Papers
Test-Time Degradation Adaptation for Open-Set Image Restoration
Yuanbiao Gou, Haiyu Zhao, Boyun Li et al.
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen et al.
Test-Time Regret Minimization in Meta Reinforcement Learning
Mirco Mutti, Aviv Tamar
The Balanced-Pairwise-Affinities Feature Transform
Daniel Shalam, Simon Korman
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
Yatin Dandi, Emanuele Troiani, Luca Arnaboldi et al.
The Computational Complexity of Finding Second-Order Stationary Points
Andreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos et al.
The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
Songhua He, Periklis A. Papakonstantinou
The Emergence of Reproducibility and Consistency in Diffusion Models
Huijie Zhang, Jinfan Zhou, Yifu Lu et al.
The Entropy Enigma: Success and Failure of Entropy Minimization
Ori Press, Ravid Shwartz-Ziv, Yann Lecun et al.
The Expressive Power of Path-Based Graph Neural Networks
Caterina Graziani, Tamara Drucks, Fabian Jogl et al.
The Fundamental Limits of Least-Privilege Learning
Theresa Stadler, Bogdan Kulynych, Michael Gastpar et al.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Cheng Li, Jindong Wang, Yixuan Zhang et al.
The Illusion of State in State-Space Models
William Merrill, Jackson Petty, Ashish Sabharwal
The Linear Representation Hypothesis and the Geometry of Large Language Models
Kiho Park, Yo Joong Choe, Victor Veitch
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, Woohyeon Byeon, Seongmin Kim et al.
The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
The Non-linear $F$-Design and Applications to Interactive Learning
Alekh Agarwal, Jian Qian, Alexander Rakhlin et al.
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
Sepanta Zeighami, Cyrus Shahabi
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Oliviero Durmus et al.
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
Yuchen Wu, Minshuo Chen, Zihao Li et al.
Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling
Zehao Dou, Minshuo Chen, Mengdi Wang et al.
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan et al.
The Pitfalls of Next-Token Prediction
Gregor Bachmann, Vaishnavh Nagarajan
The Privacy Power of Correlated Noise in Decentralized Learning
Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi et al.