yusuke1997/dblp-id-title huggingface dataset:
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Rehearsal Learning for Avoiding Undesired Future
Post-Training Quantization for Vision Transformer
The Cascade-Correlation Learning Architecture
Thompson Sampling and Approximate Inference
Speech Recognition Using Demi-Syllable Neural Prediction Model
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Deep Signature Transforms
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
Personalized Federated Learning with Moreau Envelopes
Self-Supervised Reinforcement Learning that Transfers using Random Features
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
Learning from Uncertain Data: From Possible Worlds to Possible Models
The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons
One-Step Diffusion Distillation via Deep Equilibrium Models
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition
An eye for an ear: zero-shot audio description leveraging an image captioner with audio-visual token distribution matching
State Space Models on Temporal Graphs: A First-Principles Study
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks
Sparse Spiking Gradient Descent
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
Online Convex Optimization Over Erdos-Renyi Random Networks
On Linear Stability of SGD and Input-Smoothness of Neural Networks
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior
Universality laws for Gaussian mixtures in generalized linear models
Pairwise GUI Dataset Construction Between Android Phones and Tablets
On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games
MoVie: Visual Model-Based Policy Adaptation for View Generalization
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
Discrete Modeling via Boundary Conditional Diffusion Processes
A Probabilistic Model of Auditory Space Representation in the Barn Owl
ID 3 : Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Anchor Data Augmentation
Mutually Regressive Point Processes
Explicit Planning for Efficient Exploration in Reinforcement Learning
An online passive-aggressive algorithm for difference-of-squares classification
Achieving budget-optimality with adaptive schemes in crowdsourcing
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
Predicting Label Distribution from Ternary Labels
Learning in Zero-Sum Team Markov Games Using Factored Value Functions
Solving inverse problem of Markov chain with partial observations
Linear Contextual Bandits with Knapsacks
Few-Shot Continual Active Learning by a Robot
Optimal Underdamped Langevin MCMC Method
Bayesian Models of Inductive Generalization
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
In-Network PCA and Anomaly Detection
Load and Attentional Bayes
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
To The Point: Correspondence-driven monocular 3D category reconstruction
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Streaming Belief Propagation for Community Detection
On the Effect of Analog Noise in Discrete-Time Analog Computations
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Scalable methods for 8-bit training of neural networks
Activity Grammars for Temporal Action Segmentation
Controlling Neural Level Sets
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Norm-guided latent space exploration for text-to-image generation
Cross-sectional Learning of Extremal Dependence among Financial Assets
Optimal Parallelization of Boosting
Direct Classification with Indirect Data
GriddlyJS: A Web IDE for Reinforcement Learning
Landmark Ordinal Embedding
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
Learning Mixtures of Ranking Models
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
Theoretical and Practical Perspectives on what Influence Functions Do
Attention boosted Individualized Regression
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels
Federated Multi-Task Learning under a Mixture of Distributions
Measures of Information Reflect Memorization Patterns
Concrete Dropout
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Hierarchical Transformation of Space in the Visual System
Explorations with the Dynamic Wave Model
Sample Complexity of Learning Mahalanobis Distance Metrics
An Analog Neural Network Inspired by Fractal Block Coding
Performance of Connectionist Learning Algorithms on 2-D SIMD Processor Arrays
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Discriminant Adaptive Nearest Neighbor Classification and Regression
Holistic Evaluation of Text-to-Image Models
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
LICO: Explainable Models with Language-Image COnsistency
Statistical Tests for Optimization Efficiency
Statistical Cost Sharing
Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Learning long-range spatial dependencies with horizontal gated recurrent units
A Diffusion-Model of Joint Interactive Navigation
Expressive Gaussian Human Avatars from Monocular RGB Video
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ
Learning Better Representations From Less Data For Propositional Satisfiability
Breaking the Communication-Privacy-Accuracy Tradeoff with f-Differential Privacy
Counterfactual Invariance to Spurious Correlations in Text Classification
Lexical and Hierarchical Topic Regression
Meta-Reinforcement Learning with Self-Modifying Networks
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
Parameterized Approximation Schemes for Fair-Range Clustering
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
On Learning Latent Models with Multi-Instance Weak Supervision
Towards Automated Circuit Discovery for Mechanistic Interpretability
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
Phase transition in the family of p-resistances
Monte Carlo Matrix Inversion and Reinforcement Learning
Non-Euclidean Mixture Model for Social Network Embedding
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Spectrally-normalized margin bounds for neural networks
MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials
Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
Improved Algorithms for Contextual Dynamic Pricing
Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers
A Connectionist Model for Constructive Modal Reasoning
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Causal Imitability Under Context-Specific Independence Relations
A Practitioner's Guide to Real-World Continual Multimodal Pretraining
Conformal Prediction for Time Series with Modern Hopfield Networks
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
On the Algorithmic Stability of Adversarial Training
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Density Estimation under Independent Similarly Distributed Sampling Assumptions
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits
Statistical Model Aggregation via Parameter Matching
A Minimalist Approach to Offline Reinforcement Learning
Distributionally Robust Linear Quadratic Control
Common Ground in Cooperative Communication
Estimating Propensity for Causality-based Recommendation without Exposure Data
Batched Energy-Entropy acquisition for Bayesian Optimization
Data Augmentation Can Improve Robustness
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Fast and Accurate Stochastic Gradient Estimation
Optimal Sketching for Trace Estimation
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Unsupervised Motion Representation Learning with Capsule Autoencoders
Neural Priming for Sample-Efficient Adaptation
CoinDICE: Off-Policy Confidence Interval Estimation
Efficient Bayesian Parameter Estimation in Large Discrete Domains
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits
Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport
Contextual Bandits and Imitation Learning with Preference-Based Active Queries
Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms
Dying Experts: Efficient Algorithms with Optimal Regret Bounds
Adjoint Operator Algorithms for Faster Learning in Dynamical Neural Networks
Robust Testing in High-Dimensional Sparse Models
Isolating Sources of Disentanglement in Variational Autoencoders
Pairwise Clustering and Graphical Models
Adversarially Robust Optimization with Gaussian Processes
Decentralized sketching of low rank matrices
Brain Network Transformer
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
High-dimensional Contextual Bandit Problem without Sparsity
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control
Fully Dynamic Algorithm for Constrained Submodular Optimization
Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT
Energy-Based Contrastive Learning of Visual Representations
Prediction and Semantic Association
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress
A Family of Penalty Functions for Structured Sparsity
Policy-Gradient Methods for Planning
The Bias-Variance Tradeoff and the Randomized GACV
Signal Recovery with Non-Expansive Generative Network Priors
CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models
On sensitivity of meta-learning to support data
Online PCA in Converging Self-consistent Field Equations
Agnostically Learning Single-Index Models using Omnipredictors
Non-stationary Experimental Design under Linear Trends
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
Supervised Learning of Probability Distributions by Neural Networks
Merging Constrained Optimisation with Deterministic Annealing to Solve Combinatorially Hard Problems
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
Deep Anomaly Detection Using Geometric Transformations
Model Based Inference of Synaptic Plasticity Rules
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Online RL in Linearly q π -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
On Learning µ-Perceptron Networks with Binary Weights
Constrained Optimization Applied to the Parameter Setting Problem for Analog Circuits
Comparator-Adaptive Convex Bandits
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
Rubik's Cube: High-Order Channel Interactions with a Hierarchical Receptive Field
Learning Disentangled Representations for Recommendation
Soft Calibration Objectives for Neural Networks
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
Subgraph Federated Learning with Missing Neighbor Generation
The Trojan Detection Challenge
IBA: Towards Irreversible Backdoor Attacks in Federated Learning
Learning-Augmented Algorithms with Explicit Predictors
When are dynamical systems learned from time series data statistically accurate?
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Bilevel learning of the Group Lasso structure
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
Blending Anti-Aliasing into Vision Transformer
The Power of Adaptivity in Identifying Statistical Alternatives
The Open Catalyst Challenge 2021: Competition Report
Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling
Offline Behavior Distillation
Learning in Games: Robustness of Fast Convergence
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada]
Online Control with Adversarial Disturbance for Continuous-time Linear Systems
Intriguing Properties of Contrastive Losses
EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature
Domain Adaptation with Multiple Sources
CALANet: Cheap All-Layer Aggregation for Human Activity Recognition
Efficient Exact Verification of Binarized Neural Networks
Analysis of a greedy active learning strategy
AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud Splatting
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
On Flat versus Hierarchical Classification in Large-Scale Taxonomies
Unsupervised Learning of Human Motion Models
A Unified View of Piecewise Linear Neural Network Verification
Identifiable Contrastive Learning with Automatic Feature Importance Discovery
Multi-resolution Multi-task Gaussian Processes
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
Novel Upper Bounds for the Constrained Most Probable Explanation Task
Generative Modelling of Structurally Constrained Graphs
Write, Execute, Assess: Program Synthesis with a REPL
Reverse TDNN: An Architecture For Trajectory Generation
Multimodal Virtual Point 3D Detection
Nonparametric Teaching for Multiple Learners
"Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss"
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex
Causal discovery with endogenous context variables
Conformalized Credal Set Predictors
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Bayesian inference in spiking neurons
On the Symmetries of Deep Learning Models and their Internal Representations
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization
Characteristic Kernels on Groups and Semigroups
LION: Linear Group RNN for 3D Object Detection in Point Clouds
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
Consistency of one-class SVM and related algorithms
Sharpness, Restart and Acceleration
Fast Neural Kernel Embeddings for General Activations
Distributed Bayesian Posterior Sampling via Moment Sharing
Localized Sliced Inverse Regression
Lovász Principle for Unsupervised Graph Representation Learning
Effective Targeted Attacks for Adversarial Self-Supervised Learning
4D Panoptic Scene Graph Generation
Transition-constant Normalization for Image Enhancement
Communication Efficient Federated Learning for Generalized Linear Bandits
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
ClevrSkills: Compositional Language And Visual Reasoning in Robotics
SSMF: Shifting Seasonal Matrix Factorization
Autofocused oracles for model-based design
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
Fair Canonical Correlation Analysis
Recasting Continual Learning as Sequence Modeling
Discovering plasticity rules that organize and maintain neural circuits
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
One Million Scenes for Autonomous Driving: ONCE Dataset
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution
Mixed Linear Regression with Multiple Components
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
ImageNet-21K Pretraining for the Masses
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Bayesian Action-Graph Games
Location Estimation with a Differential Update Network
Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies
Bandit Samplers for Training Graph Neural Networks
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
Fuzzy Learning Machine
The GAN is dead; long live the GAN! A Modern GAN Baseline
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Provable Benefit of Multitask Representation Learning in Reinforcement Learning
Distributed Multitask Reinforcement Learning with Quadratic Convergence
Metric Learning by Collapsing Classes
Matching a Desired Causal State via Shift Interventions
Convolution with even-sized kernels and symmetric padding
Stochastic Normalization
Boltzmann Exploration Done Right
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning
Generalized Shape Metrics on Neural Representations
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
State-Action Similarity-Based Representations for Off-Policy Evaluation
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
Incorporating Side Information by Adaptive Convolution
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Learning Attractor Landscapes for Learning Motor Primitives
Multi-scale Consistency for Robust 3D Registration via Hierarchical Sinkhorn Tree
Examples are not enough, learn to criticize! Criticism for Interpretability
Replay, Repair and Consolidation
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection
A Siamese Transformer with Hierarchical Refinement for Lane Detection
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Invertibility of Convolutional Generative Networks from Partial Measurements
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
Dynamic Regret of Adversarial Linear Mixture MDPs
Bangs, Clicks, Snaps, Thuds and Whacks: An Architecture for Acoustic Transient Processing
Deep ADMM-Net for Compressive Sensing MRI
Online Robust Regression via SGD on the l1 loss
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
Neural Networks: Tricks of the Trade
Flexible Neural Image Compression via Code Editing
Gradient Starvation: A Learning Proclivity in Neural Networks
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
Manifold Stochastic Dynamics for Bayesian Learning
A universal probabilistic spike count model reveals ongoing modulation of neural variability
Federated-EM with heterogeneity mitigation and variance reduction
Morié Attack (MA): A New Potential Risk of Screen Photos
On Making Stochastic Classifiers Deterministic
Inequity aversion improves cooperation in intertemporal social dilemmas
HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation