Papers
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Pre-training for Speech Translation: CTC Meets Optimal Transport
Phuong-Hang Le, Hongyu Gong, Changhan Wang et al.
Pretraining Language Models with Human Preferences
Tomasz Korbak, Kejian Shi, Angelica Chen et al.
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
David Simchi-Levi, Chonghuan Wang
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
Atsushi Nitanda, Kazusato Oko, Denny Wu et al.
Principled Acceleration of Iterative Numerical Methods Using Machine Learning
Sohei Arisaka, Qianxiao Li
Principled Offline RL in the Presence of Rich Exogenous Information
Riashat Islam, Manan Tomar, Alex Lamb et al.
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
Banghua Zhu, Michael Jordan, Jiantao Jiao
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo, Kamalika Chaudhuri, Pierre Stock et al.
Private Federated Learning with Autotuned Compression
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz et al.
Private Statistical Estimation of Many Quantiles
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
Probabilistic Attention-to-Influence Neural Models for Event Sequences
Xiao Shou, Debarun Bhattacharjya, Tian Gao et al.
Probabilistic Categorical Adversarial Attack and Adversarial Training
Han Xu, Pengfei He, Jie Ren et al.
Probabilistic Concept Bottleneck Models
Eunji Kim, Dahuin Jung, Sangha Park et al.
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh
Probabilistic Imputation for Time-series Classification with Missing Data
Seunghyun Kim, Hyunsu Kim, Eunggu Yun et al.
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Alexander Lin, Bahareh Tolooshams, Yves Atchade et al.
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
Progressive Purification for Instance-Dependent Partial Label Learning
Ning Xu, Biao Liu, Jiaqi Lv et al.
Projected Tensor Power Method for Hypergraph Community Recovery
Jinxin Wang, Yuen-Man Pun, Xiaolu Wang et al.
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
Zhuqing Liu, Xin Zhang, Prashant Khanduri et al.
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
Bairu Hou, Joe O’Connor, Jacob Andreas et al.
Prompting Large Language Model for Machine Translation: A Case Study
Biao Zhang, Barry Haddow, Alexandra Birch
Propensity Matters: Measuring and Enhancing Balancing for Recommendation
Haoxuan Li, Yanghao Xiao, Chunyuan Zheng et al.
Proper Losses for Discrete Generative Models
Dhamma Kimpara, Rafael Frongillo, Bo Waggoner