Program

The program follows the following color scheme: talks, breakout sessions, poster sessions, mentoring sessions, program break, sponsor talks, panel discussion. All invited talk titles, and invited speaker/mentor/panelist names are *clickable*. The majority of the program will be streamed and occur synchronously in-person and virtually, except if marked as in-person/virtual only.

You can find the zoom links and livestream on the WiML workshop page of the ICML website.

08:30 Introduction & Opening Remarks, Vinitra Swamy

all-day Virtual Sponsor Booths, [DeepMind, D.E. Shaw Research, Home Depot, Microsoft Research]

all-day In-Person Sponsor Booths, [DeepMind, Google, QuantumBlack]

08:45 Desiderata for Representation Learning: A Causal Perspective, Yixin Wang [Invited Talk]

• Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions capture. However, these desiderata are often intuitively defined and challenging to quantify or enforce.

In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning.

Joint work with Michael Jordan.

09:10 Breakout session

• [in-person only] Machine learning real-time applications in health (Leaders: Dania Humaidan, Cansu Sen)

• [hybrid] Introducing geometry awareness in deep networks (Leader: Ankita Shukla)

• [hybrid] Affective Computing: A Computational Perspective (Leaders: Shreya Ghosh, Garima Sharma)

• [hybrid] Deep Generative Models for Electronic Health Records (Leaders: Ghadeer Ghosheh)

10:10 Poster Session

10:40 Emma Brunskill [Invited Talk]

11:05 Breakout session

• [in-person only] Challenges and opportunities in certified auditing of ML models (Leader: Chhavi Yadav)

• [in-person only] Robustness of Deep Learning Models to Distribution Shift (Leaders: Polina Kirichenko, Shiori Sagawa)

• [hybrid] Knowledge Distillation through the Lens of the Capacity Gap Problem (Leaders: Ibtihel Amara, Samrudhdhi Rangrej, Zahra Vaseqi)

• [hybrid] Improving AI Education (Leaders: Mary Smart, Stefania Druga)

• [hybrid] Statistical Inference & Applications to Machine Learning (Leaders: Lilian Wong, Po-ling Loh)

12:05 Mentoring Roundtables [in-person only] /// Mentoring Panel [virtual only]

• Table 1: Choosing between academia and industry // Mentors: Jigyasa Grover, Ciara Pike-Burke, Nika

Amy Zhang & Lauren Gardiner // Haghtalab, Po-Ling Loh, Hermina Petric Maretic

• Table 2: Finding mentors and taking on mentorship roles // Moderator: Sinead Williamson

throughout your career //

Celestine Mendler-Dünner & Cyril Zhang //

• Table 3: Establishing and maintaining collaborations //

Surbhi Goel & Max Simchowitz //

• Table 4: Work-life Balance //

Ioana Bica & Kishore Kumar //

13:05 Lunch Break, joint with NewInML [in-person only] /// Virtual Sponsor Booths [virtual only]

14:40 Harnessing the power of Hybrid Intelligence, Maria Olivia Lihn [QuantumBlack Sponsor Talk]

14:55 Building embodied agents that can learn from their environments and humans, Kavya Srinet [Meta Platforms Sponsor Talk]

15:10 Machine Learning at Apple, Tatiana Likhomanenko [Apple Sponsor Talk]

15:25 Breakout session

• [in-person only] Robustness of Machine Learning (Leader: Yao Qin)

• [in-person only] Distributionally robust Reinforcement Learning (Leaders: Laixi Shi, Mengdi Xu)

• [hybrid] Machine Learning for Physical Sciences (Leader: Taoli Cheng)

• [hybrid] Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement (Leaders: Haoyu Du, Peiyuan Zhou, Annie Lee, Rainah Khan)

• [hybrid] Detection of Unseen Classes of different Domains using Computer Vision (Leader: Asra Aslam)

16:30 Poster Session, joint with LXAI

17:00 Social dynamics in prediction, Celestine Mendler-Dünner [Invited Talk]

• Abstract: Algorithmic predictions inform consequential decisions, incentivize strategic actions, and motivate precautionary measures. As such, predictions used in societal systems not only describe the world they aim to predict, but they have the power to change it; a prevalent phenomenon often neglected in theories and practices of machine learning. In this talk, I will introduce a risk minimization framework, called performative prediction, that conceptualizes this phenomenon by allowing the predictive model to influence the distribution over future data. This problem formulation elucidates different algorithmic solution concepts, optimization challenges, and offers a new perspective on prediction. In particular, I will discuss how performative prediction allows us to articulate the difference between learning from a population and steering a population through predictions, facilitating an emerging discourse on the topic of power of predictive systems in digital economies.

17:25 Best Practices for Research: Increasing Efficiency and Research Impact, and Navigating Hybrid Collaborations [Panel]

• Panelists: Amy Zhang, Surbhi Goel, Agni Kumar

• Moderator: Ioana Bica

18:25 Closing Remarks, Tatjana Chavdarova

Note: Please navigate the 'Program' menu in the slidebar at the top to find more details about speakers, panelist and mentors.