yusuke1997/dblp-id-title huggingface dataset:
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
Robust Multi-Agent Reinforcement Learning with Stochastic Adversary
Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning
Tailoring density estimation via reproducing kernel moment matching
Knowing The What But Not The Where in Bayesian Optimization
Bayesian Deep Embedding Topic Meta-Learner
Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure
Low-Rank Matrix Approximation with Stability
Feature Quantization Improves GAN Training
A Dense Reward View on Aligning Text-to-Image Diffusion with Preference
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective
Fair Learning with Private Demographic Data
Simultaneous Learning and Covering with Adversarial Noise
Case-Based Acquisition of Place Knowledge
Rank-One Modified Value Iteration
"Learning by Instruction" in connectionist Systems
A Tensor Decomposition Perspective on Second-order RNNs
Model selection via the AUC
Matching Learned Causal Effects of Neural Networks with Domain Priors
Graph Generative Model for Benchmarking Graph Neural Networks
Recasting Self-Attention with Holographic Reduced Representations
Optimal Auctions through Deep Learning
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Regression by dependence minimization and its application to causal inference in additive noise models
Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications
Tight and Robust Private Mean Estimation with Few Users
Tuning Rule-Based Systems to Their Environments
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
LegendreTron: Uprising Proper Multiclass Loss Learning
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Optimal Mean Robust Principal Component Analysis
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Generalized Federated Learning via Sharpness Aware Minimization
Knowledge Acquisition from Examples using Maximal Representation Learning
Proteus: Exploring Protein Structure Generation for Enhanced Designability and Efficiency
Towards an Explainable Comparison and Alignment of Feature Embeddings
Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions
Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions
You Only Cut Once: Boosting Data Augmentation with a Single Cut
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission
Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models
GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
Mix & Match Agent Curricula for Reinforcement Learning
Autoencoding Conditional Neural Processes for Representation Learning
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Non-Vacuous Generalisation Bounds for Shallow Neural Networks
Zero-Shot Reward Specification via Grounded Natural Language
SampleRank: Training Factor Graphs with Atomic Gradients
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Supervised Learning with General Risk Functionals
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions
Iterative Double Sketching for Faster Least-Squares Optimization
Scalable Certified Segmentation via Randomized Smoothing
Instruction-Following Pruning for Large Language Models
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning
Robust Speech Recognition via Large-Scale Weak Supervision
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Using Learning by Discovery to Segment Remotely Sensed Images
Neural Solver Selection for Combinatorial Optimization
Q-Learning for Bandit Problems
What does LIME really see in images?
The Jailbreak Tax: How Useful are Your Jailbreak Outputs?
Combinatorial Blocking Bandits with Stochastic Delays
LoRA Training in the NTK Regime has No Spurious Local Minima
The Arrow of Time in Multivariate Time Series
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
BackSlash: Rate Constrained Optimized Training of Large Language Models
Rapid Overfitting of Multi-Pass SGD in Stochastic Convex Optimization
DiMa: Understanding the Hardness of Online Matching Problems via Diffusion Models
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses
On the Optimality of Batch Policy Optimization Algorithms
Auto-reconfiguration for Latency Minimization in CPU-based DNN Serving
FONN: Combining First Order Logic with Connectionist Learning
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Calibrate Before Use: Improving Few-shot Performance of Language Models
Model-based Policy Gradient Reinforcement Learning
Modeling Adversarial Noise for Adversarial Training
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning
Multi-agent Architecture Search via Agentic Supernet
Statistical linear estimation with penalized estimators: an application to reinforcement learning
Selling Data To a Machine Learner: Pricing via Costly Signaling
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
Adaptive Estimation of Graphical Models under Total Positivity
Robust Weak Supervision with Variational Auto-Encoders
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
Influence Function Learning in Information Diffusion Networks
Intrinsic dimensionality estimation of submanifolds in R d
Inferning with High Girth Graphical Models
Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks
PokéChamp: an Expert-level Minimax Language Agent
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions
Feasible Arm Identification
Towards Practical Defect-Focused Automated Code Review
Gaussian Process Quantile Regression using Expectation Propagation
Learning to Predict Performance from Formula Modeling and Training Data
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
A Selective Learning Method for Temporal Graph Continual Learning
Weighted Low-Rank Approximations
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks
Individual Calibration with Randomized Forecasting
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss
Identifying Useful Learnwares for Heterogeneous Label Spaces
Bayesian Optimization Meets Bayesian Optimal Stopping
Learning to Quantize for Training Vector-Quantized Networks
Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
Features are fate: a theory of transfer learning in high-dimensional regression
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
Improving MRI Brain Image Classification with Anatomical Regional Kernels
Deterministic equivalent and error universality of deep random features learning
Bayesian actor-critic algorithms
Deep Regression Unlearning
Constrained Markov Decision Processes via Backward Value Functions
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think
Efficient inference with cardinality-based clique potentials
Evaluating the Performance of Reinforcement Learning Algorithms
Individual Reward Assisted Multi-Agent Reinforcement Learning
Stochastic Latent Residual Video Prediction
Generalizing Neural Wave Functions
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
Deep Gaussian Processes with Importance-Weighted Variational Inference
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Reliable and Efficient Amortized Model-based Evaluation
Nuclear Norm Minimization via Active Subspace Selection
The Double-Ellipsoid Geometry of CLIP
Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence
The Geometry of Robust Value Functions
Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
Learning when to stop thinking and do something!
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Sparse and Structured Hopfield Networks
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
General agents need world models
Provable Policy Gradient for Robust Average-Reward MDPs Beyond Rectangularity
Mixed Nash Equilibria in the Adversarial Examples Game
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction
An Augmented Lagrangian Approach to Constrained MAP Inference
Evidential Interactive Learning for Medical Image Captioning
LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
Train simultaneously, generalize better: Stability of gradient-based minimax learners
Kernel conditional random fields: representation and clique selection
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
Efficient Algorithms for Minimizing Cross Validation Error
H-Consistency Bounds for Surrogate Loss Minimizers
Multiplicative Noise and Heavy Tails in Stochastic Optimization
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
On the Generalization of Equivariant Graph Neural Networks
Can Neural Network Memorization Be Localized?
Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation
CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models
Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
Dimension-Independent Rates for Structured Neural Density Estimation
Relative Error Fair Clustering in the Weak-Strong Oracle Model
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation
Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches
SlotGAT: Slot-based Message Passing for Heterogeneous Graphs
Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Keyframe-Focused Visual Imitation Learning
Grounding Language Models to Images for Multimodal Inputs and Outputs
Objective Bound Conditional Gaussian Process for Bayesian Optimization
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Learning Iterative Reasoning through Energy Diffusion
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Scalable Model Selection for Large-Scale Factorial Relational Models
Attribute Based Interpretable Evaluation Metrics for Generative Models
Nonconvex Theory of M-estimators with Decomposable Regularizers
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
Nonparametric Canonical Correlation Analysis
Discriminative Policy Optimization for Token-Level Reward Models
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
The MENTLE Approach to Learning Heuristics for the Control of Logic Programs
Learning Action Representations for Reinforcement Learning
Learning with Selectively Labeled Data from Multiple Decision-makers
From Local SGD to Local Fixed-Point Methods for Federated Learning
Configurable Mirror Descent: Towards a Unification of Decision Making
Tensor-Train Recurrent Neural Networks for Video Classification
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Using CCA to improve CCA: A new spectral method for estimating vector models of words
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Limitations of measure-first protocols in quantum machine learning
Solving Linear Programs with Fast Online Learning Algorithms
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
Anytime optimal algorithms in stochastic multi-armed bandits
Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning
Bayesian Counterfactual Risk Minimization
Unifying Specialized Visual Encoders for Video Language Models
Equivariant Frames and the Impossibility of Continuous Canonicalization
Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
Phase Transitions in the Detection of Correlated Databases
Kernel-Based Evaluation of Conditional Biological Sequence Models
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers
Reflection-Bench: Evaluating Epistemic Agency in Large Language Models
Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation
Scalable Meta-Learning via Mixed-Mode Differentiation
Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Understanding Instance-Level Impact of Fairness Constraints
GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
Subspace Learning for Effective Meta-Learning
Boosting Multi-Domain Fine-Tuning of Large Language Models through Evolving Interactions between Samples
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
Stability-Informed Initialization of Neural Ordinary Differential Equations
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
DiffFPR: Diffusion Prior for Oversampled Fourier Phase Retrieval
Improving the Scaling Laws of Synthetic Data with Deliberate Practice
On the Power of Learning-Augmented Search Trees
Task-Optimal Exploration in Linear Dynamical Systems
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
Defense against backdoor attacks via robust covariance estimation
Identifiability of Label Noise Transition Matrix
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling
Incentivized Learning in Principal-Agent Bandit Games
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing
Position: Why We Must Rethink Empirical Research in Machine Learning
A Bootstrapping Approach to Concept Clustering
An Empirical Study of Realized GNN Expressiveness
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
Learning Mixtures of Markov Chains and MDPs
Online Learning in CMDPs: Handling Stochastic and Adversarial Constraints
Nonparametric factor analysis with beta process priors
Transitional Uncertainty with Layered Intermediate Predictions
Noisy SIGNSGD Is More Differentially Private Than You (Might) Think
Causal Action Influence Aware Counterfactual Data Augmentation
Position: Measure Dataset Diversity, Don't Just Claim It
Data-driven Web Design
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Implicit Representations for Constrained Image Segmentation
How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation
On Energy-Based Models with Overparametrized Shallow Neural Networks
Image generation with shortest path diffusion
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation
Local Cascade Generalization
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
Direct Behavior Specification via Constrained Reinforcement Learning
Discrete Temporal Models of Social Networks
Fast Constrained Submodular Maximization: Personalized Data Summarization
Complex Embeddings for Simple Link Prediction
Robust Sparse Regression under Adversarial Corruption
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes
Detecting Rewards Deterioration in Episodic Reinforcement Learning
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation
On Distribution Shift in Learning-based Bug Detectors
Coresets for Data-efficient Training of Machine Learning Models
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Proximal Causal Learning of Conditional Average Treatment Effects
Parametric Gaussian Process Regressors
PAC model-free reinforcement learning
BOOD: Boundary-based Out-Of-Distribution Data Generation
ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning
Markpainting: Adversarial Machine Learning meets Inpainting
STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment
Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
On orthogonality and learning recurrent networks with long term dependencies
QMamba: On First Exploration of Vision Mamba for Image Quality Assessment
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Precision-recall space to correct external indices for biclustering
On the Adversarial Robustness of Multi-Kernel Clustering
Differentially Private Densest Subgraph Detection
On the Global Optimality of Model-Agnostic Meta-Learning
Representation Tradeoffs for Hyperbolic Embeddings
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Tilted Sharpness-Aware Minimization
StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
Fast and space efficient string kernels using suffix arrays
Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series
A Precise Performance Analysis of Support Vector Regression
More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
Stochastic Interpolants with Data-Dependent Couplings
A Language Model's Guide Through Latent Space
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Decomposition of Graphic Design with Unified Multimodal Model
An Intriguing Property of Geophysics Inversion
Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems
Linear Bandits with Partially Observable Features
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
BINOCULARS for efficient, nonmyopic sequential experimental design
Bidirectional Model-based Policy Optimization
Conformal Prediction Sets for Graph Neural Networks
MARGE: Improving Math Reasoning with Guided Exploration
Falcon: Fast Visuomotor Policies via Partial Denoising
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Smoothed Adversarial Linear Contextual Bandits with Knapsacks
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks
Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements
Aligned Cross Entropy for Non-Autoregressive Machine Translation
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
Position: Leverage Foundational Models for Black-Box Optimization
Multiple Testing under Dependence via Semiparametric Graphical Models
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning
Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
NegMerge: Sign-Consensual Weight Merging for Machine Unlearning
Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds
Geometric Scattering for Graph Data Analysis
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Monte Carlo Variational Auto-Encoders
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models
Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
Sparse-to-dense Multimodal Image Registration via Multi-Task Learning
Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
Frustratingly Easy Transferability Estimation