yusuke1997/dblp-id-title huggingface dataset:
Certified Data Removal from Machine Learning Models
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games
Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations
RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization
Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
Long Horizon Temperature Scaling
Learning Declarative Control Rules for Constraint-BAsed Planning
Learning Routines for Effective Off-Policy Reinforcement Learning
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences
A Consistent Histogram Estimator for Exchangeable Graph Models
Detecting Out-of-distribution Data through In-distribution Class Prior
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Probabilistically Robust Learning: Balancing Average and Worst-case Performance
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
Nearly-tight Bounds for Deep Kernel Learning
Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Learning attentional policies for tracking and recognition in video with deep networks
Kernelized Support Tensor Machines
Optimization-Induced Graph Implicit Nonlinear Diffusion
CARTL: Cooperative Adversarially-Robust Transfer Learning
Learning Procedures by Environment-Driven Constructive Induction
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling
Sparsifying Networks via Subdifferential Inclusion
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Inflow, Outflow, and Reciprocity in Machine Learning
Fast evolutionary maximum margin clustering
Exploiting Ontology Structures and Unlabeled Data for Learning
Coco-Q: Learning in Stochastic Games with Side Payments
Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares
Description Based Text Classification with Reinforcement Learning
DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains
ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Automated Statistical Model Discovery with Language Models
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Unsupervised search-based structured prediction
INRFlow: Flow Matching for INRs in Ambient Space
Spectral Clustering via the Power Method - Provably
A Novel Sequential Coreset Method for Gradient Descent Algorithms
Hiring Under Uncertainty
Interactive Submodular Set Cover
Efficiently computing minimax expected-size confidence regions
Multiply-Robust Causal Change Attribution
Deconstructing the Goldilocks Zone of Neural Network Initialization
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
Prediction of Mortality and Intervention in COVID-19 Patients Using Generative Adversarial Networks
Learning a Diffusion Model Policy from Rewards via Q-Score Matching
A Hybrid Approach for Probabilistic Inference using Random Projections
Near-Optimal Linear Regression under Distribution Shift
Multigroup Robustness
Small Sample Decision tree Pruning
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Efficient Personalized Adaptation for Physiological Signal Foundation Model
Score-Based Causal Discovery of Latent Variable Causal Models
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
Retrieval Augmented Language Model Pre-Training
AdaBoost is not an Optimal Weak to Strong Learner
Provable Data Subset Selection For Efficient Neural Networks Training
Causal Imitation Learning under Temporally Correlated Noise
Fair Representation Learning through Implicit Path Alignment
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
Neural Implicit Dictionary Learning via Mixture-of-Expert Training
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Projection Pursuit Density Ratio Estimation
Curriculum learning
Latent Bandits
Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
Variational Autoencoders with Riemannian Brownian Motion Priors
AdaGrad Avoids Saddle Points
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
Heteroscedastic Gaussian process regression
The Missing Alignment Link of In-context Learning on Sequences
Archipelago: nonparametric Bayesian semi-supervised learning
New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
A Progressive Batching L-BFGS Method for Machine Learning
Learning Condensed Graph via Differentiable Atom Mapping for Reaction Yield Prediction
Model-free reinforcement learning as mixture learning
Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion
Linear and nonlinear generative probabilistic class models for shape contours
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
Improving Computational Complexity in Statistical Models with Local Curvature Information
In-Context Fine-Tuning for Time-Series Foundation Models
MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Q-Decomposition for Reinforcement Learning Agents
On User-Level Private Convex Optimization
Efficiently Solving MDPs with Stochastic Mirror Descent
On the Statistical Limits of Convex Relaxations
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models
Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Conditional Bernoulli Mixtures for Multi-label Classification
Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport
System Identification of Neural Systems: If We Got It Right, Would We Know?
Dink-Net: Neural Clustering on Large Graphs
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines
Divergence-Regularized Multi-Agent Actor-Critic
Counting atoms faster: policy-based nuclear magnetic resonance pulse sequencing for atomic abundance measurement
Power k-Means Clustering
Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning
Robust Random Cut Forest Based Anomaly Detection on Streams
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Clustering with Instance-level Constraints
Human Alignment of Large Language Models through Online Preference Optimisation
Non-convex Conditional Gradient Sliding
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
REG: Rectified Gradient Guidance for Conditional Diffusion Models
Empowering World Models with Reflection for Embodied Video Prediction
Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
Independent subspace analysis using geodesic spanning trees
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
A Spectral Approach to Gradient Estimation for Implicit Distributions
Dynamic Evaluation of Neural Sequence Models
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing
Tensor Decomposition via Joint Matrix Schur Decomposition
Apprenticeship Learning for Model Parameters of Partially Observable Environments
Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression
Text Classification Using Stochastic Keyword Generation
Unified Breakdown Analysis for Byzantine Robust Gossip
Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
Classification of Individuals with Complex Structure
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
On Multi-Armed Bandit with Impatient Arms
A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information Retrieval
HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks
Connecting Interpretability and Robustness in Decision Trees through Separation
Parallelizing Legendre Memory Unit Training
Simplifying Graph Convolutional Networks
Adaptive NeuroControl: How Black Box and Simple can it be
Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
Counterfactual Visual Explanations
The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models
Composite Marginal Likelihood Methods for Random Utility Models
Estimating Cosmological Parameters from the Dark Matter Distribution
Pointwise Binary Classification with Pairwise Confidence Comparisons
Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning
Automatic Discovery of the Statistical Types of Variables in a Dataset
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
Budgeted Distribution Learning of Belief Net Parameters
CuTS: Customizable Tabular Synthetic Data Generation
Parallel Neurosymbolic Integration with Concordia
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image Generators
Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning
Disentangled Sequential Autoencoder
Exploratory Study of a New Model for Evolving Networks
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
Learning to Retrieve Useful Information for Problem Solving
Unlocking Post-hoc Dataset Inference with Synthetic Data
Loss Function Learning for Domain Generalization by Implicit Gradient
Pi-DUAL: Using privileged information to distinguish clean from noisy labels
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
The adaptive k -meteorologists problem and its application to structure learning and feature selection in reinforcement learning
Neural Rough Differential Equations for Long Time Series
Online Latent Dirichlet Allocation with Infinite Vocabulary
Multi-period Trading Prediction Markets with Connections to Machine Learning
Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring
AUCμ: A Performance Metric for Multi-Class Machine Learning Models
A least squares formulation for canonical correlation analysis
Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning
DISTILL: Learning Domain-Specific Planners by Example
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM
Consistent regression when oblivious outliers overwhelm
Learning Input Encodings for Kernel-Optimal Implicit Neural Representations
Incremental Refinement of Approximate Domain Theories
Feature Multi-Selection among Subjective Features
Trustworthy Actionable Perturbations
Self-Attentive Associative Memory
Correcting Exposure Bias for Link Recommendation
Fairness in Reinforcement Learning
Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models
Enhanced statistical rankings via targeted data collection
The Non-linear F-Design and Applications to Interactive Learning
Acceleration via Fractal Learning Rate Schedules
Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
Learning Filaments
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
Lean and Mean Adaptive Optimization via Subset-Norm and Subspace-Momentum with Convergence Guarantees
Area Attention
Fast Population-Based Reinforcement Learning on a Single Machine
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
RRL: Resnet as representation for Reinforcement Learning
Action respecting embedding
Revisiting Context Aggregation for Image Matting
Learning Constrained Atoms
Learning to Act in Decentralized Partially Observable MDPs
Gaussian process nonparametric tensor estimator and its minimax optimality
Healing Products of Gaussian Process Experts
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Automated Loss function Search for Class-imbalanced Node Classification
On autoencoder scoring
A Bayesian Framework for Reinforcement Learning
Optimizing F-measure: A Tale of Two Approaches
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem
Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach
Layer-wise Quantization for Quantized Optimistic Dual Averaging
Learning the Valuations of a k-demand Agent
Optimal Convergence Rates for Agnostic Nyström Kernel Learning
Pliable Rejection Sampling
Sub-token ViT Embedding via Stochastic Resonance Transformers
Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective
Q-learning of sequential attention for visual object recognition from informative local descriptors
Learning Adaptive Lighting via Channel-Aware Guidance
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Efficient Algorithms for Sum-Of-Minimum Optimization
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
Multi-Agent Reinforcement Leraning for Traffic Light Control
A Modular Q-Learning Architecture for Manipulator Task Decomposition
Vision Transformers as Probabilistic Expansion from Learngene
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs
Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction
SHE: Streaming-media Hashing Retrieval
A Sample Efficient Conditional Independence Test in the Presence of Discretization
Whoever Started the interference Should End It: Guiding Data-Free Model Merging via Task Vectors
Active kernel learning
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Control and Realism: Best of Both Worlds in Layout-to-Image without Training
Projection techniques to update the truncated SVD of evolving matrices with applications
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization
A Contrastive Divergence for Combining Variational Inference and MCMC
From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments
Neuron Dependency Graphs: A Causal Abstraction of Neural Networks
Using Inverse Resolution to Learn Relations from Experiments
Stochastically Dominant Distributional Reinforcement Learning
Utile distinction hidden Markov models
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
Adaptive Neural Networks for Efficient Inference
Parameter-Efficient Fine-Tuning with Controls
A Deep Learning Approach to Unsupervised Ensemble Learning
Data Efficient Neural Scaling Law via Model Reusing
Nonparametric classification with polynomial MPMC cascades
Deep Principal Support Vector Machines for Nonlinear Sufficient Dimension Reduction
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture
Performative Prediction with Bandit Feedback: Learning through Reparameterization
How Hard is Inference for Structured Prediction?
