Pushmeet Kohli

research

∙ 08/21/2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Privacy-preserving machine learning aims to train models on private data...

0 Leonard Berrada, et al. ∙

research

∙ 07/05/2023

Evaluating AI systems under uncertain ground truth: a case study in dermatology

For safety, AI systems in health undergo thorough evaluations before dep...

0 David Stutz, et al. ∙

research

∙ 02/08/2022

Competition-Level Code Generation with AlphaCode

Programming is a powerful and ubiquitous problem-solving tool. Developin...

0 Yujia Li, et al. ∙

research

∙ 09/15/2021

Challenges in Detoxifying Language Models

Large language models (LM) generate remarkably fluent text and can be ef...

7 Johannes Welbl, et al. ∙

research

∙ 06/26/2021

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs

Cryo-electron microscopy (cryo-EM) has revolutionized experimental prote...

0 Dan Rosenbaum, et al. ∙

research

∙ 04/14/2021

Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition

We improve the scalability of Branch and Bound (BaB) algorithms for form...

6 Alessandro De Palma, et al. ∙

research

∙ 12/23/2020

Solving Mixed Integer Programs Using Neural Networks

Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...

9 Vinod Nair, et al. ∙

research

∙ 12/07/2020

Autoencoding Variational Autoencoder

Does a Variational AutoEncoder (VAE) consistently encode typical samples...

15 A. Taylan Cemgil, et al. ∙

research

∙ 11/14/2020

Towards transformation-resilient provenance detection of digital media

Advancements in deep generative models have made it possible to synthesi...

7 Jamie Hayes, et al. ∙

research

∙ 10/28/2020

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

The instability of Generative Adversarial Network (GAN) training has fre...

21 Chongli Qin, et al. ∙

research

∙ 10/22/2020

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Convex relaxations have emerged as a promising approach for verifying de...

6 Sumanth Dathathri, et al. ∙

research

∙ 10/07/2020

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Adversarial training and its variants have become de facto standards for...

0 Sven Gowal, et al. ∙

research

∙ 07/10/2020

Contrastive Training for Improved Out-of-Distribution Detection

Reliable detection of out-of-distribution (OOD) inputs is increasingly u...

3 Jim Winkens, et al. ∙

research

∙ 07/09/2020

Evaluating the Apperception Engine

The Apperception Engine is an unsupervised learning system. Given a sequ...

1 Richard Evans, et al. ∙

research

∙ 07/07/2020

Strong Generalization and Efficiency in Neural Programs

We study the problem of learning efficient algorithms that strongly gene...

11 Yujia Li, et al. ∙

research

∙ 02/24/2020

Lagrangian Decomposition for Neural Network Verification

A fundamental component of neural network verification is the computatio...

30 Rudy Bunel, et al. ∙

research

∙ 12/06/2019

Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations

Recent research has made the surprising finding that state-of-the-art de...

14 Sven Gowal, et al. ∙

research

∙ 11/08/2019

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Recent improvements in large-scale language models have driven progress ...

0 Po-Sen Huang, et al. ∙

research

∙ 10/28/2019

Learning Transferable Graph Exploration

This paper considers the problem of efficient exploration of unseen envi...

12 Hanjun Dai, et al. ∙

research

∙ 10/21/2019

An Alternative Surrogate Loss for PGD-based Adversarial Testing

Adversarial testing methods based on Projected Gradient Descent (PGD) ar...

8 Sven Gowal, et al. ∙

research

∙ 10/05/2019

Making sense of sensory input

This paper attempts to answer a central question in unsupervised learnin...

14 Richard Evans, et al. ∙

research

∙ 10/03/2019

CLEVRER: CoLlision Events for Video REpresentation and Reasoning

The ability to reason about temporal and causal events from videos lies ...

12 Kexin Yi, et al. ∙

research

∙ 09/14/2019

Branch and Bound for Piecewise Linear Neural Network Verification

The success of Deep Learning and its potential use in many safety-critic...

11 Rudy Bunel, et al. ∙

research

∙ 09/03/2019

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Neural networks are part of many contemporary NLP systems, yet their emp...

