Proposes a new method to minimize non-singleton predictions in conformal prediction.
problem Large prediction sets in conformal prediction are costly and inefficient.
method Introduces a new nonconformity score to minimize non-singleton sets and provides an algorithm to compute it efficiently.
result The proposed Singleton-Optimized Conformal Prediction (SOCOP) method increases singleton frequency by over 20% compared to standard scores, with minimal impact on average set size.
Introduces a new manifold from a graph subgraph.
problem None explicitly stated in the abstract.
method Defined geometrically from a combinatorial subgraph of the Hoffman-Singleton graph.
result Geometric properties of the new manifold presented.
The paper tackles multi-label ranking with uncertain probabilities.
problem Making skeptical inferences for multi-label ranking with sets of probabilities.
method Assumes a convex set of probabilities (credal set) over labels and seeks set-valued predictions.
result Developed methods for making skeptical inferences in multi-label ranking with uncertain probabilities.
New theorem removes uniform finite upper bound for shrinkability of null decompositions.
problem Shrinkability of null decompositions with non-singleton elements.
method Defining squeezable and squashable subsets, proving their equivalence, and applying these definitions to null decompositions.
result Any null decomposition of a compact metric space whose non-singleton elements are recursively squeezable is shrinkable.
New framework tackles bi-level optimization without LLS condition.
problem Bi-level optimization problems without LLS condition.
method Bi-level Descent Aggregation (BDA) framework, proving convergence without LLS condition.
result Proves convergence of BDA without LLS condition.
Rank-one measurements limit feasible sets for low-rank PSD matrices.
problem Feasibility of PSD matrices under rank-one measurements.
method Characterization of feasible sets for PSD matrices given rank-one projections.
result Radius of feasible sets determines singleton solution sets for low-rank matrices.
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then…
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the correlations between data points, which might be crucial for learning latent representa…
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for [0,1]-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicabili…
Learn conditional averages in PAC framework for better predictions.
problem Learning average labels over neighborhoods in unknown concept class.
method Characterization of learnability using combinatorial parameters.
result Complete characterization and sample complexity bounds.
Defines new extremal potentials and measures for Kähler forms.
problem No specific problem stated; dealing with Kähler forms and measures.
method Introduces new extremal potentials and measures for collections of Kähler forms.
result New extremal potentials and measures coincide with classical ones when the collection is a singleton.
New method improves conformal prediction for machine learning models.
problem Improving the efficiency and informativeness of conformal prediction models.
method Introduces Penalized Inverse Probability (PIP) and Regularized PIP (RePIP) nonconformity score functions.
result PIP-based conformal classifiers strike a good balance between informativeness and efficiency.
Let X be a Banach space and ConvH(X) be the space of non-empty closed convex subsets of X, endowed with the Hausdorff metric dH. We prove that each connected component of the space ConvH(X) is homeomorphic to one of the spaces: a singleton, the real line, a closed half-plane, the Hilbert cube multiplied by…
We introduce and study a new family of extensions for the Borsuk-Ulam and topological Radon type theorems. The defining idea for this new family is to replace requirements of the form `a subset that is large in some sense goes to a singleton' with requirements of the milder form `a subset that is large in some sense go…
AR CI framework handles complex confounders and sequential actions.
problem Low-dimensional confounders and singleton actions in causal inference.
method Sequencification to transform data into sequences, enabling CI with complex confounders and sequential actions.
result AR model can estimate multiple causal quantities using a single model, simplifying inference and improving outcome prediction.
In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this paper, we show how this representation helps performing some operations with in…
For finitely supported random walks on finitely generated groups G we prove that the identity map on G extends to a continuous equivariant surjection from the Martin boundary to the Floyd boundary, with preimages of conical points being singletons. This yields new results for relatively hyperbolic groups. Our key e…
We extend the scheme developed in B. Düring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2019, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived by Duffie, Pan and Singleton. The performance of the scheme is asse…
Adapting Hedge algorithm for semi-adversarial data with root-entropy regularization.
problem Minimizing regret in prediction with expert advice under varying distributions.
method Follow-the-Regularized-Leader (FTRL) with root-entropy regularization.
result Adaptive minimax optimal regret across all levels of constraint sets.
Pairwise Choice Markov Chains (PCMC) have been recently introduced to overcome limitations of choice models based on traditional axioms unable to express empirical observations from modern behavior economics like context effects occurring when a choice between two options is altered by adding a third alternative. The i…
This study optimizes offline reinforcement learning methods for various tasks without rewards.
problem Optimizing offline reinforcement learning for multiple tasks without rewards.
method Designing a new model-based approach with singleton absorbing MDPs to achieve optimal convergence rates.
result Achieved optimal convergence rates for offline reinforcement learning in various settings.
The paper defines Benoist-Hulin groups and explores their properties.
problem Defining and characterizing Benoist-Hulin groups.
method Developing theory and proving properties of Benoist-Hulin groups.
result Uniform lattices and parabolic subgroups are Benoist-Hulin groups.
Two results on end spaces of infinite type surfaces, answering questions about their topology and equivalence.
problem Topology and equivalence of end spaces of infinite type surfaces.
method Examples and Tsankov's argument to show equivalence relation.
result Examples of infinite type surfaces with end spaces that are not self-similar but have a unique maximal type.
Geometric framework for signed multivariate tail-dependence compatibility at various thresholds.
problem Modeling and analyzing signed multivariate tail-dependence across different thresholds.
method Developed a geometric witness framework to represent and invert signed tail families, identifying nonnegative weights and normalized masses.
result Characterization and synthesis of signed multivariate tail-dependence at finite thresholds, preserving the complete signed tail family throughout.
We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function e…
On a complete, connected, locally compact, non-compact geodesic space (X,d), we assign each compact set a distance-like function. With the help of these functions, we obtain a pseudo-metric on the space of (non-empty) compact subsets of X which is less than the Hausdorff distance. The quotient metric space is close…
In each manifold M modeled on a finite or infinite dimensional cube [0,1]n we construct a closed nowhere dense subset S⊂M (called a spongy set) which is a universal nowhere dense set in M in the sense that for each nowhere dense subset A⊂M there is a homeomorphism h:M→M such that $h(A)\sub…
How do we assign value to economic transactions? To answer this question, we must consider whether the value of objects is inherent, is a product of social interaction, or involves other mechanisms. Economic theory predicts that there is an optimal price for any market transaction, and can be observed during auctions o…
This paper provides a stratification of semi-algebraic sets in the plane with finitely many geodesic segments.
problem How to stratify semi-algebraic sets in the plane with finitely many geodesic segments.
method Develops a semi-algebraic stratification of a real semi-algebraic set in the plane with open cells having the finiteness property.
result Provides insights for high-dimensional stratifications of semi-algebraic sets in connection with geodesics.
This paper studies moduli spaces of statistical structures on Lie groups.
problem Understanding statistical structures on Lie groups.
method Introduced and studied moduli spaces for left-invariant statistical structures on Lie groups.
result Moduli spaces of left-invariant Riemannian metrics are singletons for certain Lie groups.
Cube category simplifies set modeling.
problem Modeling set operations efficiently.
method Introducing interval-preserving monotone functions between finite Boolean lattices.
result Cube category facilitates model structures equivalent to simplicial sets.
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be inte…
This paper explores how local behavior of meromorphic connections on the projective line determines the global connection.
problem Determining the global meromorphic connection based on specified local behavior at singular points.
method Expository discussion of various problems related to meromorphic connections with specified local behavior, including Deligne-Simpson and rigidity problems.
result The existence and nonemptiness of moduli spaces of meromorphic connections with specified local behavior.
Optimizes assortment decisions with a new OFU scheme for online choice problems.
problem Online assortment optimization under stochastic choice with revenue performance and inference quality considerations.
method Forced-exploration OFU scheme combining regularized estimators for decision making and inference.
result Explicit regret bound and error bounds for approximate optimistic actions, showing Pareto optimality.
VRCQ algorithm reduces variance in Q-learning for MDPs, achieving optimal sample complexity.
problem Estimating the optimal Q-function in MDPs with synchronous sampling.
method VRCQ combines direct variance reduction and Cascade Q-learning.
result VRCQ is minimax optimal and instance optimal for single-action problems.
Deviance-style normalization for sparse, jointly overdispersed count matrices
problem Jointly overdispersed count matrices
method Dirichlet-multinomial deviance residualization
result Preserves exact sparsity, evaluates in constant time, recovers multinomial residual
Study geodesics on Grassmann manifold for functions vanishing on subsets of a set X.
problem Finding minimal geodesics on Grassmann manifold of reproducing kernel Hilbert spaces.
method Analyzing necessary and sufficient conditions for geodesic existence and uniqueness, and studying examples.
result Established conditions for geodesic existence and uniqueness, and found estimates on eigenvalues.
Study confirms mispricing in sportsbooks but finds data issues affect results.
problem Mispricing and inefficiency in online sportsbooks.
method Replication using Wikipedia buzz factor metric and backtesting.
result Data quality issues affect betting results; only one strategy remains profitable.
We consider whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit regularization. We focus on noiseless matrix sensing over rank-r positive semi-definite (PSD) matrices in Rn×n, with a sensing mechanism that satisfies restricted isometry properties (RIP)…
Paper studies zero-sum games with noisy observations and identifies equilibrium conditions.
problem Zero-sum games with noisy observations of the leader's actions.
method Analyzes the equilibrium of games with noisy action observability, identifies necessary conditions for uniqueness, and investigates the cardinality of best responses.
result The noisy observations significantly impact the cardinality of the follower's set of best responses, and under certain conditions, this set becomes a singleton almost surely.
New manifold construction yields Baire-1 functions as cohomotopy groups.
problem Understanding Baire-1 functions on metric spaces.
method Constructing a manifold and analyzing its cohomotopy groups.
result First cohomotopy group of a constructed manifold is the additive group of integer-valued Baire-1 functions.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
We study the closed group of homeomorphisms of the boundary of real hyperbolic space generated by a cocompact Kleinian group G1 and a quasiconformal conjugate h−1G2h of a cocompact group G2. We show that if the conjugacy h is not conformal then this group contains a non-trivial one parameter subgroup. Th…
Expectation-Maximization (EM) is a prominent approach for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the negative log-likelihood of the model at each iteration and updates to the minimizer of this upper-bound. We first provide a "model level" in…
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently…
Proposes DISCO, the first CVI for density-based clustering with noise.
problem Evaluation of noise assignments in density-based clustering.
method Density-connectivity and Silhouette Coefficient adaptation for noise evaluation.
result DISCO is the first CVI to explicitly assess noise assignments.
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
problem Decomposing the squared price-of-risk premium into its components
method Identifying an order-three obstruction to aggregation across portfolios
result The decomposition is estimable and detectable with a permutation-calibrated screen