arXiv research

FlipOut prunes neural networks by flipping weights' signs, achieving high sparsity.

New neural architectures invariant to sign flips and basis symmetries for graph representation learning.

The paper introduces DP algorithms using random projections and sign random projections for improved privacy in machine learning.

Paper tackles robust estimation of tree-structured Ising models without side information.

This paper resolves BIHT convergence, showing normalization is not necessary in noiseless settings but crucial for robustness.

The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.

DKMD is a fast signed statistic for comparing univariate distributions.

A new protocol evaluates small machine learning improvements conservatively.

Signed-permutation coordinate transport improves model alignment across checkpoints.

Study flip graphs for surfaces of infinite type, finding uncountably many connected components.

The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.

We present a simple hybrid dynamical model as a tool to investigate behavioral strategies based on trend following. The multiplicative symbolic dynamics are generated using a lognormal diffusion model for the at-the-money implied volatility term structure. Thus, are model exploits information from derivative markets to…

New methods train neural networks without changing weights, achieving similar or higher performance.

Improves bit error tolerance in RRAM-based BNNs without overfitting.

Finite subgraphs in flip graphs ensure unique surface embeddings.

EPMF factorizes matrices by adjusting their entries to match a specified power.

Study of flip graphs and their automorphism groups for infinite-type surfaces.

Study shows flipping a small subset of labels can severely damage machine learning models.

In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in $\mathbb{R}^n$ generated by $k$-sparse vectors is bounded below by $k \lg (n/k)$ and above by…

Connected flip graphs for triangulations on hyperbolic surfaces.

Study of skateboard flips as continuous curves in $SO(3)$ group.

Neural networks have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Here, we introduce a novel technique, interpreting a trained neural network by investigating its flip points. A flip point is any point that lies on the boundary between two o…

Paper analyzes BIHT for noisy 1-bit CS, improving results with up to τ-fraction of incorrect measurements.

Flip symmetry on knot diagrams affects Khovanov homology.

ViTaX provides formal guarantees for targeted explanations in safety-critical systems.

The flip graph and arc complex of a surface are shown to have finite rigidity.

Efficiently poisons offline RLHF models by flipping preference labels.

New examples show flip distance and polyhedron triangulation numbers differ, with ratio close to 3/2.

We prove that every injective simplicial map $\mathcal{F}(S) \to \mathcal{F}(S')$ between flip graphs is induced by a subsurface inclusion $S\to S'$, except in finitely many cases. This extends a result of Korkmaz--Papadopoulos which asserts that every automorphism of the flip graph of a surface without boundary is ind…

We prove that for a given flat surface with conical singularities, any pair of geometric triangulations can be connected by a chain of flips.

We introduce a notion of cross-flips: local moves that transform a balanced (i.e., properly $(d+1)$-colored) triangulation of a combinatorial $d$-manifold into another balanced triangulation. These moves form a natural analog of bistellar flips (also known as Pachner moves). Specifically, we establish the following the…

This paper is about the geometry of flip-graphs associated to triangulations of surfaces. More precisely, we consider a topological surface with a privileged boundary curve and study the spaces of its triangulations with n vertices on the boundary curve. The surfaces we consider topologically fill this boundary curve s…

This paper diagnoses factor-model pricing errors using a new method.

Geodesics count exponentially between triangulations of surfaces with enough topology.

Identifies minimal training subset to flip a prediction.

In order to model volatile real-world network behavior, we analyze phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-flipping dynamics, and find the probability that no more than q% of nodes…

Let $Σ$ be a compact surface. We prove that the set of surface cubications modulo flips, up to isotopy, is in one-to-one correspondence with $\Z/2\Z\oplus H_1(Σ,\Z/2\Z)$.

We use flip points to explain and audit deep learning models, revealing decision boundaries and improving model performance.

Study finds flipped classrooms improve student self-concept, enjoyment, but not exam scores.

Study quasisymmetric maps on hyperbolic plane boundaries.

We study flip-graphs of triangulations on topological surfaces where distance is measured by counting the number of necessary flip operations between two triangulations. We focus on surfaces of positive genus $g$ with a single boundary curve and $n$ marked points on this curve; we consider triangulations up to homeomor…

Using existing technology, we prove a Masur-Minsky style distance formula for flip- graph distance between two triangulations, expressed as a sum of the distances of the projections of these triangulations into arc graphs of the suitable subsurfaces of S.

Paper proves a new lower bound on calibration error for binary prediction.

We investigate a type of distance between triangulations on finite type surfaces where one moves between triangulations by performing simultaneous flips. We consider triangulations up to homeomorphism and our main results are upper bounds on distance between triangulations that only depend on the topology of the surfac…

Deep Partition Aggregation defends against poisoning attacks with provable certificates.

ELD compares graphs by their embedded Laplacian eigenvectors, resolving ambiguities.

The paper solves pentagon equations using triangulations and edge transformations.

New method makes machine learning models robust to label flipping attacks.