63 citations · 143 across the 15 of their papers we have counts for
17 papers
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards
Tianyu Liu, Davide Belli, Bence Major +2
Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to construct f…
MoNe: Modular Neural Memory for Efficient Long Context Inference
Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6
We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…
LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation
Gabriele Cesa, Thomas Hehn, Aleix Torres-Camps +4
Parallel LLM test-time scaling techniques (e.g., best-of-) require drawing sequences conditioned on the same input prompt. These methods boost accuracy while exploiting th…
Efficient Reasoning on the Edge
Yelysei Bondarenko, Thomas Hehn, Rob Hesselink +15
Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large…
LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents
Amin Rakhsha, Thomas Hehn, Pietro Mazzaglia +3
Large language models can perform well on many isolated tasks, yet they continue to struggle on multi-turn, long-horizon agentic problems that require skills such as planning, stat…
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
Gianluigi Silvestri, Fabio Valerio Massoli, Tribhuvanesh Orekondy +2
A promising way to mitigate the expensive process of obtaining a high-dimensional signal is to acquire a limited number of low-dimensional measurements and solve an under-determine…
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization
Fabio Valerio Massoli, Tim Bakker, Thomas Hehn +2
Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing design parameters to minimize a…
Differentiable and Learnable Wireless Simulation with Geometric Transformers
Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy +2
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based…
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental +7
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wir…
MIMO-GAN: Generative MIMO Channel Modeling
Tribhuvanesh Orekondy, Arash Behboodi, Joseph B. Soriaga
We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distribution…
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries
Shadi Rahimian, Tribhuvanesh Orekondy, Mario Fritz
Machine learning models have been shown to leak information violating the privacy of their training set. We focus on membership inference attacks on machine learning models which a…
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., m…
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
Hui-Po Wang, Tribhuvanesh Orekondy, Mario Fritz
Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails pri…
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing…
Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
Machine Learning (ML) models are increasingly deployed in the wild to perform a wide range of tasks. In this work, we ask to what extent can an adversary steal functionality of suc…
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang +2
Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartp…
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele
Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of…