Papers
316 papers found
Privacy Amplification via Shuffling for Linear Contextual Bandits
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet et al.
Refined Lower Bounds for Nearest Neighbor Condensation
Rajesh Chitnis
Scale-Free Adversarial Multi Armed Bandits
Sudeep Raja Putta, Shipra Agrawal
Social Learning in Non-Stationary Environments
Etienne Boursier, Vianney Perchet, Marco Scarsini
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions
Gellért Weisz, Csaba Szepesvári, András György
The Mirror Langevin Algorithm Converges with Vanishing Bias
Ruilin Li, Molei Tao, Santosh S. Vempala et al.
Understanding Simultaneous Train and Test Robustness
Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan
Universal Online Learning with Unbounded Losses: Memory Is All You Need
Moïse Blanchard, Romain Cosson, Steve Hanneke
A case where a spindly two-layer linear network decisively outperforms any neural network with a fully connected input layer
Manfred K. Warmuth, Wojciech Kotłowski, Ehsan Amid
Adaptive Reward-Free Exploration
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues et al.
A Deep Conditioning Treatment of Neural Networks
Naman Agarwal, Pranjal Awasthi, Satyen Kale
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
Ehsan Emamjomeh-Zadeh, Chen-Yu Wei, Haipeng Luo et al.
An Efficient Algorithm for Cooperative Semi-Bandits
Riccardo Della Vecchia, Tommaso Cesari
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time
Thibaut Cuvelier, Richard Combes, Eric Gourdin
A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions
Louis Faury, Yoan Russac, Marc Abeille et al.
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics
Mark Cesar, Ryan Rogers
Characterizing the implicit bias via a primal-dual analysis
Ziwei Ji, Matus Telgarsky
Contrastive learning, multi-view redundancy, and linear models
Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
Differentially Private Assouad, Fano, and Le Cam
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Efficient Algorithms for Stochastic Repeated Second-price Auctions
Juliette Achddou, Olivier Cappé, Aurélien Garivier
Efficient Learning with Arbitrary Covariate Shift
Adam Tauman Kalai, Varun Kanade
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Marc Jourdan, Mojmír Mutný, Johannes Kirschner et al.