yusuke1997/dblp-id-title huggingface dataset:
Learning Approximate Control Rules of High Utility
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data
Unsupervised Learning for Class Distribution Mismatch
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification
Grokking at the Edge of Linear Separability
Robot Learning From Demonstration
Max-Margin Infinite Hidden Markov Models
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting
KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
Case-Based or Rule-Based: How Do Transformers Do the Math?
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression
Topic Discovery through Data Dependent and Random Projections
Near-Optimal Φ-Regret Learning in Extensive-Form Games
Evolving Attention with Residual Convolutions
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Generative Flows with Matrix Exponential
Private Federated Learning with Autotuned Compression
Induction of Concept Hierarchies from Noisy Data
Scalable Deep Poisson Factor Analysis for Topic Modeling
Event Outlier Detection in Continuous Time
Flow-based Attribution in Graphical Models: A Recursive Shapley Approach
Language Models as Implicit Tree Search
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers
Skew Orthogonal Convolutions
FADAS: Towards Federated Adaptive Asynchronous Optimization
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games
Deep Isometric Learning for Visual Recognition
Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks
Unconstrained Robust Online Convex Optimization
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback
Tractable n-Metrics for Multiple Graphs
Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Graded Multilabel Classification: The Ordinal Case
Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation
Spline Filters For End-to-End Deep Learning
Steerable Transformers for Volumetric Data
High-Dimensional Tensor Regression With Oracle Properties
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
Learning Symbolic Prototypes
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
EgoPrivacy: What Your First-Person Camera Says About You?
True Online TD(lambda)
Nyström Method with Kernel K-means++ Samples as Landmarks
Internal Rewards Mitigate Agent Boundedness
FetchSGD: Communication-Efficient Federated Learning with Sketching
A Whale's Tail - Finding the Right Whale in an Uncertain World
Dynamic Topic Models for Temporal Document Networks
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction
Spherical-Nested Diffusion Model for Panoramic Image Outpainting
Compute or Load KV Cache? Why Not Both?
Bipartite Ranking through Minimization of Univariate Loss
Minimizing Trust Leaks for Robust Sybil Detection
Flexible Modeling of Latent Task Structures in Multitask Learning
Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects
BARNN: A Bayesian Autoregressive and Recurrent Neural Network
Prototype Based Classification from Hierarchy to Fairness
Parallel tempering on optimized paths
Statistical and computational analysis of locality preserving projection
Dynamical Products of Experts for Modeling Financial Time Series
Discriminative Features via Generalized Eigenvectors
Generalized Strategic Classification and the Case of Aligned Incentives
PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Learning to Select Text Databases with Neural Nets
FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation
Core Knowledge Deficits in Multi-Modal Language Models
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Fine-grained Classes and How to Find Them
M³HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Decentralized detection and classification using kernel methods
Gradient-based Explanations for Deep Learning Survival Models
Learning the Kernel Matrix with Semi-Definite Programming
Near-Optimal Representation Learning for Linear Bandits and Linear RL
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
The Importance of Being Lazy: Scaling Limits of Continual Learning
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs
Practical Gauss-Newton Optimisation for Deep Learning
Approximating Correlated Equilibria using Relaxations on the Marginal Polytope
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
MEC: Memory-efficient Convolution for Deep Neural Network
Hyperparameter optimization with approximate gradient
Generalized Doubly Reparameterized Gradient Estimators
Topic-link LDA: joint models of topic and author community
Temporal Difference Flows
Clustering by Left-Stochastic Matrix Factorization
GDPP: Learning Diverse Generations using Determinantal Point Processes
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
From data to functa: Your data point is a function and you can treat it like one
Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering
Bayesian Attention Belief Networks
Finding Influential Training Samples for Gradient Boosted Decision Trees
Improving Token-Based World Models with Parallel Observation Prediction
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Analyzing feature generation for value-function approximation
Competitive Caching with Machine Learned Advice
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
MADE: Masked Autoencoder for Distribution Estimation
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization
Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions
Dictionary Learning for Massive Matrix Factorization
Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Text-To-Concept (and Back) via Cross-Model Alignment
Thompson Sampling Algorithms for Mean-Variance Bandits
SuDA: Support-based Domain Adaptation for Sim2Real Hinge Joint Tracking with Flexible Sensors
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
You Get What You Give: Reciprocally Fair Federated Learning
Bayesian Optimization over Hybrid Spaces
Following the Perturbed Leader for Online Structured Learning
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache
Optimal transport-based conformal prediction
Men Also Do Laundry: Multi-Attribute Bias Amplification
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
Learning Episodes for Optimization
Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method
Fair Generalized Linear Models with a Convex Penalty
Discovering Latent Covariance Structures for Multiple Time Series
Improving Generative Imagination in Object-Centric World Models
Active Embedding Search via Noisy Paired Comparisons
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Scaling Laws for Floating-Point Quantization Training
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Map building without localization by dimensionality reduction techniques
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics
On the price of explainability for some clustering problems
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
M2PDE: Compositional Generative Multiphysics and Multi-component PDE Simulation
RLTHF: Targeted Human Feedback for LLM Alignment
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments
Demystifying Information-Theoretic Clustering
Concise Explanations of Neural Networks using Adversarial Training
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Reward-free World Models for Online Imitation Learning
Stochastic Block BFGS: Squeezing More Curvature out of Data
Bolasso: model consistent Lasso estimation through the bootstrap
Domain Adaptation for Time Series Under Feature and Label Shifts
Modeling Order in Neural Word Embeddings at Scale
Continual Learners are Incremental Model Generalizers
Investigating the Maximum Likelihood Alternative to TD(lambda)
Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
It's Not What Machines Can Learn, It's What We Cannot Teach
Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning
Continual Learning with Guarantees via Weight Interval Constraints
Using Weighted Networks to Represent Classification Knowledge in Noisy Domains
Stochastic Online Conformal Prediction with Semi-Bandit Feedback
Improving Accuracy of Incorrect Domain Theories
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts
Extracting Rare Dependence Patterns via Adaptive Sample Reweighting
How do Transformers Perform In-Context Autoregressive Learning ?
Equivariant Polynomial Functional Networks
Flexible and Efficient Grammar-Constrained Decoding
Compact Hyperplane Hashing with Bilinear Functions
Automated Benchmark Generation for Repository-Level Coding Tasks
Uncertainty sampling and transductive experimental design for active dual supervision
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
Explanation-Based Learning and Reinforcement Learning: A Unified View
Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations
Estimating replicability of classifier learning experiments
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Nonparametric Mixture of Gaussian Processes with Constraints
Structured Generative Models of Natural Source Code
Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012
MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation
Predicting good probabilities with supervised learning
Predicting Consumer Behavior in Commerce Search
Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making
Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning
Scaling Up Reinforcement Learning for Robot Control
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Neural Diffusion Models
Fast Max-Margin Matrix Factorization with Data Augmentation
Truthful Univariate Estimators
Chi-square Generative Adversarial Network
Rethinking Time Encoding via Learnable Transformation Functions
Stochastic Localization via Iterative Posterior Sampling
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
The Four Color Theorem for Cell Instance Segmentation
Transductive Learning with Multi-class Volume Approximation
Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition
Self-Consistency Preference Optimization
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Gambling-Based Confidence Sequences for Bounded Random Vectors
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Generative Cooperative Networks for Natural Language Generation
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization
Parsimonious Learning-Augmented Caching
A Generative Model for Molecular Distance Geometry
Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning
Route kernels for trees
AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models
Successor Features for Efficient Multi-Subject Controlled Text Generation
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data
Learning a Mixture of Two Multinomial Logits
Proceedings of the Eighth International Workshop (ML91), Northwestern University, Evanston, Illinois, USA
Is Pessimism Provably Efficient for Offline RL?
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
Neurosymbolic World Models for Sequential Decision Making
Federated Online and Bandit Convex Optimization
Learning to Fly by Controlling Dynamic Instabilities
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Let Go of Your Labels with Unsupervised Transfer
Reformation from State Space to Reduction Space
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
MP-Nav: Enhancing Data Poisoning Attacks against Multimodal Learning
Statistical Indistinguishability of Learning Algorithms
B-score: Detecting biases in large language models using response history
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
Employing EM and Pool-Based Active Learning for Text Classification
A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers
Beyond Filters: Compact Feature Map for Portable Deep Model
Computational Asymmetries in Robust Classification
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
Quantum algorithms for reinforcement learning with a generative model
Differentially Private Maximal Information Coefficients
Gaussian Approximation of Collective Graphical Models
Explanation-Based Learning with Incomplete Theories: A Three-step Approach
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
Leveraging Per-Instance Privacy for Machine Unlearning
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
Stein Variational Gradient Descent Without Gradient
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
A Nearly-Linear Time Framework for Graph-Structured Sparsity
Fair k-Centers via Maximum Matching
Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
Learning to Model the World With Language
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Bidirectional Learning for Time-series Models with Hidden Units
Active Policy Improvement from Multiple Black-box Oracles
Feature-Space Transformation Improves Supervised Segmentation Across Scanners
In-Context Adaptation to Concept Drift for Learned Database Operations
Exploration and Anti-Exploration with Distributional Random Network Distillation
Offline Contextual Bandits with Overparameterized Models
CompILE: Compositional Imitation Learning and Execution
Power of Ordered Hypothesis Testing
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
What Can Be Learnt With Wide Convolutional Neural Networks?
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Discovering Conditionally Salient Features with Statistical Guarantees
E2GAN: Efficient Training of Efficient GANs for Image-to-Image Translation
Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks
Unsupervised estimation for noisy-channel models
Transductive regression piloted by inter-manifold relations
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
Weight Uncertainty in Neural Network
Multiresolution Matrix Factorization
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis
Open Ad Hoc Teamwork with Cooperative Game Theory
Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration
Accelerated Message Passing for Entropy-Regularized MAP Inference
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection
A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms
BASE Layers: Simplifying Training of Large, Sparse Models
BEATs: Audio Pre-Training with Acoustic Tokenizers
Optimal Complexity in Decentralized Training
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs
Neural Variational Inference for Text Processing
Representational Issues in Meta-Learning
Under-Counted Tensor Completion with Neural Incorporation of Attributes
An embarrassingly simple approach to zero-shot learning
On the Finite-Time Performance of the Knowledge Gradient Algorithm
Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Nyström Kernel Mean Embeddings
Multi-Instance Kernels
A New Formalism, Method and Open Issues for Zero-Shot Coordination
Congested Bandits: Optimal Routing via Short-term Resets
Learning Control-Oriented Dynamical Structure from Data
SimpleNPKL: simple non-parametric kernel learning
Finite-Time Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents
Why do Nearest Neighbor Language Models Work?
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization
Smoothness Adaptive Hypothesis Transfer Learning
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions
Learnable Spatial-Temporal Positional Encoding for Link Prediction
Eluder-based Regret for Stochastic Contextual MDPs
Variational Inference for Monte Carlo Objectives
Spatial Analogy and Subsumption
HumanTOMATO: Text-aligned Whole-body Motion Generation
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
Provably Adversarially Robust Nearest Prototype Classifiers
Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
Object Permanence Emerges in a Random Walk along Memory
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
Riemannian Pursuit for Big Matrix Recovery
Consistent Covariance Selection From Data With Missing Values
Constructive Induction Using a Non-Greedy Strategy for Feature Selection
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Anytime Online-to-Batch, Optimism and Acceleration
On the Last-Iterate Convergence of Shuffling Gradient Methods
De-mark: Watermark Removal in Large Language Models
A Geometric Explanation of the Likelihood OOD Detection Paradox
Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
The Devil Is in the Details: Tackling Unimodal Spurious Correlations for Generalizable Multimodal Reward Models
Federated Conformal Predictors for Distributed Uncertainty Quantification
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
Online Convex Optimization in the Random Order Model
BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
Coactive Learning for Interactive Machine Translation
Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Featured Graph Coarsening with Similarity Guarantees
Masked Trajectory Models for Prediction, Representation, and Control
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Integrating Experimentation and Guidance in Relational Reinforcement Learning
EGPlace: An Efficient Macro Placement Method via Evolutionary Search with Greedy Repositioning Guided Mutation
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning
Reliable Fidelity and Diversity Metrics for Generative Models