yusuke1997/dblp-id-title huggingface dataset:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
A Personalized Affective Memory Model for Improving Emotion Recognition
Trustless Audits without Revealing Data or Models
Polarity Is All You Need to Learn and Transfer Faster
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine
Adversarial Learning with Local Coordinate Coding
Two Algorithms That Learn DNF by Discovering Relevant Features
Adaptive-Gradient Policy Optimization: Enhancing Policy Learning in Non-Smooth Differentiable Simulations
Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation
Incorporating probabilistic domain knowledge into deep multiple instance learning
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Active Heteroscedastic Regression
Active Learning for Cost-Sensitive Classification
Navigating Scaling Laws: Compute Optimality in Adaptive Model Training
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
How Bad is Top-K Recommendation under Competing Content Creators?
Learning Representations that Support Extrapolation
Support vector machine learning for interdependent and structured output spaces
Estimation of Markov Chain via Rank-constrained Likelihood
Goal Misgeneralization in Deep Reinforcement Learning
PIPA: Preference Alignment as Prior-Informed Statistical Estimation
Causal Representation Learning from Multiple Distributions: A General Setting
VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth
Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models
Adversarial Vulnerability of Randomized Ensembles
Conformal Prediction with Missing Values
Search Control, Utility, and Concept Induction
PASS: Private Attributes Protection with Stochastic Data Substitution
Autoformulation of Mathematical Optimization Models Using LLMs
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Mechanistic Mode Connectivity
Online Learning with Sleeping Experts and Feedback Graphs
More Information Supervised Probabilistic Deep Face Embedding Learning
Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme
Generative Graph Dictionary Learning
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
Discovering Support and Affiliated Features from Very High Dimensions
Federated Self-Explaining GNNs with Anti-shortcut Augmentations
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Hardness and Algorithms for Robust and Sparse Optimization
BSO: Binary Spiking Online Optimization Algorithm
Comparing clusterings: an axiomatic view
On Autoencoders and Score Matching for Energy Based Models
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
Position: Topological Deep Learning is the New Frontier for Relational Learning
AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
Learning to Probabilistically Identify Authoritative Documents
Faster Kernel Matrix Algebra via Density Estimation
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
Finite-Time Convergence in Continuous-Time Optimization
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
The Characterisation of Predictive Accuracy and Decision Combination
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks
ProSec: Fortifying Code LLMs with Proactive Security Alignment
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
Contrastive Localized Language-Image Pre-Training
Threshold Influence Model for Allocating Advertising Budgets
Graph Generative Pre-trained Transformer
DriveGPT: Scaling Autoregressive Behavior Models for Driving
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
HyperHyperNetwork for the Design of Antenna Arrays
A Kronecker-factored approximate Fisher matrix for convolution layers
DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Learning as search optimization: approximate large margin methods for structured prediction
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
An Approach to Concept Learning Based on Term Generalization
Deep Generative Learning via Schrödinger Bridge
No-Regret Exploration in Goal-Oriented Reinforcement Learning
An All-Atom Generative Model for Designing Protein Complexes
Robust Graph Mode Seeking by Graph Shift
Reward-Mixing MDPs with Few Latent Contexts are Learnable
A Modern Look at the Relationship between Sharpness and Generalization
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Combinatorial Multi-Armed Bandit: General Framework and Applications
THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure
On Approximation Guarantees for Greedy Low Rank Optimization
Capturing topical content with frequency and exclusivity
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
Uncertainty Estimation by Density Aware Evidential Deep Learning
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Pareto Regret Analyses in Multi-objective Multi-armed Bandit
Understanding the unstable convergence of gradient descent
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Narrow Margins: Classification, Margins and Fat Tails
Faster Privacy Accounting via Evolving Discretization
Leveraging Offline Data in Online Reinforcement Learning
Investigating the Role of Model-Based Learning in Exploration and Transfer
An Iterative Locally Linear Embedding Algorithm
Linear Off-Policy Actor-Critic
Stochastic Low-Rank Kernel Learning for Regression
Emergence of Sparse Representations from Noise
Flexibly Fair Representation Learning by Disentanglement
Task Understanding from Confusing Multi-task Data
Semi-supervised learning using randomized mincuts
Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference
Zero Shot Generalization of Vision-Based RL Without Data Augmentation
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining
Deep Canonical Correlation Analysis
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Estimating relatedness via data compression
Nonlinear transformers can perform inference-time feature learning
Structure learning of Bayesian networks using constraints
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Fair Off-Policy Learning from Observational Data
Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging
Online Multi-Kernel Learning with Graph-Structured Feedback
Local Algorithms for Finding Densely Connected Clusters
On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning
When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
Geometric Algebra Planes: Convex Implicit Neural Volumes
A Bayesian Approach to Model Learning in Non-Markovian Environments
Adaptive Text Watermark for Large Language Models
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Concentration Distribution Learning from Label Distributions
Quantum Algorithm for Online Exp-concave Optimization
Predictive Entropy Search for Multi-objective Bayesian Optimization
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
Algorithms for $\ell_p$ Low-Rank Approximation
FloWaveNet : A Generative Flow for Raw Audio
Simple and Deep Graph Convolutional Networks
Discovering Discrete Latent Topics with Neural Variational Inference
A Unified Robust Classification Model
OmiAD: One-Step Adaptive Masked Diffusion Model for Multi-class Anomaly Detection via Adversarial Distillation
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
A Provable Decision Rule for Out-of-Distribution Detection
A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums
Graph-Triggered Rising Bandits
How Smooth Is Attention?
