yusuke1997/dblp-id-title huggingface dataset:
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment
The Natural Language of Actions
Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone
Order Constraints in Optimal Transport
Fast Information-theoretic Bayesian Optimisation
Top-k eXtreme Contextual Bandits with Arm Hierarchy
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
FrameBridge: Improving Image-to-Video Generation with Bridge Models
An Analysis of the Convergence of Graph Laplacians
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
Adaptive Localization of Knowledge Negation for Continual LLM Unlearning
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations
The Flexibility of Speculative Refinement
Vector-valued Manifold Regularization
Curvature-Exploiting Acceleration of Elastic Net Computations
Generalized Beliefs for Cooperative AI
Convex variational Bayesian inference for large scale generalized linear models
An Evolutionary Approach to Evidence-Based Learning of Deterministic Finite Automata
Scaling of Class-wise Training Losses for Post-hoc Calibration
Differential Privacy Guarantees of Markov Chain Monte Carlo Algorithms
Bayesian Optimization of Combinatorial Structures
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Approximating Orthogonal Matrices with Effective Givens Factorization
Compositional Condition Question Answering in Tabular Understanding
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Learning the Behavior of Dynamical Systems form Examples
Graph Adversarial Diffusion Convolution
The Bayesian backfitting relevance vector machine
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ε -7/4 ) Complexity
On discriminative learning of prediction uncertainty
SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes
QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
Using Learning for Approximation in Stochastic Processes
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
EMI: Exploration with Mutual Information
Quantum Speedup for Hypergraph Sparsification
Streaming Bayesian Deep Tensor Factorization
A Closer Look at Generalized BH Algorithm for Out-of-Distribution Detection
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction
Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport
Hierarchical Importance Weighted Autoencoders
Learning and Forgetting Unsafe Examples in Large Language Models
Efficient Online Learning for Dynamic k-Clustering
Optimal Decision Tree Pruning Revisited: Algorithms and Complexity
Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011
On Matching Pursuit and Coordinate Descent
A Dynamical System Perspective for Lipschitz Neural Networks
A New Analysis of Co-Training
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
Meta-Learning the Inductive Bias of Simple Neural Circuits
Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection
Addressing Function Approximation Error in Actor-Critic Methods
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Learning to Recognize Plans Involving Affect
Analyzing Uncertainty in Neural Machine Translation
Enriching Vocabularies by Generalizing Explanation Structures
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation
Complexity Analysis of the Lasso Regularization Path
Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
On the Surrogate Gap between Contrastive and Supervised Losses
FACT: A Diagnostic for Group Fairness Trade-offs
Combinatorial Neural Bandits
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
Learn Singularly Perturbed Solutions via Homotopy Dynamics
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Learning to Disentangle Factors of Variation with Manifold Interaction
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
A statistical approach to rule learning
TS-SNN: Temporal Shift Module for Spiking Neural Networks
Core Vector Regression for very large regression problems
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
The cross entropy method for classification
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
Eigenvalue Sensitive Feature Selection
Bayesian CCA via Group Sparsity
Fractional Denoising for 3D Molecular Pre-training
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
Learning Polynomials with Neural Networks
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Variational Inference for sparse network reconstruction from count data
Analogy-preserving Semantic Embedding for Visual Object Categorization
Self-attention Networks Localize When QK-eigenspectrum Concentrates
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Efficient Quantum Algorithms for Quantum Optimal Control
Prediction via Shapley Value Regression
Black-box Certification and Learning under Adversarial Perturbations
Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
Error Bounds for Approximate Policy Iteration
Robust Learning with the Hilbert-Schmidt Independence Criterion
Distilling the Knowledge in Data Pruning
Trusted Multi-View Classification with Expert Knowledge Constraints
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA
Fair and Fast k-Center Clustering for Data Summarization
Who Needs to Know? Minimal Knowledge for Optimal Coordination
Moniqua: Modulo Quantized Communication in Decentralized SGD
Regularized Online Allocation Problems: Fairness and Beyond
Reducing sequential change detection to sequential estimation
Learning Deep Time-index Models for Time Series Forecasting
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