1 Po-Sen Huang, et al. ∙

research

∙ 07/04/2019

Adversarial Robustness through Local Linearization

Adversarial training is an effective methodology for training deep neura...

3 Chongli Qin, et al. ∙

research

∙ 05/31/2019

Are Labels Required for Improving Adversarial Robustness?

Recent work has uncovered the interesting (and somewhat surprising) find...

0 Jonathan Uesato, et al. ∙

research

∙ 05/30/2019

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Medical imaging only indirectly measures the molecular identity of the t...

5 Simon A. A. Kohl, et al. ∙

research

∙ 05/07/2019

REGAL: Transfer Learning For Fast Optimization of Computation Graphs

We present a deep reinforcement learning approach to optimizing the exec...

14 Aditya Paliwal, et al. ∙

research

∙ 04/29/2019

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

This paper addresses the challenging problem of retrieval and matching o...

8 Yujia Li, et al. ∙

research

∙ 04/26/2019

Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications

Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...

6 Chenglong Wang, et al. ∙

research

∙ 04/26/2019

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that lear...

12 Jiayuan Mao, et al. ∙

research

∙ 04/05/2019

Structured agents for physical construction

Physical construction -- the ability to compose objects, subject to phys...

20 Victor Bapst, et al. ∙

research

∙ 04/02/2019

Analysing Mathematical Reasoning Abilities of Neural Models

Mathematical reasoning---a core ability within human intelligence---pres...

14 David Saxton, et al. ∙

research

∙ 03/28/2019

Meta-Learning surrogate models for sequential decision making

Meta-learning methods leverage past experience to learn data-driven indu...

22 Alexandre Galashov, et al. ∙

research

∙ 02/27/2019

Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in ...

14 Ray Jiang, et al. ∙

research

∙ 02/25/2019

Verification of Non-Linear Specifications for Neural Networks

Prior work on neural network verification has focused on specifications ...

14 Chongli Qin, et al. ∙

research

∙ 12/14/2018

Scaling shared model governance via model splitting

Currently the only techniques for sharing governance of a deep learning ...

6 Miljan Martic, et al. ∙

research

∙ 12/06/2018

Verification of deep probabilistic models

Probabilistic models are a critical part of the modern deep learning too...

0 Krishnamurthy Dvijotham, et al. ∙

research

∙ 12/04/2018

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

This paper addresses the problem of evaluating learning systems in safet...

6 Jonathan Uesato, et al. ∙

research

∙ 12/04/2018

Compositional Imitation Learning: Explaining and executing one task at a time

We introduce a framework for Compositional Imitation Learning and Execut...

4 Thomas Kipf, et al. ∙

research

∙ 11/22/2018

Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles

While deep learning has led to remarkable results on a number of challen...

0 Edward Grefenstette, et al. ∙

research

∙ 10/30/2018

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Recent works have shown that it is possible to train models that are ver...

0 Sven Gowal, et al. ∙

research

∙ 10/04/2018

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

We marry two powerful ideas: deep representation learning for visual rec...

0 Kexin Yi, et al. ∙

research

∙ 06/05/2018

Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn ...

14 Dzmitry Bahdanau, et al. ∙

research

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

0 Peter W. Battaglia, et al. ∙

research

∙ 05/28/2018

Value Propagation Networks

We present Value Propagation (VProp), a parameter-efficient differentiab...

0 Nantas Nardelli, et al. ∙

research

∙ 05/25/2018

Training verified learners with learned verifiers

This paper proposes a new algorithmic framework,predictor-verifier train...

0 Krishnamurthy Dvijotham, et al. ∙

research

∙ 05/23/2018

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) with Gaussian pairwise potentials...

0 Thomas Joy, et al. ∙

research

∙ 05/11/2018

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

Program synthesis is the task of automatically generating a program cons...

0 Rudy Bunel, et al. ∙

research

∙ 04/06/2018

Programmatically Interpretable Reinforcement Learning

We study the problem of generating interpretable and verifiable policies...

0 Abhinav Verma, et al. ∙

Pushmeet Kohli

Featured Co-authors