{"total":125,"rows":[{"id":"2202.01017","title":"Multi-Task Learning as a Bargaining Game","created":"2022-02-02","cited_by_count":215,"area":null},{"id":"2103.04628","title":"Personalized Federated Learning using Hypernetworks","created":"2021-03-08","cited_by_count":212,"area":null},{"id":"2301.08727","title":"Neural Architecture Search: Insights from 1000 Papers","created":"2023-01-20","cited_by_count":139,"area":null},{"id":"2306.06955","title":"A Brief Review of Hypernetworks in Deep Learning","created":"2023-06-12","cited_by_count":102,"area":null},{"id":"2403.14119","title":"C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion","created":"2024-03-21","cited_by_count":83,"area":"mllm"},{"id":"2402.02030","title":"Panacea: Pareto Alignment via Preference Adaptation for LLMs","created":"2024-02-03","cited_by_count":80,"area":"alignment"},{"id":"2208.07914","title":"PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm","created":"2022-08-16","cited_by_count":58,"area":null},{"id":"2203.15386","title":"Pareto Set Learning for Neural Multi-objective Combinatorial Optimization","created":"2022-03-29","cited_by_count":56,"area":null},{"id":"2402.19078","title":"Smooth Tchebycheff Scalarization for Multi-Objective Optimization","created":"2024-02-29","cited_by_count":54,"area":null},{"id":"2210.08495","title":"Pareto Set Learning for Expensive Multi-Objective Optimization","created":"2022-10-16","cited_by_count":51,"area":null},{"id":"2308.13985","title":"Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective","created":"2023-08-27","cited_by_count":51,"area":null},{"id":"2302.04040","title":"Sample-efficient Multi-objective Molecular Optimization with GFlowNets","created":"2023-02-08","cited_by_count":50,"area":null},{"id":"2103.13392","title":"Scalable Pareto Front Approximation for Deep Multi-Objective Learning","created":"2021-03-24","cited_by_count":45,"area":null},{"id":"2305.20057","title":"Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance","created":"2023-05-31","cited_by_count":36,"area":null},{"id":"2209.05227","title":"DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization","created":"2022-09-12","cited_by_count":35,"area":null},{"id":"2210.09759","title":"Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models","created":"2022-10-18","cited_by_count":29,"area":null},{"id":"2203.14949","title":"Controllable Dynamic Multi-Task Architectures","created":"2022-03-28","cited_by_count":21,"area":null},{"id":"2308.08810","title":"Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts","created":"2023-08-17","cited_by_count":21,"area":null},{"id":"2112.04137","title":"Pareto Domain Adaptation","created":"2021-12-08","cited_by_count":20,"area":null},{"id":"2406.07529","title":"MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation","created":"2024-06-11","cited_by_count":20,"area":null},{"id":"2501.06773","title":"Pareto Set Learning for Multi-Objective Reinforcement Learning","created":"2025-01-12","cited_by_count":20,"area":"deeprl"},{"id":"2212.03905","title":"Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve","created":"2022-12-07","cited_by_count":18,"area":null},{"id":"2501.10945","title":"Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond","created":"2025-01-19","cited_by_count":18,"area":null},{"id":"2102.04523","title":"Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization","created":"2021-02-08","cited_by_count":16,"area":null},{"id":"2108.07926","title":"Collaboration Equilibrium in Federated Learning","created":"2021-08-18","cited_by_count":16,"area":null},{"id":"2212.01130","title":"Improving Pareto Front Learning via Multi-Sample Hypernetworks","created":"2022-12-02","cited_by_count":16,"area":null},{"id":"2211.13723","title":"Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective","created":"2022-11-24","cited_by_count":15,"area":null},{"id":"2206.01934","title":"Stochastic Multiple Target Sampling Gradient Descent","created":"2022-06-04","cited_by_count":14,"area":null},{"id":"2210.12624","title":"Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach","created":"2022-10-23","cited_by_count":14,"area":null},{"id":"2310.15195","title":"Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement","created":"2023-10-22","cited_by_count":13,"area":null},{"id":"2407.08056","title":"Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences","created":"2024-07-10","cited_by_count":13,"area":null},{"id":"2110.08713","title":"Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set","created":"2021-10-17","cited_by_count":12,"area":null},{"id":"2307.12551","title":"Continuation Path Learning for Homotopy Optimization","created":"2023-07-24","cited_by_count":12,"area":null},{"id":"2407.06083","title":"A Survey of Controllable Learning: Methods and Applications in Information Retrieval","created":"2024-07-04","cited_by_count":12,"area":null},{"id":"2412.04233","title":"HyperMARL: Adaptive Hypernetworks for Multi-Agent RL","created":"2024-12-05","cited_by_count":11,"area":null},{"id":"2308.08497","title":"HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation","created":"2023-08-14","cited_by_count":10,"area":null},{"id":"2409.02969","title":"LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch","created":"2024-09-04","cited_by_count":10,"area":null},{"id":"2407.20734","title":"Efficient Pareto Manifold Learning with Low-Rank Structure","created":"2024-07-30","cited_by_count":9,"area":null},{"id":"2410.19321","title":"Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning","created":"2024-10-25","cited_by_count":9,"area":null},{"id":"2412.01773","title":"FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning","created":"2024-12-02","cited_by_count":9,"area":null},{"id":"2211.08253","title":"HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization","created":"2022-11-15","cited_by_count":8,"area":"moe"},{"id":"2406.09353","title":"Enhancing Domain Adaptation through Prompt Gradient Alignment","created":"2024-06-13","cited_by_count":8,"area":"mllm"},{"id":"2406.18924","title":"Learning Pareto Set for Multi-Objective Continuous Robot Control","created":"2024-06-27","cited_by_count":8,"area":null},{"id":"2505.12833","title":"Reasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs","created":"2025-05-19","cited_by_count":8,"area":"lr"},{"id":"2308.11744","title":"Efficient Controllable Multi-Task Architectures","created":"2023-08-22","cited_by_count":7,"area":null},{"id":"2309.17337","title":"Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools","created":"2023-09-29","cited_by_count":7,"area":null},{"id":"2505.22086","title":"iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs","created":"2025-05-28","cited_by_count":7,"area":null},{"id":"2307.14382","title":"When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review","created":"2023-07-25","cited_by_count":6,"area":null},{"id":"2507.04976","title":"Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models","created":"2025-07-07","cited_by_count":6,"area":"mllm"},{"id":"2206.00832","title":"Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates","created":"2022-06-02","cited_by_count":5,"area":null},{"id":"2312.01692","title":"Risk-Controlling Model Selection via Guided Bayesian Optimization","created":"2023-12-04","cited_by_count":5,"area":null},{"id":"2410.08316","title":"COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework","created":"2024-10-10","cited_by_count":5,"area":"alignment"},{"id":"2201.11945","title":"Learning Proximal Operators to Discover Multiple Optima","created":"2022-01-28","cited_by_count":4,"area":null},{"id":"2208.13499","title":"Generalization In Multi-Objective Machine Learning","created":"2022-08-29","cited_by_count":4,"area":null},{"id":"2304.04641","title":"Probably Approximately Correct Federated Learning","created":"2023-04-10","cited_by_count":4,"area":null},{"id":"2312.11391","title":"FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants","created":"2023-12-18","cited_by_count":4,"area":null},{"id":"2404.08414","title":"Evolutionary Preference Sampling for Pareto Set Learning","created":"2024-04-12","cited_by_count":4,"area":null},{"id":"2407.16807","title":"In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning","created":"2024-07-23","cited_by_count":4,"area":"deeprl"},{"id":"2410.11878","title":"Neural Metamorphosis","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2412.17312","title":"Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks","created":"2024-12-23","cited_by_count":4,"area":null},{"id":"2110.03461","title":"Self-Evolutionary Optimization for Pareto Front Learning","created":"2021-10-07","cited_by_count":3,"area":null},{"id":"2110.07301","title":"Multi-task problems are not multi-objective","created":"2021-10-14","cited_by_count":3,"area":null},{"id":"2204.07661","title":"Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech","created":"2022-04-15","cited_by_count":3,"area":null},{"id":"2302.02565","title":"Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search","created":"2023-02-06","cited_by_count":3,"area":null},{"id":"2311.13159","title":"Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow","created":"2023-11-22","cited_by_count":3,"area":null},{"id":"2312.03051","title":"Generating Interpretable Networks using Hypernetworks","created":"2023-12-05","cited_by_count":3,"area":"mechinterp"},{"id":"2402.02190","title":"Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems","created":"2024-02-03","cited_by_count":3,"area":null},{"id":"2402.05955","title":"A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints","created":"2024-02-04","cited_by_count":3,"area":null},{"id":"2403.16162","title":"Multi-Task Learning with Multi-Task Optimization","created":"2024-03-24","cited_by_count":3,"area":null},{"id":"2405.16056","title":"FedSheafHN: Personalized Federated Learning on Graph-structured Data","created":"2024-05-25","cited_by_count":3,"area":null},{"id":"2407.16828","title":"Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems","created":"2024-07-23","cited_by_count":3,"area":null},{"id":"2410.07286","title":"Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning","created":"2024-10-09","cited_by_count":3,"area":null},{"id":"2503.23390","title":"Pareto Continual Learning: Preference-Conditioned Learning and Adaption for Dynamic Stability-Plasticity Trade-off","created":"2025-03-30","cited_by_count":3,"area":null},{"id":"2506.07549","title":"Improving Memory Efficiency for Training KANs via Meta Learning","created":"2025-06-09","cited_by_count":3,"area":null},{"id":"2606.04342","title":"Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty","created":"2026-06-03","cited_by_count":3,"area":null},{"id":"2405.01480","title":"Common pitfalls to avoid while using multiobjective optimization in machine learning","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2405.08604","title":"Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization","created":"2024-05-14","cited_by_count":2,"area":null},{"id":"2408.09976","title":"Preference-Optimized Pareto Set Learning for Blackbox Optimization","created":"2024-08-19","cited_by_count":2,"area":null},{"id":"2502.11981","title":"Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2503.07784","title":"Joint Explainability-Performance Optimization With Surrogate Models for AI-Driven Edge Services","created":"2025-03-10","cited_by_count":2,"area":null},{"id":"2506.22095","title":"Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs","created":"2025-06-27","cited_by_count":2,"area":null},{"id":"2509.05627","title":"Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives","created":"2025-09-06","cited_by_count":2,"area":null},{"id":"2510.19105","title":"MetaCluster: Enabling Deep Compression of Kolmogorov-Arnold Network","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2511.06790","title":"Robust Causal Discovery under Imperfect Structural Constraints","created":"2025-11-10","cited_by_count":2,"area":null},{"id":"2606.03102","title":"Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling","created":"2026-06-02","cited_by_count":2,"area":"lr"},{"id":"2110.15442","title":"Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints","created":"2021-10-28","cited_by_count":1,"area":null},{"id":"2203.01298","title":"Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP","created":"2022-03-02","cited_by_count":1,"area":null},{"id":"2209.14161","title":"Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models","created":"2022-09-28","cited_by_count":1,"area":null},{"id":"2312.13240","title":"Efficient Verification-Based Face Identification","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2402.09486","title":"UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition","created":"2024-02-14","cited_by_count":1,"area":null},{"id":"2402.18213","title":"Multi-objective Differentiable Neural Architecture Search","created":"2024-02-28","cited_by_count":1,"area":null},{"id":"2404.01224","title":"Collaborative Pareto Set Learning in Multiple Multi-Objective Optimization Problems","created":"2024-04-01","cited_by_count":1,"area":null},{"id":"2404.08397","title":"Data-Driven Preference Sampling for Pareto Front Learning","created":"2024-04-12","cited_by_count":1,"area":null},{"id":"2405.11397","title":"Preparing for Black Swans: The Antifragility Imperative for Machine Learning","created":"2024-05-18","cited_by_count":1,"area":null},{"id":"2503.03438","title":"Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning","created":"2025-03-05","cited_by_count":1,"area":null},{"id":"2503.08849","title":"Learning Pareto manifolds in high dimensions: How can regularization help?","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2503.11120","title":"A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2506.06715","title":"A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks","created":"2025-06-07","cited_by_count":1,"area":null},{"id":"2508.13642","title":"Personalized Subgraph Federated Learning with Sheaf Collaboration","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2510.15748","title":"Towards Relaxed Multimodal Inputs for Gait-based Parkinson's Disease Assessment","created":"2025-10-17","cited_by_count":1,"area":null}],"next_cursor":"100"}