Stochastic Regret Minimization in Extensive-Form Games
Non-Linear Stationary Subspace Analysis with Application to Video Classification
Feature Space Particle Inference for Neural Network Ensembles
Some Interesting Properties of a Connectionist Inductive Learning System
Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares
DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
The Dynamics of Learning: A Random Matrix Approach
Minimalist Concept Erasure in Generative Models
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Certified Robust Neural Networks: Generalization and Corruption Resistance
Teaching with Limited Information on the Learner's Behaviour
RuleAdapter: Dynamic Rules for training Safety Reward Models in RLHF
Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs
SVM-based generalized multiple-instance learning via approximate box counting
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
Multi-Task Learning of Gaussian Graphical Models
Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005), Bonn, Germany, August 7-11, 2005
Delving into Differentially Private Transformer
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
Mean Reversion with a Variance Threshold
Rethinking Benign Overfitting in Two-Layer Neural Networks
Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Heteroscedastic Sequences: Beyond Gaussianity
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Towards Stable and Efficient Adversarial Training against l 1 Bounded Adversarial Attacks
Repoformer: Selective Retrieval for Repository-Level Code Completion
Deconstructing the Digit Recognition Problem
VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
DITTO: Diffusion Inference-Time T-Optimization for Music Generation
Combining Multiple Perspectives
Min-Max Problems on Factor Graphs
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Bandits with Knapsacks: Advice on Time-Varying Demands
Learning to Avoid Obstacles Through Reinforcement
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark
Breaking the Small Cluster Barrier of Graph Clustering
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Extensions of marginalized graph kernels
Hidden process models
Sobolev Space Regularised Pre Density Models
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Residual Algorithms: Reinforcement Learning with Function Approximation
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
How Expressive are Knowledge Graph Foundation Models?
Buying Information for Stochastic Optimization
Learning with Expected Signatures: Theory and Applications
Applying Abstraction and Simplification to Learn in Intractable Domains
Learning without Isolation: Pathway Protection for Continual Learning
Learning Deep Neural Networks under Agnostic Corrupted Supervision
Accelerating Large Language Model Reasoning via Speculative Search
Understanding Black-box Predictions via Influence Functions
A Hybrid Approach to Guaranteed Effective Control Strategies
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
Human Boosting
Full-Atom Peptide Design based on Multi-modal Flow Matching
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
Predicting relative performance of classifiers from samples
Extrapolated Random Tree for Regression
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Variability Bias and Category Learning
A Normative Examination of Ensemble Learning Algorithms
How Transformers Learn Causal Structure with Gradient Descent
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Mean-Variance Optimization in Markov Decision Processes
Obtaining Adjustable Regularization for Free via Iterate Averaging
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Fragments to Facts: Partial-Information Fragment Inference from LLMs
Structure-Aware Transformer for Graph Representation Learning
An Infinite Latent Attribute Model for Network Data
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
Learning to Bid in Repeated First-Price Auctions with Budgets
Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games
SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution
Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints
Optimization Planning for 3D ConvNets
Verifying message-passing neural networks via topology-based bounds tightening
When and How Mixup Improves Calibration
Approximate maximum margin algorithms with rules controlled by the number of mistakes
An Information-Theoretic Definition of Similarity
A causal approach to hierarchical decomposition of factored MDPs
Bayesian Model Selection for Change Point Detection and Clustering
Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning
Learning Calibratable Policies using Programmatic Style-Consistency
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Learning from a Learner
Multi-view clustering via canonical correlation analysis
DiCE: The Infinitely Differentiable Monte Carlo Estimator
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety
Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning
Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
Input Perturbation Reduces Exposure Bias in Diffusion Models
Population Size in classifier Systems
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
Thompson Sampling via Local Uncertainty
Revisiting Consistency Regularization for Deep Partial Label Learning
Constructing informative priors using transfer learning
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Emerging Convolutions for Generative Normalizing Flows
