arXiv research

The paper formalizes robot environments using topological concepts.

New index principle shows indistinguishability of certain knot crossings.

The paper examines how to test if two learning algorithms produce similar outcomes.

Characterizes sample complexity for outcome indistinguishability in machine learning.

Graph neural networks fail to distinguish certain 3D atom configurations.

Framework uses human judgment to distinguish algorithmically indistinguishable cases.

Survey on calibration in machine learning, viewing it as indistinguishability.

A new metric evaluates classification algorithms at the point of indistinguishability.

DQN outperforms static policies in a dynamic fee environment for automated market makers.

This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies. We show that a potential-based reward shaping scheme is able to preserve optimality of stochastic policies, and demonstrate that the ability of an a…

Efficiently generates models resistant to falsification.

We investigate the possibility of statistical evaluation of the market completeness for discrete time stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one. The paper shows that market inc…

New approach shows backdoor attacks are indistinguishable from natural data features.

Transformers approximate mean-field dynamics of indistinguishable particles.

The possibility of statistical evaluation of the market completeness and incompleteness is investigated for continuous time diffusion stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one.…

Unifies 18 definitions of surprise, classifies them into four categories.

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, as a function of their number of layers, to distinguish between different random graph models (corresponding to different class-conditional d…

Unsupervised learning models can be indistinguishable without identifiability, leading to unreliable representations.

Graphs indistinguishable by GNNs are fully characterized.

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

We show social events can be accurately predicted, but often undesirably.

Study identifies contagion in aggregated defaults despite environmental changes.

Study tackles ranking fraud in online platforms by learning robust rankings.

Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) easier to detect changes in the data distribution over time, and c) be ab…

Efficient algorithms for deleting data from machine learning models without significantly affecting performance.

Study on replicability in reinforcement learning algorithms.

New budget quantifies drift in closed-loop learning, improving reproducibility.

Two knots in three-space are S-equivalent if they are indistinguishable by Seifert matrices. We show that S-equivalence is generated by the doubled-delta move on knot diagrams. It follows as a corollary that a knot has trivial Alexander polynomial if and only if it can be undone by doubled-delta moves.

Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curator. A major barrier, however, towards a wider adoption of this model is that it offers a poor privacy-utility tradeoff. In this work, we add…

We establish tools to facilitate the computation and application of the Chekanov-Eliashberg differential graded algebra (DGA), a Legendrian-isotopy invariant of Legendrian knots in standard contact three-space. More specifically, we reformulate the DGA in terms of front projection, and introduce the characteristic alge…

Paper distinguishes causal structures under latent confounding and selection bias.

We resolve the open problem of optimal sample complexity for multicalibration and deterministic predictors.

This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to approximate solutions. The approximations are compared with full GP regression and generated paths are demonstrated to be indistinguishable from …

We inject undetectable backdoors into obfuscated neural networks and language models.

PathNNs improve graph neural networks by distinguishing non-isomorphic graphs.

Generative models create indistinguishable adversarial objects for object detection.

A new approach for instance-optimal learning that bypasses impossibility results.

Enhanced attacks quantify machine learning data leakage.

We study, using Mean Curvature Flow methods, 2+1 dimensional cosmologies with a positive cosmological constant and matter satisfying the dominant and the strong energy conditions. If the spatial slices are compact with non-positive Euler characteristic and are initially expanding everywhere, then we prove that the spat…

Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images. The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set. In practice, however, a range of problems frequently p…

AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.

Modified model prevents volatility from approaching zero.

Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many s…

We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this c…

New model of vague knowledge without strict partitions or transitivity.

In this work we define and study the relations between Lorentzian Manifolds given by the diffeomorphisms which map causal future directed vectors onto causal future directed vectors. This class of diffeomorphisms, called proper causal relations, contains as a subset the well-known group of conformal relations and are d…

We explore a simple lattice field model intended to describe statistical properties of high frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tu…

Generative Adversarial Networks create realistic brain MRI images.