Sequence Generation with Mixed Representations
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Preferential Bayesian Optimization
Distributed Clustering via LSH Based Data Partitioning
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Total Variation Floodgate for Variable Importance Inference in Classification
Point Cloud Dataset Distillation
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Private Reinforcement Learning with PAC and Regret Guarantees
Zero-Inflated Bandits
Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Rank minimization via online learning
Propagating distributions on a hypergraph by dual information regularization
Implicit Regularization in Variational Bayesian Matrix Factorization
Self-Supervised Exploration via Disagreement
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Online metric algorithms with untrusted predictions
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Kernel Adaptive Metropolis-Hastings
An accelerated gradient method for trace norm minimization
On the Maximal Local Disparity of Fairness-Aware Classifiers
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images
Counterfactual Metarules for Local and Global Recourse
Receptive Fields As Experts in Convolutional Neural Architectures
Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation
MetAug: Contrastive Learning via Meta Feature Augmentation
Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation
FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning
Unsupervised hierarchical modeling of locomotion styles
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Relational Instance-Based Learning
What Makes In-context Learning Effective for Mathematical Reasoning
Certified Adversarial Robustness via Randomized Smoothing
Clustering in the Presence of Background Noise
Curious Replay for Model-based Adaptation
Off-Policy Evaluation for Large Action Spaces via Embeddings
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
Controlling Large Language Model with Latent Action
Disentangling Disease-related Representation from Obscure for Disease Prediction
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Domain Adaptation for Time Series Forecasting via Attention Sharing
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time
Memory-Efficient Pipeline-Parallel DNN Training
GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes
Neural Encoding and Decoding at Scale
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Fast particle smoothing: if I had a million particles
Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization
Autonomous geometric precision error estimation in low-level computer vision tasks
The Benefits of Model-Based Generalization in Reinforcement Learning
PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity
Direct Bayes Point Machines
Cost-sensitive learning with conditional Markov networks
DsDm: Model-Aware Dataset Selection with Datamodels
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
NeuronTune: Towards Self-Guided Spurious Bias Mitigation
Intrinsic Reward Driven Imitation Learning via Generative Model
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Local distance preservation in the GP-LVM through back constraints
On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving
Voting Nearest-Neighbor Subclassifiers
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation
Understanding the Effects of Iterative Prompting on Truthfulness
Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning
Position: Towards Unified Alignment Between Agents, Humans, and Environment
INSPECTRE: Privately Estimating the Unseen
Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
Position: Embracing Negative Results in Machine Learning
Best of Both Worlds: Regret Minimization versus Minimax Play
Automatic Brain Localization in Fetal MRI Using Superpixel Graphs
Theory on Forgetting and Generalization of Continual Learning
Structured Variationally Auto-encoded Optimization
Discriminative Complementary-Label Learning with Weighted Loss
Discovering Admissible Search Heuristics by Abstracting and Optimizing
Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints
Boosting for transfer learning
Quantifying the Variability Collapse of Neural Networks
The Pitfalls of Next-Token Prediction
Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression
Automatic discovery and transfer of MAXQ hierarchies
Scalable Variational Inference in Log-supermodular Models
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions
A Spectral Learning Approach to Range-Only SLAM
Optimizing for the Future in Non-Stationary MDPs
Asynchronous Distributed Variational Gaussian Process for Regression
Representative Language Generation
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Speeding-Up Hoeffding-Based Regression Trees With Options
Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization
Fast support vector machine training and classification on graphics processors
Privacy Profiles for Private Selection
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Overcoming Incomplete Perception with Utile Distinction Memory
