Papers
The Algebraic Path Problem for Graph Metrics
Enrique Fita Sanmartı́n, Sebastian Damrich, Fred Hamprecht
The CLRS Algorithmic Reasoning Benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden et al.
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu, Thiago Serra, Srikumar Ramalingam et al.
The Complexity of k-Means Clustering when Little is Known
Robert Ganian, Thekla Hamm, Viktoriia Korchemna et al.
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber
The dynamics of representation learning in shallow, non-linear autoencoders
Maria Refinetti, Sebastian Goldt
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Wei-Ning Chen, Christopher A Choquette Choo, Peter Kairouz et al.
The Geometry of Robust Value Functions
Kaixin Wang, Navdeep Kumar, Kuangqi Zhou et al.
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti, Riccardo De Santi, Marcello Restelli
The Infinite Contextual Graph Markov Model
Daniele Castellana, Federico Errica, Davide Bacciu et al.
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
Hadeel Soliman, Lingfei Zhao, Zhipeng Huang et al.
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
Andrew Saxe, Shagun Sodhani, Sam Jay Lewallen
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Chi Jin, Qinghua Liu, Tiancheng Yu
The power of first-order smooth optimization for black-box non-smooth problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii et al.
The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro et al.
The Role of Deconfounding in Meta-learning
Yinjie Jiang, Zhengyu Chen, Kun Kuang et al.
The State of Sparse Training in Deep Reinforcement Learning
Laura Graesser, Utku Evci, Erich Elsen et al.
The Teaching Dimension of Regularized Kernel Learners
Hong Qian, Xu-Hui Liu, Chen-Xi Su et al.
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam et al.
Thompson Sampling for (Combinatorial) Pure Exploration
Siwei Wang, Jun Zhu
Thompson Sampling for Robust Transfer in Multi-Task Bandits
Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri
Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points
Yi Wang, Zhiren Wang
Thresholded Lasso Bandit
Kaito Ariu, Kenshi Abe, Alexandre Proutiere
Tight and Robust Private Mean Estimation with Few Users
Shyam Narayanan, Vahab Mirrokni, Hossein Esfandiari