yusuke1997/dblp-id-title huggingface dataset:
Accelerated Stochastic Optimization Methods under Quasar-convexity
Are Large Kernels Better Teachers than Transformers for ConvNets?
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Instance-Family Abstraction in Memory-Based Language Learning
Feature Importance Metrics in the Presence of Missing Data
On causal and anticausal learning
Reward Functions for Accelerated Learning
Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Weak-to-Strong Generalization Even in Random Feature Networks, Provably
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Backdoor Attacks in Token Selection of Attention Mechanism
PoF: Post-Training of Feature Extractor for Improving Generalization
USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval
Learning Rate Schedules in the Presence of Distribution Shift
Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling
Focused crawling with scalable ordinal regression solvers
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
Learning Functional Distributions with Private Labels
Finite time bounds for sampling based fitted value iteration
A Boo(n) for Evaluating Architecture Performance
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Triple-Optimistic Learning for Stochastic Contextual Bandits with General Constraints
On the Trajectory Regularity of ODE-based Diffusion Sampling
Generalized Majorization-Minimization
Learning structural SVMs with latent variables
Inverse Constrained Reinforcement Learning
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
Continuous-Time Hierarchical Reinforcement Learning
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Hierarchical Novelty Detection via Fine-Grained Evidence Allocation
Learning from a Learning User for Optimal Recommendations
SGD Learns One-Layer Networks in WGANs
Adversarial Filters of Dataset Biases
Bayesian Multiple Target Localization
Listenable Maps for Audio Classifiers
A Schema for an Integrated Learning System
Transfer learning for collaborative filtering via a rating-matrix generative model
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions
Bayesian approaches to failure prediction for disk drives
Counterfactual Prediction for Outcome-Oriented Treatments
On Variational Bounds of Mutual Information
A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
Fitting New Speakers Based on a Short Untranscribed Sample
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Unitary Branching Programs: Learnability and Lower Bounds
Labor Saving New Distinctions
Compressing Neural Networks with the Hashing Trick
Random Shuffle Transformer for Image Restoration
Convolutional Kernel Networks for Graph-Structured Data
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
Discovering Structure in Multiple Learning Tasks: The TC Algorithm
How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Slicing Mutual Information Generalization Bounds for Neural Networks
Regret Minimization for Partially Observable Deep Reinforcement Learning
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Incremental Constructive Induction: An Instance-Based Approach
Emergence of In-Context Reinforcement Learning from Noise Distillation
Latent Diffusion Energy-Based Model for Interpretable Text Modelling
Gradient Coding: Avoiding Stragglers in Distributed Learning
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
Domain Adaptation with Conditional Transferable Components
Predictive representations for policy gradient in POMDPs
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
The Acquisition of Human Planning Expertise
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Being Bayesian about Categorical Probability
Learning in POMDPs with Monte Carlo Tree Search
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
A Theoretical Analysis of Feature Pooling in Visual Recognition
Revisiting the Nystrom method for improved large-scale machine learning
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers
Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse
Submodular Inference of Diffusion Networks from Multiple Trees
Inferring serial correlation with dynamic backgrounds
Self-Attentive Hawkes Process
Why skewing works: learning difficult Boolean functions with greedy tree learners
Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning
Federated Continual Learning via Prompt-based Dual Knowledge Transfer
Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies
Best Model Identification: A Rested Bandit Formulation
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Joint Learning of Energy-based Models and their Partition Function
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
Uncorrelated multilinear principal component analysis through successive variance maximization
Multi-task Learning with Labeled and Unlabeled Tasks
Graph Adaptive Autoregressive Moving Average Models
Policy Gradient Bayesian Robust Optimization for Imitation Learning
State Entropy Maximization with Random Encoders for Efficient Exploration
Learning Invariant Representations with Local Transformations
Improving Text Classification by Shrinkage in a Hierarchy of Classes
Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
Unpaired Point Cloud Completion via Unbalanced Optimal Transport
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Mapping natural-language problems to formal-language solutions using structured neural representations
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
Effective and Efficient Masked Image Generation Models
Nonparametric Information Theoretic Clustering Algorithm
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Bayesian learning of measurement and structural models
AnalogGenie-Lite: Enhancing Scalability and Precision in Circuit Topology Discovery through Lightweight Graph Modeling
Off-policy Model-based Learning under Unknown Factored Dynamics
Global Optimization with a Power-Transformed Objective and Gaussian Smoothing
Training Recurrent Neural Networks via Forward Propagation Through Time
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Decision Tree with Better Ranking
Linear Transformers Are Secretly Fast Weight Programmers
Learning from History for Byzantine Robust Optimization
Learning Words From Context
StrokeNUWA - Tokenizing Strokes for Vector Graphic Synthesis
Almost Optimal Fully Dynamic k-Center Clustering with Recourse
Towards Theoretical Understandings of Self-Consuming Generative Models
Provable Variable Selection for Streaming Features
Ultra-Resolution Adaptation with Ease
The Bayesian group-Lasso for analyzing contingency tables
Sparse projections onto the simplex
Unifying Task Specification in Reinforcement Learning
Models of Heavy-Tailed Mechanistic Universality
Universal Gradient Methods for Stochastic Convex Optimization
DVI: A Derivative-based Vision Network for INR
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
SafeMap: Robust HD Map Construction from Incomplete Observations
On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples
Not All Memories are Created Equal: Learning to Forget by Expiring
Sassha: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation
More efficiency in multiple kernel learning
AnyEdit: Edit Any Knowledge Encoded in Language Models
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation
Neural Networks Learn Statistics of Increasing Complexity
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Extracting Training Data From Document-Based VQA Models
NOMU: Neural Optimization-based Model Uncertainty
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
Continuously Indexed Domain Adaptation
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Normalizing Flows are Capable Generative Models
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Maximum Total Correlation Reinforcement Learning
Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
VIP: Vision Instructed Pre-training for Robotic Manipulation
A Convex Feature Learning Formulation for Latent Task Structure Discovery
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Selection of Support Vector Kernel Parameters for Improved Generalization
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
Partial Counterfactual Identification from Observational and Experimental Data
am-ELO: A Stable Framework for Arena-based LLM Evaluation
Robustness Implies Generalization via Data-Dependent Generalization Bounds
Learning Prototypical Concept Descriptions
Accelerating Convergence of Score-Based Diffusion Models, Provably
A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation
Hierarchical Imitation Learning with Vector Quantized Models
Adversarially Robust Hypothesis Transfer Learning
Towards flexible perception with visual memory
On-Line Portfolio Selection Using Multiplicative Updates
Machine Learning Force Fields with Data Cost Aware Training
A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Optimizing Watermarks for Large Language Models
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
Weak-to-Strong Jailbreaking on Large Language Models
Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
Growing Adaptive Multi-hyperplane Machines
Approximate message passing for amplitude based optimization
Non-Stationary Approximate Modified Policy Iteration
LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation
Goal-Aware Prediction: Learning to Model What Matters
Position: The Causal Revolution Needs Scientific Pragmatism
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Robust Conformal Outlier Detection under Contaminated Reference Data
Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering
Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
Modeling Language Tokens as Functionals of Semantic Fields
Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization
Peepholing: Choosing Attributes Efficiently for Megainduction
Learning Factorized Weight Matrix for Joint Filtering
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Lifted Disjoint Paths with Application in Multiple Object Tracking
Variational Heteroscedastic Gaussian Process Regression
Federated Learning for Feature Generalization with Convex Constraints
Decoupling Gradient-Like Learning Rules from Representations
Concept-Based Unsupervised Domain Adaptation
A Convergence Theory for Deep Learning via Over-Parameterization
Gap Filling in the Plant Kingdom - Trait Prediction Using Hierarchical Probabilistic Matrix Factorization
Training Graph Neural Networks with 1000 Layers
Collective Stability in Structured Prediction: Generalization from One Example
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
Robust Guarantees of Stochastic Greedy Algorithms
Retrosynthetic Planning with Dual Value Networks
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Peripheral Memory for LLMs: Integration of Sequential Memory Banks with Adaptive Querying
Enhancing Foundation Models with Federated Domain Knowledge Infusion
Imitation Learning from Imperfect Demonstration
Qualitative reinforcement learning
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model
Pseudo-convergent Q-Learning by Competitive Pricebots
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
Supervised Aggregation of Classifiers using Artificial Prediction Markets
Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps
Algorithms for the Communication of Samples
A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree
Segmenting Hybrid Trajectories using Latent ODEs
C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
