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Found 12 papers, 2 papers with code
Hallucination Diversity-Aware Active Learning for Text Summarization
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i. e., texts that are factually incorrect or unsupported.
Optimal Sketching Bounds for Sparse Linear Regression
For this problem, under the $\ell_2$ norm, we observe an upper bound of $O(k \log (d)/\varepsilon + k\log(k/\varepsilon)/\varepsilon^2)$ rows, showing that sparse recovery is strictly easier to sketch than sparse regression.
Sample Constrained Treatment Effect Estimation
In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the population to experiment on.
Electra: Conditional Generative Model based Predicate-Aware Query Approximation
The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets.
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
Coresets for Classification -- Simplified and Strengthened
It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.
Coresets for Classification – Simplified and Strengthened
It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.
Asymptotics of Ridge Regression in Convolutional Models
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
Machine Unlearning via Algorithmic Stability
Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure.
Online Discrepancy Minimization via Persistent Self-Balancing Walks
We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Fundamental Tradeoffs in Distributionally Adversarial Training
We study the tradeoff between standard risk and adversarial risk and derive the Pareto-optimal tradeoff, achievable over specific classes of models, in the infinite data limit with features dimension kept fixed.
Graph Neural Networks with Heterophily
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
