yusuke1997/dblp-id-title huggingface dataset:
Feature Clustering for Support Identification in Extreme Regions
Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters
Toward a Model of Mind as a Laissez-Faire Economy of Idiots
Soft Threshold Weight Reparameterization for Learnable Sparsity
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift
Certified Robustness Against Natural Language Attacks by Causal Intervention
An in depth look at the Procrustes-Wasserstein distance: properties and barycenters
How Free is Parameter-Free Stochastic Optimization?
An Adaptive Learning Rate for Stochastic Variational Inference
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
Unconstrained Online Learning with Unbounded Losses
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
Generalizing Gaussian Smoothing for Random Search
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations
A dual coordinate descent method for large-scale linear SVM
Predictive automatic relevance determination by expectation propagation
Mixtures of Rectangles: Interpretable Soft Clustering
LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Bayesian Learning of Recursively Factored Environments
ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks
Characterizing Implicit Bias in Terms of Optimization Geometry
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Discovering Object-Centric Generalized Value Functions From Pixels
An Approach to Data Reduction and Clustering with Theoretical Guarantees
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection
Deliberation in Latent Space via Differentiable Cache Augmentation
Recovering temporally rewiring networks: a model-based approach
The Effective Size of a Neural Network: A Principal Component Approach
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Regularized Optimal Transport is Ground Cost Adversarial
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D
A Nearly-Optimal Bound for Fast Regression with ℓ ∞ Guarantee
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
Privately Learning Markov Random Fields
Generative Point Cloud Registration
Object correspondence as a machine learning problem
Cluster analysis of heterogeneous rank data
Bayesian Optimization under Stochastic Delayed Feedback
Mixed batches and symmetric discriminators for GAN training
FeatSharp: Your Vision Model Features, Sharper
Inexact Tensor Methods with Dynamic Accuracies
Discount Factor as a Regularizer in Reinforcement Learning
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Learning to Plan in Complex Domains
HashAttention: Semantic Sparsity for Faster Inference
Ranking-based Client Imitation Selection for Efficient Federated Learning
Design Rationale Capture as Knowledge Acquisition
Cost-Sensitive Tree of Classifiers
Multi-Dimensional Classification via Sparse Label Encoding
Watermark Stealing in Large Language Models
Correlation Clustering in Constant Many Parallel Rounds
A Neural Autoregressive Approach to Collaborative Filtering
Supervised Quantile Normalization for Low Rank Matrix Factorization
Controlled Text Generation with Natural Language Instructions
An optimal algorithm for the Thresholding Bandit Problem
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Pairwise Comparison of Hypotheses in Evolutionary Learning
Nearest Neighbors Using Compact Sparse Codes
Reinforcement Learning with Random Time Horizons
Position: Automatic Environment Shaping is the Next Frontier in RL
Task Space Retrieval Using Inverse Feedback Control
Prospective Side Information for Latent MDPs
Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces
A Consistent and Efficient Evaluation Strategy for Attribution Methods
Sparse coding for multitask and transfer learning
Risk and cross validation in ridge regression with correlated samples
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Block-quantized kernel matrix for fast spectral embedding
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
Rejecting Hallucinated State Targets during Planning
Learning Football Evaluation for a Walking Robot
Neural Logic Reinforcement Learning
ABNet: Adaptive explicit-Barrier Net for Safe and Scalable Robot Learning
Ensemble Bootstrapping for Q-Learning
Privacy-Preserving Feature Selection with Secure Multiparty Computation
Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
HyperIV: Real-time Implied Volatility Smoothing
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
Noise-Resistant Classification
Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation
On the Role of Label Noise in the Feature Learning Process
Conformal Prediction with Learned Features
Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective
3D Infomax improves GNNs for Molecular Property Prediction
Towards Better Robust Generalization with Shift Consistency Regularization
Dual Operating Modes of In-Context Learning
Online Platt Scaling with Calibeating
