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Theory

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Papers

Language Generation in the Limit NIPS 2024
Back to the Continuous Attractor NIPS 2024
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise NIPS 2024
Smoothed Online Classification can be Harder than Batch Classification NIPS 2024
Policy Aggregation NIPS 2024
What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks NIPS 2024
Building a stable classifier with the inflated argmax NIPS 2024
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits NIPS 2024
The Star Geometry of Critic-Based Regularizer Learning NIPS 2024
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization NIPS 2024
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces NIPS 2024
Universal In-Context Approximation By Prompting Fully Recurrent Models NIPS 2024
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees NIPS 2024
Symmetries in Overparametrized Neural Networks: A Mean Field View NIPS 2024
On the Expressive Power of Tree-Structured Probabilistic Circuits NIPS 2024
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents NIPS 2024
Transformers Represent Belief State Geometry in their Residual Stream NIPS 2024
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach NIPS 2024
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability NIPS 2024
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs NIPS 2024
Deep linear networks for regression are implicitly regularized towards flat minima NIPS 2024
Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks NIPS 2024
Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma NIPS 2024
Bridging the Divide: Reconsidering Softmax and Linear Attention NIPS 2024
Compact Proofs of Model Performance via Mechanistic Interpretability NIPS 2024