolution kernel: mapping kernel
Near Optimal Reward-Free Reinforcement Learning
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers
Probabilistic score estimation with piecewise logistic regression
Learning from Deliberated Reactivity
Taming graph kernels with random features
Learning Iterative Reasoning through Energy Minimization
On Regularization and Inference with Label Constraints
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Confidence Difference Reflects Various Supervised Signals in Confidence-Difference Classification
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence
Adaptive Random Walk Gradient Descent for Decentralized Optimization
Towards a Persistence Diagram that is Robust to Noise and Varied Densities
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Risk-Sensitive Theory of Mind: Coordinating with Agents of Unknown Bias using Cumulative Prospect Theory
Thompson Sampling for Complex Online Problems
Better Mixing via Deep Representations
A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Online Submodular Minimization for Combinatorial Structures
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits
Learning Neurosymbolic Generative Models via Program Synthesis
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
Trustworthy Policy Learning under the Counterfactual No-Harm Criterion
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Using Error-Correcting Codes for Text Classification
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
Identifying the Information Contained in a Flawed Theory
Ellipsoidal Multiple Instance Learning
UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation
Rotation Invariant Householder Parameterization for Bayesian PCA
A brain computer interface with online feedback based on magnetoencephalography
Detection of Signal in the Spiked Rectangular Models
Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems
Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
Apprenticeship learning via inverse reinforcement learning
How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation
Robust Budget Pacing with a Single Sample
HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling
An Ounce of Knowledge is Worth a Ton of Data: Quantitative studies of the Trade-Off between Expertise and Data Based On Statistically Well-Founded Empirical Induction
Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
Learning Human Objectives by Evaluating Hypothetical Behavior
Subspace Robust Wasserstein Distances
Adversarial Cheap Talk
Approximately Optimal Approximate Reinforcement Learning
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
Doubly Adversarial Federated Bandits
Generative modeling for continuous non-linearly embedded visual inference
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
Learning to Optimize Differentiable Games
Influenza Forecasting Framework based on Gaussian Processes
Language Models Represent Beliefs of Self and Others
Dataset Condensation with Differentiable Siamese Augmentation
Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents
Improving Fair Training under Correlation Shifts
Autonomous shaping: knowledge transfer in reinforcement learning
Supervised feature selection via dependence estimation
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
Geometric convergence of elliptical slice sampling
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
On the Effectiveness of Offline RL for Dialogue Response Generation
Disentangled Graph Convolutional Networks
Can We Predict Performance of Large Models across Vision-Language Tasks?
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
Collaborative Bayesian Optimization with Fair Regret
Towards Explainability for AI Fairness
Variational Rectified Flow Matching
Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction
LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning
Revealing Vision-Language Integration in the Brain with Multimodal Networks
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time
Settling the Maximin Share Fairness for Scheduling among Groups of Machines
Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach
Closed-form dual perturb and combine for tree-based models
Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
Differentiable Solver Search for Fast Diffusion Sampling
3D Question Answering via only 2D Vision-Language Models
Optimal Sequential Maximization: One Interview is Enough!
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
Taylor Videos for Action Recognition
Causal Effect Identifiability under Partial-Observability
Dynamic Capacity Networks
Graph Positional Encoding via Random Feature Propagation
MusicRL: Aligning Music Generation to Human Preferences
Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
Multi-User Reinforcement Learning with Low Rank Rewards
Individual Preference Stability for Clustering
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Anonymous Walk Embeddings
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Dynamical phases of short-term memory mechanisms in RNNs
Fair k-Center Clustering for Data Summarization
Data Augmentation as Feature Manipulation
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Learning Efficient Robotic Garment Manipulation with Standardization
Tutorial summary: Structured prediction for natural language processing
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications
Parametric Graph for Unimodal Ranking Bandit
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Angular Visual Hardness
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Algorithms for Direct 0-1 Loss Optimization in Binary Classification
Knowledge Distillation with Auxiliary Variable
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
Visual Autoregressive Modeling for Image Super-Resolution
Exact Inference for Integer Latent-Variable Models
Factorized Asymptotic Bayesian Hidden Markov Models
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Best of Both Worlds Policy Optimization
Proactive Detection of Voice Cloning with Localized Watermarking
Using Knowledge to Speed Learning: A Comparison of Knowledge-based Cascade-correlation and Multi-task Learning
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
Large-Margin Metric Learning for Constrained Partitioning Problems
On Transportation of Mini-batches: A Hierarchical Approach
Extreme Compression of Large Language Models via Additive Quantization
Knowledge Acquisition Combining Analytical and Empirrcal Techniques
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Convex optimization techniques for fitting sparse Gaussian graphical models
Reinforce LLM Reasoning through Multi-Agent Reflection
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
Exploring the Benefit of Activation Sparsity in Pre-training
ILILT: Implicit Learning of Inverse Lithography Technologies
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Algebraic Variety Models for High-Rank Matrix Completion
Towards Distraction-Robust Active Visual Tracking
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Forest-type Regression with General Losses and Robust Forest
Safe Offline Reinforcement Learning with Real-Time Budget Constraints
Cramming: Training a Language Model on a single GPU in one day
MOMENT: A Family of Open Time-series Foundation Models
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
On Learning Deep O(n)-Equivariant Hyperspheres
Provable guarantees for decision tree induction: the agnostic setting
Mitigating Local Cohesion and Global Sparseness in Graph Contrastive Learning with Fuzzy Boundaries
Explainable Automated Graph Representation Learning with Hyperparameter Importance
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
Generalization Analysis for Contrastive Representation Learning
RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution
Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation
Multimodal Prototyping for cancer survival prediction
Stochastic Variance Reduction Methods for Policy Evaluation
A Practical Approach to Feature Selection
FG-CLIP: Fine-Grained Visual and Textual Alignment
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
Emergent Representations of Program Semantics in Language Models Trained on Programs