CLUSTSEG: Clustering for Universal Segmentation
AlphaFold Meets Flow Matching for Generating Protein Ensembles
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
Compositional Video Synthesis with Action Graphs
Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Exploring Correlations of Self-Supervised Tasks for Graphs
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
A Closer Look at Smoothness in Domain Adversarial Training
Integer linear programming inference for conditional random fields
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Learnable Group Transform For Time-Series
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Authorship verification as a one-class classification problem
Decision Tree Induction of 3-D Manufacturing Features
Extended Unconstrained Features Model for Exploring Deep Neural Collapse
Inferring Reduced Ordered Decision Graphs of Minimum Description Length
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
Test-Time Adaptation with Binary Feedback
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning
Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Stochastic Weakly Convex Optimization beyond Lipschitz Continuity
Improving Bi-level Optimization Based Methods with Inspiration from Humans' Classroom Study Techniques
TLLC: Transfer Learning-based Label Completion for Crowdsourcing
Probably Approximately Metric-Fair Learning
Real-time and Explainable Detection of Epidemics with Global News Data
Modeling for Optimal Probability Prediction
Timer: Generative Pre-trained Transformers Are Large Time Series Models
Low-Rank Thinning
Distributionally Robust Data Valuation
UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process
Canonical Trends: Detecting Trend Setters in Web Data
Unveiling the Latent Space Geometry of Push-Forward Generative Models
Distribution calibration for regression
Sparse Autoencoders, Again?
Diversified Flow Matching with Translation Identifiability
Reinforcement Learning in Low-rank MDPs with Density Features
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Online Matrix Completion: A Collaborative Approach with Hott Items
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Communication-Efficient Distributed SVD via Local Power Iterations
CollabLLM: From Passive Responders to Active Collaborators
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank
Position: Graph Foundation Models Are Already Here
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment
DoRA: Weight-Decomposed Low-Rank Adaptation
Robust feature extraction via information theoretic learning
An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function
Policy Design for Two-sided Platforms with Participation Dynamics
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
Uncovering the Temporal Dynamics of Diffusion Networks
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
The Limits of Maxing, Ranking, and Preference Learning
Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Stochastic Deep Restoration Priors for Imaging Inverse Problems
Momentum Residual Neural Networks
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
Nearly Linear Row Sampling Algorithm for Quantile Regression
Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces
Minimax Regret Bounds for Reinforcement Learning
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling
Generating Error Candidates for Assigning Blame in a Knowledge Base
MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification
Information Discrepancy in Strategic Learning
SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
IncDSI: Incrementally Updatable Document Retrieval
Knows what it knows: a framework for self-aware learning
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
A Theoretical Comparison of Graph Neural Network Extensions
Don't trust your eyes: on the (un)reliability of feature visualizations
Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training
Estimating Causal Effects using a Multi-task Deep Ensemble
Dynamic Survival Analysis with Controlled Latent States
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression
Global optimality of Elman-type RNNs in the mean-field regime
Exploring Model Dynamics for Accumulative Poisoning Discovery
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Learning from Contagion (Without Timestamps)
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching
PASOA- PArticle baSed Bayesian Optimal Adaptive design
From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set
Interpretable Off-Policy Learning via Hyperbox Search
Transformative or Conservative? Conservation laws for ResNets and Transformers
Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Graph-Coupled Oscillator Networks
Regularized Q-learning through Robust Averaging
Towards Certified Unlearning for Deep Neural Networks
Is Overfitting Necessary for Implicit Video Representation?
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking
Practical Reinforcement Learning in Continuous Spaces
Exploiting Code Symmetries for Learning Program Semantics
Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
Reverse Engineering ℓ p attacks: A block-sparse optimization approach with recovery guarantees
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World
Sampling-based Multi-dimensional Recalibration
Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning
Regularized Submodular Maximization at Scale
Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
Mitigating over-Exploration in Latent Space Optimization using les
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
Quadruple Attention in Many-body Systems for Accurate Molecular Property Predictions
On Proximal Policy Optimization's Heavy-tailed Gradients
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
Privacy-Preserving Video Classification with Convolutional Neural Networks
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006
Performative Prediction
Learning Hierarchical Performance Knowledge by Observation
Stability and Multigroup Fairness in Ranking with Uncertain Predictions
Video Prediction with Appearance and Motion Conditions
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks
Incremental Clustering by Minimizing Representation Length
Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification
A Framework for Discovering Discrete Event Models
Bayesian Imitation Learning for End-to-End Mobile Manipulation
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Accelerated Flow for Probability Distributions
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies
Faster Sampling via Stochastic Gradient Proximal Sampler
Spectral Clustering of Signed Graphs via Matrix Power Means
Imitation Learning Applied to Embodied Conversational Agents
Neighbor search with global geometry: a minimax message passing algorithm
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
Learning on the Test Data: Leveraging Unseen Features
Communications Inspired Linear Discriminant Analysis
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
Linear Spectral Estimators and an Application to Phase Retrieval
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
Learning linear dynamical systems without sequence information
Conjugate Energy-Based Models
Discovering Spoofing Attempts on Language Model Watermarks
Interactive Object Placement with Reinforcement Learning
Can DBNNs Robust to Environmental Noise for Resource-constrained Scenarios?
Understanding Self-Distillation in the Presence of Label Noise
Hyperbolic Optimizer as a Dynamical System
Explainable k-Means and k-Medians Clustering
T-GD: Transferable GAN-generated Images Detection Framework
Continuous Treatment Effects with Surrogate Outcomes
Bayesian clustering for email campaign detection
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Bayesian Quadrature on Riemannian Data Manifolds
Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
Efficient Denoising Diffusion via Probabilistic Masking
Privacy Amplification Through Synthetic Data: Insights from Linear Regression
Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry
Physics and Lie symmetry informed Gaussian processes
DragSolver: A Multi-Scale Transformer for Real-World Automotive Drag Coefficient Estimation
Moreau-Yosida f-divergences
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Cauchy Graph Embedding
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
Global Prosody Style Transfer Without Text Transcriptions
Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection
Nonlinear Feature Diffusion on Hypergraphs
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
Constrained Ensemble Exploration for Unsupervised Skill Discovery
CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses
COMIC: Multi-view Clustering Without Parameter Selection
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Surrogate Likelihoods for Variational Annealed Importance Sampling
RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes
Adaptive Stochastic Alternating Direction Method of Multipliers
Explanation-Based Acceleration of Similarity-Based Learning
Valid Causal Inference with (Some) Invalid Instruments
Everyone's Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval
Safe Grid Search with Optimal Complexity
Interacting Diffusion Processes for Event Sequence Forecasting
Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
xT: Nested Tokenization for Larger Context in Large Images
Structured Prediction with Partial Labelling through the Infimum Loss
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
Variational Bayesian Inference with Stochastic Search
Counting in Small Transformers: The Delicate Interplay between Attention and Feed-Forward Layers
Long-Short Alignment for Effective Long-Context Modeling in LLMs
Visual Attention Emerges from Recurrent Sparse Reconstruction
Reidentify: Context-Aware Identity Generation for Contextual Multi-Agent Reinforcement Learning
Learning Spatial Relations from Images
Learning Invariant Causal Mechanism from Vision-Language Models
Asymmetric Decision-Making in Online Knowledge Distillation: Unifying Consensus and Divergence
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Automatically Interpreting Millions of Features in Large Language Models
Prompting Decision Transformer for Few-Shot Policy Generalization
Learning Safety Constraints for Large Language Models
Meta-Learning by Landmarking Various Learning Algorithms
Prediction-Aware Learning in Multi-Agent Systems
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Can Machines Learn the True Probabilities?
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
Sequential Cooperative Bayesian Inference
Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport
Learning to Speed Up Structured Output Prediction
Self-Improved Retrosynthetic Planning
Exact MAP Inference by Avoiding Fractional Vertices
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
Confidence Score for Source-Free Unsupervised Domain Adaptation