Differentially Private Sliced Wasserstein Distance
Infinite Markov-Switching Maximum Entropy Discrimination Machines
Robust Subspace Segmentation by Low-Rank Representation
Behavioral Exploration: Learning to Explore via In-Context Adaptation
Be Confident: Uncovering Overfitting in MLLM Multi-Task Tuning
Learning Preconditioners for Conjugate Gradient PDE Solvers
Continuous-time Lower Bounds for Gradient-based Algorithms
Long-Form Speech Generation with Spoken Language Models
A Transformational Approach to Constructive Induction
Marginalized Denoising Autoencoders for Domain Adaptation
Bi-perspective Splitting Defense: Achieving Clean-Seed-Free Backdoor Security
Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Bayesian Optimization with Inequality Constraints
Multilabel Classification with Group Testing and Codes
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
See More Details: Efficient Image Super-Resolution by Experts Mining
Self-Tuning for Data-Efficient Deep Learning
Maximum Variance Correction with Application to A* Search
On the Sample Complexity of Adversarial Multi-Source PAC Learning
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
Robust Matrix Completion and Corrupted Columns
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Behavioral Cloning of Student Pilots with Modular Neural Networks
Familywise Error Rate Control by Interactive Unmasking
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
Partition Functions from Rao-Blackwellized Tempered Sampling
GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models
BSLoRA: Enhancing the Parameter Efficiency of LoRA with Intra-Layer and Inter-Layer Sharing
FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Translation Equivariant Transformer Neural Processes
Constraint-based graph network simulator
Are Equivariant Equilibrium Approximators Beneficial?
Hierarchical Self-Organization in Genetic programming
Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces
Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Active Learning with Selective Time-Step Acquisition for PDEs
Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
The Blind Leading the Blind: Mutual Refinement of Approximate Theories
Estimation of Bounds on Potential Outcomes For Decision Making
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Beamforming using the relevance vector machine
Towards Practical Mean Bounds for Small Samples
LEEP: A New Measure to Evaluate Transferability of Learned Representations
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Classifier Systems with Hamming Weights
Masked Face Recognition with Generative-to-Discriminative Representations
Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization
Emulating the Expert: Inverse Optimization through Online Learning
Classification-based Policy Iteration with a Critic
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Graph Mixture Density Networks
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Homomorphic Sensing
Exploring Representations and Interventions in Time Series Foundation Models
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Normalized Loss Functions for Deep Learning with Noisy Labels
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems
Learning Deep Architectures via Generalized Whitened Neural Networks
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
Predictive state representations with options
Efficient Euclidean Projections onto the Intersection of Norm Balls
A Knowledge Intensive Approach to Concept Induction
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Simultaneous Inference for Massive Data: Distributed Bootstrap
A Convex Optimization Framework for Bi-Clustering
Understanding the Training Speedup from Sampling with Approximate Losses
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
When to retrain a machine learning model
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Learning to Reuse Policies in State Evolvable Environments
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
Symmetric Matrix Completion with ReLU Sampling
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Network Effects in Performative Prediction Games
Scalable Nearest Neighbor Search for Optimal Transport
Binary Embedding: Fundamental Limits and Fast Algorithm
Region-Based Semantic Factorization in GANs
Flexible Mixture Model for Collaborative Filtering
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching
Learning spectral graph transformations for link prediction
Modular Multitask Reinforcement Learning with Policy Sketches
Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
Accelerating Convergence in Bayesian Few-Shot Classification
Entropy Minimization In Emergent Languages
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data
A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization
Towards Unified Multi-granularity Text Detection with Interactive Attention
Improving Flow Matching by Aligning Flow Divergence
On Second-Order Group Influence Functions for Black-Box Predictions
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?
Accelerating Federated Learning with Quick Distributed Mean Estimation