Papers
Spectral Recovery of a Planted Triangle-Dense Subgraph
Sam van der Poel, Cheng Mao, Benjamin McKenna
The Monotonicity of the Franz–Parisi Potential Is Equivalent to Low-Degree MMSE Lower Bounds: Extended Abstract
Konstantinos Tsirkas, Leda Wang, Ilias Zadik
Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear q^π-Realizability and Concentrability
Volodymyr Tkachuk, Csaba Szepesvári, Xiaoqi Tan
When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks
Berk Tinaz, Changzhi Xie, Mahdi Soltanolkotabi
On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Mohammad Tinati, Stephen Tu
CONVERGENCE RATES FOR DISTRIBUTION MATCHING WITH SLICED OPTIMAL TRANSPORT
Gauthier Thurin, Claire Boyer, Kimia Nadjahi
Data Augmentation: A Fourier Analysis Perspective
Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka
Truly Adapting to Adversarial Constraints in Constrained MABs
Francesco Emanuele Stradi, Kalana Kalupahana, Matteo Castiglioni et al.
Privately Estimating Black-Box Statistics
Günter Steinke, Thomas Steinke
Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment
Ved Sriraman, Adam Block
Efficient Learning and Symmetry Discovery under Exact Invariances
Ashkan Soleymani, Behrooz Tahmasebi, Patrick Jaillet et al.
Finite Sample Bounds for Learning with Score Matching
Devin Smedira, Abhijith Jayakumar, Sidhant Misra et al.
Testing for a Hidden Geometry in Random Graphs
Amit Silber, Mor Oren-Loberman, Wasim Huleihel
Optimal Sample Complexity Lower Bounds on Conditional Independence Testing
Jan Seyfried, Neelkanth Mishra, Sayantan Sen et al.
The Hidden Cost of Approximation in Online Mirror Descent
Ofir Schlisselberg, Uri Sherman, Tomer Koren et al.
Convergence of Continual Learning in Homogeneous Deep Networks
Matan Schliserman, Gon Buzaglo, Itay Evron et al.
Private Linear Regression via a Down-Sensitivity to Privacy Reduction
Ittai Rubinstein, Chris Ge, Samuel B. Hopkins
Model Agreement via Anchoring
Eric Eaton, Surbhi Goel, Marcel Hussing et al.
Continuous time policy evaluation is easier with noisy dynamics
Samuel Robertson, Thomas Newton, Csaba Szepesvári
Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining
Yunwei Ren, Yatin Dandi, Florent Krzakala et al.
Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum
Nived Rajaraman, Audrey Huang, Miro Dudik et al.
Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
Hao Qiu, Mengxiao Zhang, Nicolò Cesa-Bianchi
Deep Q-Learning on Hölder Spaces
Qian Qi