Enhancing Spectral GNNs: From Topology and Perturbation Perspectives
Canopy Fast Sampling with Cover Trees
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
Solving Partial Assignment Problems using Random Clique Complexes
Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
Learning Adversarial MDPs with Stochastic Hard Constraints
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
Graph Neural Tangent Kernel: Convergence on Large Graphs
Differentially Private Representation Learning via Image Captioning
Residual Unfairness in Fair Machine Learning from Prejudiced Data
LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
Polynomial-based Self-Attention for Table Representation Learning
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Rich Feature Construction for the Optimization-Generalization Dilemma
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Adapting Precomputed Features for Efficient Graph Condensation
Inferring Latent Structure From Mixed Real and Categorical Relational Data
Access to Unlabeled Data can Speed up Prediction Time
Gaussian quadrature for matrix inverse forms with applications
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Learning One Convolutional Layer with Overlapping Patches
Integrating constraints and metric learning in semi-supervised clustering
Open-ended learning in symmetric zero-sum games
Generalized No Free Lunch Theorem for Adversarial Robustness
Learning Mixtures of Linear Classifiers
DeltaGrad: Rapid retraining of machine learning models
Proximal regularization for online and batch learning
Position: A Call for Embodied AI
Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009
Multi-class image segmentation using conditional random fields and global classification
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Approximate Dynamic Programming for Storage Problems
Learning Latent Variable Gaussian Graphical Models
Implicit degree bias in the link prediction task
Clustering Items through Bandit Feedback: Finding the Right Feature out of Many
Nondeterminism and Instability in Neural Network Optimization
GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention
Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015
Nonparametric Bayesian Deep Networks with Local Competition
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays
An Exploration Into Incremental Learning: the INFLUENCE System
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
From Perception to Programs: Regularize, Overparameterize, and Amortize
On generalization bounds, projection profile, and margin distribution
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization
Improving Generalization in Meta-learning via Task Augmentation
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
Future-conditioned Unsupervised Pretraining for Decision Transformer
Representation Matters: Offline Pretraining for Sequential Decision Making
Distinguishing the Knowable from the Unknowable with Language Models
A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
Accurate Identification of Communication Between Multiple Interacting Neural Populations
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
Weakly Supervised Regression with Interval Targets
Learning Efficiently with Approximate Inference via Dual Losses
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration
One Size Fits All: Can We Train One Denoiser for All Noise Levels?
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
In Search for a Generalizable Method for Source Free Domain Adaptation
Tractable Average-Case Analysis of Naive Bayesian Classifiers
Machine Learning in Engineering Automation
On Online Experimentation without Device Identifiers
Learning and Enforcement: Stabilizing Environments to Facilitate Activity
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
Error limiting reductions between classification tasks
Direct Modeling of Complex Invariances for Visual Object Features
ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding
On the Provable Separation of Scales in Maximal Update Parameterization
Restoring balance: principled under/oversampling of data for optimal classification
LangCell: Language-Cell Pre-training for Cell Identity Understanding
Near Optimal Best Arm Identification for Clustered Bandits
MIB: A Mechanistic Interpretability Benchmark
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
Classifying Treatment Responders Under Causal Effect Monotonicity
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation
Understanding Synthetic Context Extension via Retrieval Heads
A General Framework for Sequential Decision-Making under Adaptivity Constraints
Convex Total Least Squares
Learning useful representations for shifting tasks and distributions
Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning
Bounded Planning in Passive POMDPs
UPSCALE: Unconstrained Channel Pruning
Optimal Kernel Choice for Score Function-based Causal Discovery
Learning Registered Point Processes from Idiosyncratic Observations
Understanding Stochastic Natural Gradient Variational Inference
Learning to Encode Position for Transformer with Continuous Dynamical Model
Getting the Most from Flawed Theories
An empirical comparison of supervised learning algorithms
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
Path Integral Policy Improvement with Covariance Matrix Adaptation
Retrieval-Augmented Multimodal Language Modeling
Alpha-Beta Divergences Discover Micro and Macro Structures in Data
Sparse Support Vector Infinite Push
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization
Gradient Aligned Regression via Pairwise Losses
ID5: An Incremental ID3
OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
Matrix Completion with Model-free Weighting