2025 Poster "sample complexity" Papers
14 papers found
Breaking Neural Network Scaling Laws with Modularity
Akhilan Boopathy, Sunshine Jiang, William Yue et al.
ICLR 2025posterarXiv:2409.05780
6
citations
Deployment Efficient Reward-Free Exploration with Linear Function Approximation
Zihan Zhang, Yuxin Chen, Jason Lee et al.
NeurIPS 2025poster
Formal Models of Active Learning from Contrastive Examples
Farnam Mansouri, Hans Simon, Adish Singla et al.
NeurIPS 2025posterarXiv:2506.15893
Learning Hierarchical Polynomials of Multiple Nonlinear Features
Hengyu Fu, Zihao Wang, Eshaan Nichani et al.
ICLR 2025posterarXiv:2411.17201
3
citations
Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
Maryam Aliakbarpour, Zhan Shi, Ria Stevens et al.
NeurIPS 2025posterarXiv:2506.01162
Non-Convex Tensor Recovery from Tube-Wise Sensing
Tongle Wu, Ying Sun
NeurIPS 2025poster
On the Convergence of Single-Timescale Actor-Critic
Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi et al.
NeurIPS 2025posterarXiv:2410.08868
1
citations
On the Sample Complexity of Differentially Private Policy Optimization
Yi He, Xingyu Zhou
NeurIPS 2025posterarXiv:2510.21060
Revisiting Agnostic Boosting
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice et al.
NeurIPS 2025posterarXiv:2503.09384
1
citations
Simple and Optimal Sublinear Algorithms for Mean Estimation
Beatrice Bertolotti, Matteo Russo, Chris Schwiegelshohn et al.
NeurIPS 2025posterarXiv:2406.05254
Stabilizing LTI Systems under Partial Observability: Sample Complexity and Fundamental Limits
Ziyi Zhang, Yorie Nakahira, Guannan Qu
NeurIPS 2025poster
1
citations
Streaming Federated Learning with Markovian Data
Khiem HUYNH, Malcolm Egan, Giovanni Neglia et al.
NeurIPS 2025posterarXiv:2503.18807
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
Taejong Joo, Diego Klabjan
NeurIPS 2025posterarXiv:2502.04580
Tight Bounds for Answering Adaptively Chosen Concentrated Queries
Emma Rapoport, Edith Cohen, Uri Stemmer
NeurIPS 2025posterarXiv:2507.13700