We consider two-alternative elections where voters' preferences depend on a state variable that is not directly observable. Each voter receives a private signal that is correlated to the state variable. Voters may be "contingent" with different preferences in different states; or predetermined with the same preference in every state. In this setting, even if every voter is a contingent voter, agents voting according to their private information need not result in the adoption of the universally preferred alternative, because the signals can be systematically biased. We present an easy-to-deploy mechanism that elicits and aggregates the private signals from the voters, and outputs the alternative that is favored by the majority. In particular, voters truthfully reporting their signals forms a strong Bayes Nash equilibrium (where no coalition of voters can deviate and receive a better outcome).

Shift invariance is a critical property of CNNs that improves performance on classification. However, we show that invariance to circular shifts can also lead to greater sensitivity to adversarial attacks. We first characterize the margin between classes when a shift-invariant linear classifier is used. We show that the margin can only depend on the DC component of the signals. Then, using results about infinitely wide networks, we show that in some simple cases, fully connected and shift-invariant neural networks produce linear decision boundaries. Using this, we prove that shift invariance in neural networks produces adversarial examples for the simple case of two classes, each consisting of a single image with a black or white dot on a gray background. This is more than a curiosity; we show empirically that with real datasets and realistic architectures, shift invariance reduces adversarial robustness. Finally, we describe initial experiments using synthetic data to probe the source of this connection.

Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for pathfinding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic functions have been handcrafted using domain knowledge, recent studies demonstrate that learning heuristic functions from data is effective in many applications. Motivated by this emerging approach, we study the sample complexity of learning heuristic functions for GBFS and A*. We build on a recent framework called data-driven algorithm design and evaluate the pseudo-dimension of a class of utility functions that measure the performance of parameterized algorithms.

In the Supplementary materials, we include detailed descriptions on convex surrogate losses,convolutional neural networks, non-asymptotic error bounds for commonly used loss functions, and prove Theorems 2.1,2.2, A toy example on the numerical performance of CNN approximation is presented in Appendix D. We next give a brief review of the convex surrogate loss functions and discuss in details on the connection between the excess risk with respect to the ϕ-loss and that of 0-1 loss [28, 4]. Let ϕbe a given convex univariate function ϕ: R [0,). Instead of minimizing the excess risk R over H, we consider minimizing the risk with respect to the loss ϕ(ϕ-risk) R(f):= E{ϕ(Yf(X))} over a certain class of functions F, where ϕ: R [0,) is some generic loss function. For the special case when H = {h: h(x) = sign(f(x)),f F} and ϕ() is a step function, i.e., ϕ(x) = 1 Guohao Shen and Yuling Jiao contributed equally to this work Corresponding authors 36th Conference on Neural Information Processing Systems (NeurIPS 2022). As shown in [28] and [4], for a properly chosen ϕ, ˆfn can indeed help reduce the 0-1 excess risk R (ˆhn) R (h0). More precisely, let R0:= inff measurable R(f), then for a proper ϕ, we have ψ(R (ˆhn) R (h0)) R(ˆfn) R(f0), where ψ: [ 1,1] [0,)is a nonnegative continuous function, invertible on [0,1], and achieves its minimum at 0 with ψ(0) = 0. A wide variety of popular classification methods are based on this tactic.

Bootstrap Bio and Manhattan Genomics, which were pursuing gene editing in human embryos to prevent serious disease, have shut down. Two companies that launched last year with plans to create gene-edited babies have already shut down, citing money issues and internal conflict. One of them, Manhattan Genomics of New York, closed abruptly shortly after announcing a team of scientific advisers in October that included a prominent fertility doctor, a data scientist who worked for de-extinction company Colossal Biosciences, and a scientist who pioneered a "three-parent" IVF technique. The other, California-based Bootstrap Bio, said it ceased operations in late 2025, as first reported by Mother Jones. Manhattan Genomics and Bootstrap Bio had ambitions to edit DNA in human embryos with the goal of preventing serious disease in babies.

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