Papers
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections
Marco Miani, Hrittik Roy, Søren Hauberg
Behavior-Inspired Neural Networks for Relational Inference
Yulong Yang, Bowen Feng, Keqin Wang et al.
Best-Arm Identification in Unimodal Bandits
Riccardo Poiani, Marc Jourdan, Emilie Kaufmann et al.
Beyond Discretization: Learning the Optimal Solution Path
Qiran Dong, Paul Grigas, Vishal Gupta
Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression
Krzysztof Kacprzyk, Mihaela van der Schaar
Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level Convexity
Yan Yang, Bin Gao, Ya-xiang Yuan
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization
Simon Vary, David Martínez-Rubio, Patrick Rebeschini
Bridging Domains with Approximately Shared Features
Ziliang Samuel Zhong, Xiang Pan, Qi Lei
Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification Problem
Zijun Gao, Shu Ge, Jian Qian
Bridging the Theoretical Gap in Randomized Smoothing
Blaise Delattre, Paul Caillon, Quentin Barthélemy et al.
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff et al.
Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
Bo Chen, Xiaoyu Li, Yingyu Liang et al.
Calibrated Computation-Aware Gaussian Processes
Disha Hegde, Mohamed Adil, Jon Cockayne
Calm Composite Losses: Being Improper Yet Proper Composite
Han Bao, Nontawat Charoenphakdee
Causal Discovery-Driven Change Point Detection in Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu et al.
Causal discovery in mixed additive noise models
Ruicong Yao, Tim Verdonck, Jakob Raymaekers
Causal Discovery on Dependent Binary Data
Alex Chen, Qing Zhou
Causal Representation Learning from General Environments under Nonparametric Mixing
Ignavier Ng, Shaoan Xie, Xinshuai Dong et al.
Causal Temporal Regime Structure Learning
Abdellah Rahmani, Pascal Frossard
Certifiably Quantisation-Robust training and inference of Neural Networks
Hue Dang, Matthew Robert Wicker, Goetz Botterweck et al.
Change Point Detection in Hadamard Spaces by Alternating Minimization
Anica Kostic, Vincent Runge, Charles Truong
Changepoint Estimation in Sparse Dynamic Stochastic Block Models under Near-Optimal Signal Strength
Shirshendu Chatterjee, Soumendu Sundar Mukherjee, TAMOJIT SADHUKHAN
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
Sudeep Salgia, Nikola Pavlovic, Yuejie Chi et al.
Choice is what matters after Attention
Chenhan Fu, Guoming Wang, Juncheng Li et al.
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor et al.