A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study generalizes non-interaction theorems for relativistic systems.
New method trains deep neural networks for non-interacting kinetic-energy functionals in DFT.
This work refines grid size selection for non-interactive private -means clustering.
Study learning and refutation in non-interactive LDP, showing sample complexity equivalence.
We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model require many rounds of communication between the server and the clients holding the data points. Yet multi-round protocols are prohibitively slow in practice due to network …
Paper addresses ERM in LDP, reducing sample complexity for smooth and convex losses.
New method reduces communication in distributed learning, improving privacy and utility.
In this paper, we study the Empirical Risk Minimization problem in the non-interactive local model of differential privacy. In the case of constant or low dimensionality (), we first show that if the ERM loss function is -smooth, then we can avoid a dependence of the sample complexity, to achieve e…
Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive version of this model the protocol consists of a single round in which a server sends requests to all users then receives their responses. Th…
Interactive privacy mechanisms improve spectral density estimation under local differential privacy.
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is …
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improv…
Optimal threshold resetting reduces search time for multiple diffusive searchers.
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density. The properties of different kernels and methods of cross-validation are explored, and highly accurate energies are achieved. Accurate {\em constrained optimal densitie…
We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by match…
Study local differential privacy methods for estimating power sums of discrete distributions.
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
New K-theory approach classifies anyonic topological phases in 2D semimetals.
In this work the system of agents is applied to establish a model of the nonlinear distributed signal processing. The evolution of the system of the agents - by the prediction time scale diversified trend followers, has been studied for the stochastic time-varying environments represented by the real currency-exchange …
New methods test discrete distributions faster with local privacy constraints.
We develop model free PAC performance guarantees for multiple concurrent MDPs, extending recent works where a single learner interacts with multiple non-interacting agents in a noise free environment. Our framework allows noisy and resource limited communication between agents, and develops novel PAC guarantees in this…
In this paper, we study the problem of estimating smooth Generalized Linear Models (GLMs) in the Non-interactive Local Differential Privacy (NLDP) model. Different from its classical setting, our model allows the server to access some additional public but unlabeled data. In the first part of the paper we focus on GLMs…
We consider a simple model of a closed economic system where the total money is conserved and the number of economic agents is fixed. In analogy to statistical systems in equilibrium, money and the average money per economic agent are equivalent to energy and temperature, respectively. We investigate the effect of the …
We study a credit risk model which captures effects of economic interactions on a firm's default probability. Economic interactions are represented as a functionally defined graph, and the existence of both cooperative, and competitive, business relations is taken into account. We provide an analytic solution of the mo…
Study on information evolution in interactive decision making using multi-armed bandits.
New method uses public data to achieve optimal nonparametric classification with privacy constraints.
ACOWA improves distributed sparse classification with extra communication round.
In this paper we analyze supergeometric locally covariant quantum field theories. We develop suitable categories SLoc of super-Cartan supermanifolds, which generalize Lorentz manifolds in ordinary quantum field theory, and show that, starting from a few representation theoretic and geometric data, one can construct a f…
New model quantifies interactions' role in real-world phenomena.
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
The abstract proposes a neural network theory using quantum field theory.
We characterize the communication complexity of the following distributed estimation problem. Alice and Bob observe infinitely many iid copies of -correlated unit-variance (Gaussian or binary) random variables, with unknown . By interactively exchanging bits, Bob wants to produce an estimate $…
New gradient methods solve multiscale optimization problems efficiently.
In this paper we provide a comprehensive analysis of a structural model for the dynamics of prices of assets traded in a market originally proposed in [1]. The model takes the form of an interacting generalization of the geometric Brownian motion model. It is formally equivalent to a model describing the stochastic dyn…
This work describes simple and efficient algorithms for interactively learning non-binary concepts in the learning from random counter-examples (LRC) model. Here, learning takes place from random counter-examples that the learner receives in response to their proper equivalence queries. In this context, the learning ti…
New framework crafts adversarial examples for neural networks.
Flexible decentralized MARL framework for cooperative multi-agent learning.
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to rely on and upload their private data to big data companies they may not trust. Due to the compliance, legal, or privacy constraints, most u…
Optimal testing for densities under local differential privacy constraints.
An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we stud…
Paper sets fundamental limits for distributed covariance estimation with constrained communication.
Optimal locally private hypothesis selection with interactive rounds.
We break dimension dependence in sparse distribution estimation with communication constraints.
The paper explores privacy-preserving methods for counting unique elements in distributed settings.
The paper addresses hypothesis selection with local differential privacy, requiring more samples than non-private methods.
This study measures price risk aversion using indirect utility functions in a lab experiment.
Interactive IL algorithm queries noisy expert for feedback, reducing sample size.
AI generates theorems and proofs for training theorem provers.