yusuke1997/dblp-id-title huggingface dataset:
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Multi-label Classification via Feature-aware Implicit Label Space Encoding
How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees
Learning k-Reversible Context-Free Grammars from Positive Structural Examples
Measuring In-Context Computation Complexity via Hidden State Prediction
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, 2002
No Double Descent in Principal Component Regression: A High-Dimensional Analysis
The Power of Random Features and the Limits of Distribution-Free Gradient Descent
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Non-stationary Diffusion For Probabilistic Time Series Forecasting
Switched Flow Matching: Eliminating Singularities via Switching ODEs
CHEMREASONER: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback
Safe-EF: Error Feedback for Non-smooth Constrained Optimization
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
On the Learning of Non-Autoregressive Transformers
An Region-Based Learning Approach to Discovering Temporal Structures in Data
How to Escape Sharp Minima with Random Perturbations
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
Adversarial Attacks on Gaussian Process Bandits
From Logits to Hierarchies: Hierarchical Clustering made Simple
On the role of tracking in stationary environments
Learning Fast-Mixing Models for Structured Prediction
Learning to Clear the Market
Nonparametric Iterative Machine Teaching
Learning to solve game trees
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
Divide and Conquer: Learning Label Distribution with Subtasks
Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
Loss Balancing for Fair Supervised Learning
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Learning strategies for story comprehension: a reinforcement learning approach
Rolling Diffusion Models
Submodular Dictionary Selection for Sparse Representation
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
Detecting Novel Classes with Applications to Fault Diagnosis
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
Privacy-preserving reinforcement learning
Revisiting Semi-Supervised Learning with Graph Embeddings
Adaptive Multiple-Arm Identification
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
AlphaPO: Reward Shape Matters for LLM Alignment
On the Theoretical Properties of the Network Jackknife
Improving the Efficiency of Knowledge Base Refinement
Membership Inference Attacks on Diffusion Models via Quantile Regression
A Closer Look at Transformers for Time Series Forecasting: Understanding Why They Work and Where They Struggle
Classification with Low Rank and Missing Data
CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
Online Discovery of Feature Dependencies
Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
Maximum Entropy Markov Models for Information Extraction and Segmentation
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Sparse probabilistic classifiers
SpreadsheetCoder: Formula Prediction from Semi-structured Context
What can I do here? A Theory of Affordances in Reinforcement Learning
Speech Self-Supervised Learning Using Diffusion Model Synthetic Data
SADA: Stability-guided Adaptive Diffusion Acceleration
Efficient Core-set Selection for Deep Learning Through Squared Loss Minimization
Learning to Price Against a Moving Target
Effect of Incomplete Meta-dataset on Average Ranking Method
On the Noisy Gradient Descent that Generalizes as SGD
Energy-Based Processes for Exchangeable Data
Sample-based approximate regularization
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
DORO: Distributional and Outlier Robust Optimization
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet)
Bidirectional Helmholtz Machines
Variational Data Assimilation with a Learned Inverse Observation Operator
Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit
Learning to compete, compromise, and cooperate in repeated general-sum games
Anomaly Detection With Multiple-Hypotheses Predictions
Multi-Task Differential Privacy Under Distribution Skew
Knowledge Retention in Continual Model-Based Reinforcement Learning
Machine Learning, Proceedings of the Thirteenth International Conference (ICML '96), Bari, Italy, July 3-6, 1996
Finding trainable sparse networks through Neural Tangent Transfer
Label Distribution Propagation-based Label Completion for Crowdsourcing
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning
Learning Representations and Generative Models for 3D Point Clouds
Network Inference and Influence Maximization from Samples
Convergence of Least Squares Temporal Difference Methods Under General Conditions
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Taming Diffusion for Dataset Distillation with High Representativeness
LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
Using output codes to boost multiclass learning problems
Graph U-Nets
Active learning via transductive experimental design
Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
Non-clairvoyant Scheduling with Partial Predictions
On Learning Parallel Pancakes with Mostly Uniform Weights
Estimation of Simultaneously Sparse and Low Rank Matrices
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Hypernetwork approach to generating point clouds
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Controversy in mechanistic modelling with Gaussian processes
Local Density Estimation in High Dimensions
Structural Maxent Models
Function-to-Style Guidance of LLMs for Code Translation
Can Implicit Bias Imply Adversarial Robustness?
How to Evaluate and Mitigate IP Infringement in Visual Generative AI?
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Generalization Bounds using Data-Dependent Fractal Dimensions
Adaptive Group Personalization for Federated Mutual Transfer Learning
LEVER: Learning to Verify Language-to-Code Generation with Execution
Latent Variable Causal Discovery under Selection Bias
Boosting and Other Machine Learning Algorithms
The Fundamental Limits of Least-Privilege Learning
A Fully First-Order Method for Stochastic Bilevel Optimization
Rethinking Weak Supervision in Helping Contrastive Learning
Leveraged Weighted Loss for Partial Label Learning
Predicting mutational effects on protein binding from folding energy
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
On the Adversarial Robustness of Causal Algorithmic Recourse
Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition
On the Emergence of Position Bias in Transformers
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Be Like Water: Adaptive Floating Point for Machine Learning
Incorporating Arbitrary Matrix Group Equivariance into KANs
2nd International Workshop on Mining Urban Data (Preface)
An Object-Oriented Representation for Search algorithms
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
Hierarchical Verification for Adversarial Robustness
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
When Will Gradient Regularization Be Harmful?
Invariant Risk Minimization Games
Identifiability Conditions for Domain Adaptation
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
When is Realizability Sufficient for Off-Policy Reinforcement Learning?
Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
Beam Tree Recursive Cells
Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator
Coresets for Nonparametric Estimation - the Case of DP-Means
Scaling Down Deep Learning with MNIST-1D
spred: Solving L1 Penalty with SGD
Black-box Optimization with a Politician
Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models
Accelerating Parallel Sampling of Diffusion Models
NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks
From Complex to Atomic: Enhancing Augmented Generation via Knowledge-Aware Dual Rewriting and Reasoning
An Efficient Boosting Algorithm for Combining Preferences
Physics-Informed Generative Modeling of Wireless Channels
Q-value Regularized Transformer for Offline Reinforcement Learning
Importance weighted active learning
Self-Attention Graph Pooling
Do ImageNet Classifiers Generalize to ImageNet?
Smooth Non-stationary Bandits
Actor-Critics Can Achieve Optimal Sample Efficiency
Optimistic Multi-Agent Policy Gradient
Self-Play Q-Learners Can Provably Collude in the Iterated Prisoner's Dilemma
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Inverse Optimization via Learning Feasible Regions
Marginalized Kernels Between Labeled Graphs
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Trapdoor Normalization with Irreversible Ownership Verification
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Winograd Algorithm for AdderNet
Lightweight Online Adaption for Time Series Foundation Model Forecasts
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games
Induction of One-Level Decision Trees
Sparse Uncorrelated Linear Discriminant Analysis
CursorCore: Assist Programming through Aligning Anything
On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
Refined Regret for Adversarial MDPs with Linear Function Approximation
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification
Online Learning in Markov Decision Processes with Changing Cost Sequences
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Learning In-context n-grams with Transformers: Sub-n-grams Are Near-Stationary Points
Problem Dependent View on Structured Thresholding Bandit Problems
Local Identifying Causal Relations in the Presence of Latent Variables
Robust Inference via Generative Classifiers for Handling Noisy Labels
Option Discovery in the Absence of Rewards with Manifold Analysis
Conformity Score Averaging for Classification
Selecting Data Augmentation for Simulating Interventions
A Better k-means++ Algorithm via Local Search
Least Squares Estimation using Sketched Data with Heteroskedastic Errors
(How) Do Language Models Track State?
Bias in Natural Actor-Critic Algorithms
Learning from Data with Bounded Inconsistency
Minimizing the Maximal Loss: How and Why
Naive Bayes models for probability estimation
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Learning State-Based Node Representations from a Class Hierarchy for Fine-Grained Open-Set Detection
Replicable Learning of Large-Margin Halfspaces
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses
Decentralized Online Convex Optimization in Networked Systems
Do GANs always have Nash equilibria?
Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning
Detecting Corrupted Labels Without Training a Model to Predict
Imitation by Predicting Observations
World Model as a Graph: Learning Latent Landmarks for Planning
SMG: A Shuffling Gradient-Based Method with Momentum
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
DataDecide: How to Predict Best Pretraining Data with Small Experiments
Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods
Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
Unlocking the Power of SAM 2 for Few-Shot Segmentation
CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
Model Uncertainty Quantification by Conformal Prediction in Continual Learning
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Higher order learning with graphs
AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning
Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory
Controlling Search for the Consequences of New Information During Knowledge Integration
DRew: Dynamically Rewired Message Passing with Delay
Performative Reinforcement Learning
Calibrating Video Watch-time Predictions with Credible Prototype Alignment
Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution
Conditional Independence in Testing Bayesian Networks
Multiclass boosting with repartitioning
Debating with More Persuasive LLMs Leads to More Truthful Answers
Adaptive Observation Cost Control for Variational Quantum Eigensolvers
Sparse Subspace Clustering with Entropy-Norm
Stochastic Subspace Cubic Newton Method
A fast algorithm to simulate nonlinear resistive networks
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
A Simple Unified Framework for High Dimensional Bandit Problems
Dependent Normalized Random Measures
AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning
Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction
DiJiang: Efficient Large Language Models through Compact Kernelization
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
Leveraging Predictive Equivalence in Decision Trees
How Important is the Train-Validation Split in Meta-Learning?
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
Instance Specific Approximations for Submodular Maximization
Starting Small - Learning with Adaptive Sample Sizes
SΩI: Score-based O-INFORMATION Estimation
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
Position: Mission Critical - Satellite Data is a Distinct Modality in Machine Learning
Approximate Modified Policy Iteration
Lazy Estimation of Variable Importance for Large Neural Networks
Stochastic Reweighted Gradient Descent
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
Efficient Learning from Delayed Rewards through Symbiotic Evolution
Closed-form supervised dimensionality reduction with generalized linear models
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
AutoClass: A Bayesian Classification System
A continuation method for semi-supervised SVMs
R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models
A Wasserstein Minimax Framework for Mixed Linear Regression
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
Bayesian Design Principles for Frequentist Sequential Learning
Sparse Video-Gen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
How boosting the margin can also boost classifier complexity
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
Mapping kernels for trees
Sample-specific Masks for Visual Reprogramming-based Prompting
Learning Deep Structured Models
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
Recovering AES Keys with a Deep Cold Boot Attack
Complexity and Cooperation in Q-Learning
Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development
Pareto-frontier Entropy Search with Variational Lower Bound Maximization
Avoiding spurious sharpness minimization broadens applicability of SAM
Log Neural Controlled Differential Equations: The Lie Brackets Make A Difference
The Multilinear Structure of ReLU Networks
Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Active Multi-Task Representation Learning
Understand and Modularize Generator Optimization in ELECTRA-style Pretraining
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Analytical Lyapunov Function Discovery: An RL-based Generative Approach
Vector Quantized Models for Planning
Iterative Embedding with Robust Correction using Feedback of Error Observed
Efficient Intervention Design for Causal Discovery with Latents
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Bridging Theory and Algorithm for Domain Adaptation
Value Iteration in Continuous Actions, States and Time
Improving Reward Model Generalization from Adversarial Process Enhanced Preferences
Ranking interesting subgroups
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
Go Beyond Imagination: Maximizing Episodic Reachability with World Models
Does learning the right latent variables necessarily improve in-context learning?
Accelerated Stochastic Gradient-free and Projection-free Methods
Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Solving Inverse Problems with a Flow-based Noise Model
Recurrent Convolutional Neural Networks for Scene Labeling
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA
Special Properties of Gradient Descent with Large Learning Rates
Anti-Exploration by Random Network Distillation
Bayesian Program Learning by Decompiling Amortized Knowledge
GFlowOut: Dropout with Generative Flow Networks
Learning Bellman Complete Representations for Offline Policy Evaluation
Tree-Structured Infinite Sparse Factor Model
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation
Algorithms with Calibrated Machine Learning Predictions
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
PINs: Progressive Implicit Networks for Multi-Scale Neural Representations
Learning with Idealized Kernels
Parrot: Multilingual Visual Instruction Tuning
Deep equilibrium networks are sensitive to initialization statistics
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Approximate Stein Classes for Truncated Density Estimation
On Variational Learning of Controllable Representations for Text without Supervision
A pitfall and solution in multi-class feature selection for text classification
Temporal Logic Point Processes
Two New Frameworks for Learning
Estimation from Indirect Supervision with Linear Moments
Von Mises-Fisher Clustering Models
Bayesian Inference for Correlated Human Experts and Classifiers
Importance Weighted Kernel Bayes' Rule
Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach
TAN Without a Burn: Scaling Laws of DP-SGD
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Greedy Attribute Selection
Equivariant Quantum Graph Circuits
Shifting Regret, Mirror Descent, and Matrices
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Domain Generalization via Invariant Feature Representation
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Variance-Reduced and Projection-Free Stochastic Optimization
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction