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Found 13 papers, 0 papers with code
Task Arithmetic Through The Lens Of One-Shot Federated Learning
In this paper, we examine Task Arithmetic for multi-task learning by framing it as a one-shot Federated Learning problem.
Asymptotically Optimal Change Detection for Unnormalized Pre- and Post-Change Distributions
Our approach is based on the estimation of the Cumulative Sum (CUSUM) statistics, which is known to produce optimal performance.
On the Convergence of a Federated Expectation-Maximization Algorithm
We completely characterize the convergence rate of the EM algorithm under all regimes of $m/n$ where $m$ is the number of clients and $n$ is the number of data points per client.
Adaptive Curves for Optimally Efficient Market Making
We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality.
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models
This work studies the challenge of aligning large language models (LLMs) with offline preference data.
Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals.
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling.
A provable initialization and robust clustering method for general mixture models
In this paper, we present initialization and subsequent clustering methods that provably guarantee near-optimal mislabeling for general mixture models when the number of clusters and data dimensions are finite.
ZeroSwap: Data-driven Optimal Market Making in DeFi
This ensures that the market maker balances losses to informed traders with profits from noise traders.
Adversarially robust clustering with optimality guarantees
In the absence of outliers, in fixed dimensions, our theoretical guarantees are similar to that of the Lloyd algorithm.
Real Time Analytics: Algorithms and Systems
Velocity is one of the 4 Vs commonly used to characterize Big Data.
Nonbacktracking Bounds on the Influence in Independent Cascade Models
This paper develops upper and lower bounds on the influence measure in a network, more precisely, the expected number of nodes that a seed set can influence in the independent cascade model.
The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy
The potential risk of privacy leakage prevents users from sharing their honest opinions on social platforms.