Reinforcement Learning in POMDPs with Function Approximation
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
Reducing Misclassification Costs
Detecting and Identifying Selection Structure in Sequential Data
Fast Timing-Conditioned Latent Audio Diffusion
Online Learning under Delayed Feedback
Off-Policy Temporal Difference Learning with Function Approximation
An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process
Stochastic Gradient MCMC Methods for Hidden Markov Models
Image-to-Markup Generation with Coarse-to-Fine Attention
Smooth Operators
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning
Contrasting Multiple Representations with the Multi-Marginal Matching Gap
Compressed sensing and Bayesian experimental design
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models
Efficient Multi-label Classification with Many Labels
Safe Reinforcement Learning in Constrained Markov Decision Processes
ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
TSLANet: Rethinking Transformers for Time Series Representation Learning
Gaussian Process Regression Networks
Using Sampling and Queries to Extract Rules from Trained Neural Networks
Efficient Gradient-Free Variational Inference using Policy Search
Achieving High Accuracy with PINNs via Energy Natural Gradient Descent
Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
Parameter-Efficient Fine-Tuning of State Space Models
Protein Folding: Symbolic Refinement Competes with Neural Networks
Automatic Data Augmentation via Invariance-Constrained Learning
Manifold-valued Dirichlet Processes
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Causal Discovery from Conditionally Stationary Time Series
Multi Resolution Analysis (MRA) for Approximate Self-Attention
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation
Normalizing Flows for Interventional Density Estimation
Probabilistic Programs with Stochastic Conditioning
Using Genetic Algorithms to Learn Disjunctive Rules from Examples
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Nearest hyperdisk methods for high-dimensional classification
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
A Dynamical Model of Neural Scaling Laws
Accelerating Iterative Retrieval-augmented Language Model Serving with Speculation
On optimization methods for deep learning
Tractable learning of large Bayes net structures from sparse data
An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization
Active Learning for Multi-Objective Optimization
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation
Improved Training Via Incremental Learning
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Convariates
Robust and Scalable Bayesian Online Changepoint Detection
Accurate Robust and Efficient Error Estimation for Decision Trees
Identifying suspicious URLs: an application of large-scale online learning
Generalizing Apprenticeship Learning across Hypothesis Classes
Stacking Bagged and Dagged Models
Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation
Scaling Large Motion Models with Million-Level Human Motions
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Learning While Playing in Mean-Field Games: Convergence and Optimality
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees
Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective
A Kernel Stein Test of Goodness of Fit for Sequential Models
Causal Modeling for Fairness In Dynamical Systems
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
Learning high-order MRF priors of color images
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
Prababilistic Instance-Based Learning
Interpreting Equivariant Representations
Avoiding Catastrophe in Online Learning by Asking for Help
Multi-Frequency Phase Synchronization
Nonnegative Sparse PCA with Provable Guarantees
Temporal Difference Learning as Gradient Splitting
Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
Kernel Mean Estimation and Stein Effect
Chain-of-Thought Predictive Control
Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond
Stable-Predictive Optimistic Counterfactual Regret Minimization
Information Obfuscation of Graph Neural Networks
Stochastic positional embeddings improve masked image modeling
Selective Response Strategies for GenAI
Inverse Flow and Consistency Models
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning
PoisonedEye: Knowledge Poisoning Attack on Retrieval-Augmented Generation based Large Vision-Language Models
Fast Image Tagging
Scaling Probabilistic Circuits via Monarch Matrices
Active learning of label ranking functions
On the relation between multi-instance learning and semi-supervised learning
An Investigation of Model-Free Planning
PowerNorm: Rethinking Batch Normalization in Transformers
Learning random walk models for inducing word dependency distributions
Acceleration through spectral density estimation
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Towards Scalable and Versatile Weight Space Learning
Regret Minimization with Performative Feedback
Offline Training of Language Model Agents with Functions as Learnable Weights
UniAudio: Towards Universal Audio Generation with Large Language Models
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Adaptive Sampled Softmax with Kernel Based Sampling
Variational Bayesian dropout: pitfalls and fixes
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Markov Chain Monte Carlo Sampling using Direct Search Optimization
Solving Poisson Equations using Neural Walk-on-Spheres