Persistence weighted Gaussian kernel for topological data analysis
Unfolding Latent Tree Structures using 4th Order Tensors
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent
MANSA: Learning Fast and Slow in Multi-Agent Systems
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
Bayesian Continuous-Time Tucker Decomposition
Universal Neural Optimal Transport
Differentially Private Bias-Term Fine-tuning of Foundation Models
The Kendall and Mallows Kernels for Permutations
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
When is Transfer Learning Possible?
GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything
Statistics and Samples in Distributional Reinforcement Learning
X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation
Bias Correction in Classification Tree Construction
Sharp Optimality of Simple, Plug-in Estimation of the Fisher Information of a Smoothed Density
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
Variational Inference and Model Selection with Generalized Evidence Bounds
Learning to Route in Similarity Graphs
Adaptive Data Analysis with Correlated Observations
CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features
Control of Memory, Active Perception, and Action in Minecraft
Continuous-Time Flows for Efficient Inference and Density Estimation
Born-Again Tree Ensembles
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
A Bayesian Framework to Integrate Symbolic and Neural Learning
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations
Differentiable plasticity: training plastic neural networks with backpropagation
Mixture of Lookup Experts
Online Learning of Eigenvectors
An EM Approach to Non-autoregressive Conditional Sequence Generation
One Meta-tuned Transformer is What You Need for Few-shot Learning
Good Initializations of Variational Bayes for Deep Models
Can Forward Gradient Match Backpropagation?
Two-dimensional solution path for support vector regression
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
The Numerical Stability of Hyperbolic Representation Learning
Is Learning Rate a Good Performance Criterion for Learning?
Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise
Progressive Graph Learning for Open-Set Domain Adaptation
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
On collapsed representation of hierarchical Completely Random Measures
The Inverse Regression Topic Model
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Perceptron Based Learning with Example Dependent and Noisy Costs
Distributed training of Large-scale Logistic models
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
Discounted Adaptive Online Learning: Towards Better Regularization
Generalized additive models via direct optimization of regularized decision stump forests
Fair Resource Allocation in Multi-Task Learning
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method
Temporal Difference Learning for Model Predictive Control
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Graph World Model
Data association for topic intensity tracking
Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing
SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding
Support Vector Machines as Probabilistic Models
Learning Granger Causality for Hawkes Processes
A Unifeid Bias-Variance Decomposition and its Applications
A Knowledge-Level Analysis of Informing
Probabilistic Generating Circuits
Implicit Regularization of Random Feature Models
Hamiltonian Monte Carlo Without Detailed Balance
Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
Optimization for Amortized Inverse Problems
Splitting & Integrating: Out-of-Distribution Detection via Adversarial Gradient Attribution
Attributes Shape the Embedding Space of Face Recognition Models
Multi-step Greedy Reinforcement Learning Algorithms
Harmonizing Generalization and Personalization in Federated Prompt Learning
Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data
DAGs with No Curl: An Efficient DAG Structure Learning Approach
Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport
A Statistical Investigation of Long Memory in Language and Music
Graph Convolutional Gaussian Processes
Dueling Network Architectures for Deep Reinforcement Learning
Lightweight Protocols for Distributed Private Quantile Estimation
Efficient Lottery Ticket Finding: Less Data is More
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning
Simpler knowledge-based support vector machines
Risk minimization, probability elicitation, and cost-sensitive SVMs
One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams
Compositional Risk Minimization
Cluster-Aware Similarity Diffusion for Instance Retrieval
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction