:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ananthakrishnan, Nivasini, Bates, Stephen, Jordan, Michael I., Haghtalab, Nika
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2309.01837
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
par: Ananthakrishnan, Nivasini, et autres
Publié: (2024)

On-Demand Sampling: Learning Optimally from Multiple Distributions
par: Haghtalab, Nika, et autres
Publié: (2022)

Learning Local Stackelberg Equilibria from Repeated Interactions with a Learning Agent
par: Ananthakrishnan, Nivasini, et autres
Publié: (2025)

On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
par: Jiang, Haozhe, et autres
Publié: (2025)

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
par: Jagadeesan, Meena, et autres
Publié: (2023)

Signaling in Data Markets via Free Samples
par: Ananthakrishnan, Nivasini, et autres
Publié: (2026)

Learning With Multi-Group Guarantees For Clusterable Subpopulations
par: Dai, Jessica, et autres
Publié: (2024)

Diffusion Language Models are Provably Optimal Parallel Samplers
par: Jiang, Haozhe, et autres
Publié: (2025)

From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
par: Zhao, Eric, et autres
Publié: (2025)

Sample-Adaptivity Tradeoff in On-Demand Sampling
par: Haghtalab, Nika, et autres
Publié: (2025)

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
par: Gölz, Paul, et autres
Publié: (2025)

Algorithmic Content Selection and the Impact of User Disengagement
par: Calvano, Emilio, et autres
Publié: (2024)

Learning in Stackelberg Games with Non-myopic Agents
par: Haghtalab, Nika, et autres
Publié: (2022)

Truthfulness of Calibration Measures
par: Haghtalab, Nika, et autres
Publié: (2024)

Power and Limitations of Aggregation in Compound AI Systems
par: Ananthakrishnan, Nivasini, et autres
Publié: (2026)

Panprediction: Optimal Predictions for Any Downstream Task and Loss
par: Balakrishnan, Sivaraman, et autres
Publié: (2025)

Smooth Nash Equilibria: Algorithms and Complexity
par: Daskalakis, Constantinos, et autres
Publié: (2023)

Can Probabilistic Feedback Drive User Impacts in Online Platforms?
par: Dai, Jessica, et autres
Publié: (2024)

Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
par: Aden-Ali, Ishaq, et autres
Publié: (2026)

Privacy Can Arise Endogenously in an Economic System with Learning Agents
par: Ananthakrishnan, Nivasini, et autres
Publié: (2024)

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
par: Nguyen, Drew T., et autres
Publié: (2024)

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
par: Halawi, Danny, et autres
Publié: (2024)

Statistical Collusion by Collectives on Learning Platforms
par: Gauthier, Etienne, et autres
Publié: (2025)

Incentive-Theoretic Bayesian Inference for Collaborative Science
par: Bates, Stephen, et autres
Publié: (2023)

Verde: Verification via Refereed Delegation for Machine Learning Programs
par: Arun, Arasu, et autres
Publié: (2025)

Private Prediction Sets
par: Angelopoulos, Anastasios N., et autres
Publié: (2021)

Principal-Agent Hypothesis Testing
par: Bates, Stephen, et autres
Publié: (2022)

Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
par: Fannjiang, Clara, et autres
Publié: (2022)

A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning
par: Wadia, Neha S., et autres
Publié: (2023)

Causal Inference with Categorical Unobserved Confounder via Mixture Learning
par: Saha, Aytijhya, et autres
Publié: (2026)

The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning
par: Creaner, Oisín, et autres
Publié: (2025)

Policy Teaching via Data Poisoning in Learning from Human Preferences
par: Nika, Andi, et autres
Publié: (2025)

Data Collection and Labeling Techniques for Machine Learning
par: Huang, Qianyu, et autres
Publié: (2024)

Accelerating Optimization and Machine Learning through Decentralization
par: Chen, Ziqin, et autres
Publié: (2026)

Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
par: Feldman, Shai, et autres
Publié: (2025)

Deep Ensembles for Epistemic Uncertainty: A Frequentist Perspective
par: Jain, Anchit, et autres
Publié: (2025)

Leakage-Robust Bayesian Persuasion
par: Haghtalab, Nika, et autres
Publié: (2024)

Platforms for Efficient and Incentive-Aware Collaboration
par: Haghtalab, Nika, et autres
Publié: (2024)

A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
par: Saif, Sarwar, et autres
Publié: (2025)

Reduced-Rank Multi-objective Policy Learning and Optimization
par: Nwankwo, Ezinne, et autres
Publié: (2024)