110 citations · 120 across the 4 of their papers we have counts for
4 papers
KARL: Knowledge Agents via Reinforcement Learning
Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23
We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic sea…
Finding Everything within Random Binary Networks
Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn +1
A recent work by Ramanujan et al. (2020) provides significant empirical evidence that sufficiently overparameterized, random neural networks contain untrained subnetworks that achi…
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
Shashank Rajput, Kartik Sreenivasan, Dimitris Papailiopoulos +1
It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized. Recently, Vershynin (2020) settled a long stan…
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput +5
Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the perf…