Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, and develop a doubly...
Saksham Jain, Alexander R. Luedtke· arXiv.org· 1 citation
Deep learning problems rarely involve objectives that are equal in importance. A primary objective defines the goal, whilst secondary objectives, such as sparsity, compression, or robustness constrain the solution. While existing multi-objective methods have proven effective in practice, they have a clear symmetry prob...
Dara Varam, Mohamed I. Alhajri· arXiv.org· 0 citations
This work revisits the analysis of best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure through a forward-model based algorithm building on KL-based exploration bonuses that adapt to the entropic criterion.
Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes),...
Arnaud Zinflou· 0 citations
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PRISM-FCP is a communication-efficient Byzantine-robust federated conformal prediction framework that uses partial model sharing to mitigate stochastic model-poisoning attacks during training and histogram-based filtering to mitigate adversarial calibration submissions.
Ehsan Lari, Reza Arablouei, Stefan Werner· IEEE Transactions on Signal...· 0 citations
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prio...
Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos· 0 citations
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC), a fully data-driven method that elimin...
Si-Yi Wang, Alexandre Leblanc, P. McNicholas· 0 citations
It is shown how a limited budget for ground-truth data-annotation can be used via doubly-robust OPE with missing rewards, and the optimal annotation probabilities for sequential forward-monotone annotation protocols are characterized, and a feasible batch-adaptive implementation is provided.
Woojin Chae, Ezinne Nwankwo, Hai-Tong Qin et al.· 0 citations
Sparse Gaussian processes achieve $O(N)$ inference by replacing the kernel with an appropriate expansion in a fixed basis $\{\phi_j\}$ on the input space. Given a compute budget $M \ll N$, practitioners conventionally truncate the basis to its first $M$ entries. Nothing in the formalism, however, prevents one from sele...
This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like calibration plots, actual-vs-expected plots, lift charts, Murphy diagrams, as well as classical statistical tools such as Bregman losses, deviance losses, elementary losses...
Principal component analysis (PCA) is a fundamental tool to reduce the dimensionality of the data in many applications. PCA finds a few signal directions that contain most of the variability of the data by computing the eigenvectors of the sample covariance matrix. In this work, we focus on the spiked covariance model,...
Irina-Beatrice Haas, Maike Meier, Yuji Nakatsukasa et al.· 0 citations
We study preference elicitation under the Bradley-Terry-Luce (BTL) model where the true partworth vector is unknown and has to be estimated as a parameter with elicited preference information. The set of selected pairwise queries is non-uniform, deterministic, and arbitrary over a collection of alternatives, provided t...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
